



'''simple docstring'''





import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


class 	lowerCamelCase__ :


						def __init__( self						:  List[str]      ,       _a						:  int      ,       _a						:  int=3_6_0_0_0      ,       _a						:  Optional[int]=1_3      ,       _a						:  Optional[Any]=7      ,       _a						:  List[Any]=True      ,       _a						:  str=3_2      ,       _a						:  int=4_2      ,       _a						:  Optional[int]=5      ,       _a						:  str=2      ,       _a						:  Dict=4      ,       _a						:  int=2      ,       _a						:  Optional[Any]=3_2      ,       _a						:  Dict=8_0      ,       _a						:  str=3_3      ,       _a						:  Tuple=0.0      ,       _a						:  Dict=1e-6      ,       _a						:  List[str]=1_0_0      ,       _a						:  List[str]=0.0      ,       ):
													a__:       Optional[Any]											=vocab_size
													a__:       Any											=parent
													a__:       Any											=batch_size
													a__:       Union[str, Any]											=num_contexts
													# For common tests
													a__:       int											=self.num_contexts
													a__:       Optional[int]											=is_training
													a__:       str											=hidden_size
													a__:       str											=num_ext_layers
													a__:       str											=ext_size
													a__:       str											=num_hidden_layers
													a__:       Optional[Any]											=num_attention_heads
													a__:       Optional[int]											=num_experts
													a__:       Dict											=d_ff
													a__:       Optional[Any]											=d_ext
													a__:       Optional[Any]											=d_spout
													a__:       Dict											=dropout_rate
													a__:       Union[str, Any]											=layer_norm_epsilon
													a__:       str											=expert_capacity
													a__:       Optional[int]											=router_jitter_noise


						def   _lowerCamelCase			( self						:  int			):
													return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"			)


						def   _lowerCamelCase			( self						:  Any			):
													a__:       int											=ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

													a__:       Optional[int]											=self.get_config()

													return (config, input_ids)


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Tuple											=ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)

													a__:       Tuple											=self.get_config()

													return (config, {"input_ids": input_ids})


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													return GPTSanJapaneseConfig(
													    vocab_size=self.vocab_size      ,       num_contexts=self.seq_length      ,       d_model=self.hidden_size      ,       d_ff=self.d_ff      ,       d_ext=self.d_ext      ,       d_spout=self.d_spout      ,       num_switch_layers=self.num_hidden_layers - self.num_ext_layers      ,       num_ext_layers=self.num_ext_layers      ,       num_heads=self.num_attention_heads      ,       num_experts=self.num_experts      ,       expert_capacity=self.expert_capacity      ,       dropout_rate=self.dropout_rate      ,       layer_norm_epsilon=self.layer_norm_epsilon      ,       router_jitter_noise=self.router_jitter_noise      ,       )







						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  Tuple      ,       _a						:  int      ,       ):
													a__:       Any											=GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase__			)
													model.to(lowerCamelCase__			)
													model.eval()
													a__:       Optional[int]											=model(
													    input_ids=lowerCamelCase__      ,       )
													self.parent.assertIsNotNone(lowerCamelCase__			)


@require_torch
class 	lowerCamelCase__ (		_a				,					_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= (GPTSanJapaneseModel,) if is_torch_available() else ()
						_lowerCAmelCase								= (
						    {
						        '''conversational''': GPTSanJapaneseForConditionalGeneration,
						        '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
						        '''summarization''': GPTSanJapaneseForConditionalGeneration,
						        '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
						        '''translation''': GPTSanJapaneseForConditionalGeneration,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						_lowerCAmelCase								= [0.8, 0.9]


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  List[str]      ,       _a						:  str      ,       _a						:  str      ,       _a						:  Union[str, Any]      ,       _a						:  List[str]			):
													if pipeline_test_casse_name == "SummarizationPipelineTests":
																				# TODO: fix `_reorder_cache` is not implemented for this model
																				return True
													elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
																				# TODO: check this.
																				return True

													return False


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													a__:       List[Any]											=GPTSanJapaneseTester(self			)
													a__:       int											=ConfigTester(self      ,       config_class=lowerCamelCase__      ,       d_model=3_7			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													GPTSanJapaneseConfig()


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       int											=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCamelCase__			)


						@unittest.skip(
						    reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"			)
						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													super().test_model_parallelism()







						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
						def   _lowerCamelCase			( self						:  List[Any]			):
													pass


@require_torch
class 	lowerCamelCase__ (		_a				,					_a				,					unittest.TestCase   ):
						_lowerCAmelCase								= (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						_lowerCAmelCase								= False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						_lowerCAmelCase								= [0.8, 0.9]


						def   _lowerCamelCase			( self						:  List[Any]			):
													a__:       str											=GPTSanJapaneseTester(self			)
													a__:       List[str]											=ConfigTester(self      ,       config_class=lowerCamelCase__      ,       d_model=3_7			)


						def   _lowerCamelCase			( self						:  str			):
													GPTSanJapaneseConfig()


						def   _lowerCamelCase			( self						:  int			):
													a__:       Any											=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowerCamelCase__			)


						@unittest.skip(
						    reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"			)
						def   _lowerCamelCase			( self						:  List[Any]			):
													super().test_model_parallelism()


						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
						def   _lowerCamelCase			( self						:  int			):
													pass


						@slow
						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       str											=GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       Union[str, Any]											=GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       List[str]											=tokenizer.encode("武田信玄は"      ,       return_tensors="pt"			)
													a__:       List[str]											=model(lowerCamelCase__			)
													a__:       Any											=outputs.logits.detach().cpu().numpy()
													# Output of original model created with mesh-tensoflow
													a__:       int											=[
													    # fmt: off
													    [-1_2.0_3_7_8_3_9_8_8_9_5_2_6_3_6_7, -1_2.4_3_3_0_6_1_5_9_9_7_3_1_4_4_5, -1_4.3_3_3_8_4_0_3_7_0_1_7_8_2_2_3, -1_2.4_5_0_3_4_5_9_9_3_0_4_1_9_9_2, -1_1.1_6_6_1_3_7_6_9_5_3_1_2_5,
													    -1_1.9_3_0_1_3_7_6_3_4_2_7_7_3_4_4, -1_0.6_5_9_7_4_0_4_4_7_9_9_8_0_4_7, -1_2.9_0_9_5_7_4_5_0_8_6_6_6_9_9_2, -1_3.2_4_1_0_4_3_0_9_0_8_2_0_3_1_2, -1_3.3_9_8_5_7_9_5_9_7_4_7_3_1_4_5,
													    -1_1.1_0_7_5_2_4_8_7_1_8_2_6_1_7_2, -1_2.3_6_8_5_9_4_1_6_9_6_1_6_7, -2_2.9_7_9_4_3_1_1_5_2_3_4_3_7_5, -1_0.4_8_1_0_6_7_6_5_7_4_7_0_7_0_3, -1_2.4_8_4_0_3_0_7_2_3_5_7_1_7_7_7,
													    -1_2.8_0_7_3_6_0_6_4_9_1_0_8_8_8_7, -1_4.7_6_9_7_0_0_0_5_0_3_5_4_0_0_4, -1_2.2_3_3_5_7_9_6_3_5_6_2_0_1_1_7, -1_3.4_2_8_1_4_5_4_0_8_6_3_0_3_7_1, -2_2.6_2_4_1_7_7_9_3_2_7_3_9_2_5_8],
													    [-7.5_1_1_1_4_9_8_8_3_2_7_0_2_6_4, -8.2_8_1_8_5_1_7_6_8_4_9_3_6_5_2, -7.9_4_3_1_2_7_1_5_5_3_0_3_9_5_5, -7.5_5_0_2_1_3_3_3_6_9_4_4_5_8, -6.4_9_8_6_9_9_6_6_5_0_6_9_5_8,
													    -7.5_8_6_7_9_6_2_8_3_7_2_1_9_2_4, -6.9_7_8_0_8_5_9_9_4_7_2_0_4_5_9, -7.8_3_9_1_4_5_1_8_3_5_6_3_2_3_2, -8.2_1_9_6_4_1_6_8_5_4_8_5_8_4, -8.6_9_5_0_9_1_2_4_7_5_5_8_5_9_4,
													    -6.7_0_6_9_1_0_6_1_0_1_9_8_9_7_5, -6.6_5_8_5_7_9_8_2_6_3_5_4_9_8_0_5, -1_9.5_6_5_6_9_8_6_2_3_6_5_7_2_2_7, -5.3_5_3_8_4_2_7_3_5_2_9_0_5_2_7, -8.3_5_0_6_8_6_0_7_3_3_0_3_2_2_3,
													    -8.0_3_9_3_8_8_6_5_6_6_1_6_2_1_1, -1_0.8_5_6_5_6_9_2_9_0_1_6_1_1_3_3, -7.7_5_1_5_4_4_4_7_5_5_5_5_4_2, -8.8_1_9_0_2_2_1_7_8_6_4_9_9_0_2, -1_9.5_1_5_3_2_7_4_5_3_6_1_3_2_8],
													    [-9.7_3_0_6_6_2_3_4_5_8_8_6_2_3, -1_0.2_2_3_9_2_2_7_2_9_4_9_2_1_8_8, -9.9_3_2_9_8_1_4_9_1_0_8_8_8_6_7, -1_1.8_5_7_8_3_6_7_2_3_3_2_7_6_3_7, -7.6_6_2_6_2_6_2_6_6_4_7_9_4_9_2,
													    -1_1.1_3_5_2_9_1_0_9_9_5_4_8_3_4, -7.7_6_5_0_9_7_6_1_8_1_0_3_0_2_7, -1_1.4_7_2_9_2_3_2_7_8_8_0_8_5_9_4, -9.5_4_3_1_4_9_9_4_8_1_2_0_1_1_7, -1_1.9_0_5_6_3_3_9_2_6_3_9_1_6_0_2,
													    -9.3_6_6_1_6_4_2_0_7_4_5_8_4_9_6, -1_1.5_7_3_4_2_8_1_5_3_9_9_1_7, -2_3.6_9_9_0_0_3_2_1_9_6_0_4_4_9_2, -9.4_2_9_5_9_0_2_2_5_2_1_9_7_2_7, -1_0.4_2_8_3_9_2_4_1_0_2_7_8_3_2,
													    -1_0.5_8_5_2_4_0_3_6_4_0_7_4_7_0_7, -1_0.9_4_7_7_1_9_5_7_3_9_7_4_6_1, -1_1.0_9_5_4_1_6_0_6_9_0_3_0_7_6_2, -1_0.3_9_0_2_4_0_6_6_9_2_5_0_4_8_8, -2_3.7_6_9_3_7_2_9_4_0_0_6_3_4_7_7],
													    [-9.7_2_8_2_6_5_7_6_2_3_2_9_1_0_2, -9.8_5_9_7_1_2_6_0_0_7_0_8_0_0_8, -1_0.0_9_7_2_9_2_9_0_0_0_8_5_4_5, -9.6_7_8_5_2_2_1_0_9_9_8_5_3_5_2, -6.8_7_9_5_1_9_9_3_9_4_2_2_6_0_7,
													    -9.6_8_4_8_7_5_4_8_8_2_8_1_2_5, -4.2_8_0_3_4_2_5_7_8_8_8_7_9_3_9_5, -1_0.0_1_8_9_1_4_2_2_2_7_1_7_2_8_5, -9.3_0_8_4_4_5_9_3_0_4_8_0_9_5_7, -1_0.6_3_3_9_4_5_4_6_5_0_8_7_8_9,
													    -8.0_8_3_6_4_6_7_7_4_2_9_1_9_9_2, -9.0_6_3_0_1_4_9_8_4_1_3_0_8_6, -2_1.9_0_4_2_6_6_3_5_7_4_2_1_8_7_5, -8.9_0_1_6_0_8_4_6_7_1_0_2_0_5, -8.8_4_1_8_7_6_0_2_9_9_6_8_2_6_2,
													    -1_1.8_5_6_7_1_9_9_7_0_7_0_3_1_2_5, -1_2.0_7_9_3_9_8_1_5_5_2_1_2_4_0_2, -1_1.2_3_3_7_5_3_2_0_4_3_4_5_7_0_3, -1_0.1_7_7_3_3_8_6_0_0_1_5_8_6_9_1, -2_1.8_7_2_5_6_6_2_2_3_1_4_4_5_3],
													    [-9.6_6_9_7_6_4_5_1_8_7_3_7_7_9_3, -9.6_1_4_1_9_8_6_8_4_6_9_2_3_8_3, -9.8_1_4_5_1_0_3_4_5_4_5_8_9_8_4, -9.9_9_6_5_0_1_9_2_2_6_0_7_4_2_2, -1_1.3_7_5_6_9_0_4_6_0_2_0_5_0_7_8,
													    -1_0.1_1_3_4_0_5_2_2_7_6_6_1_1_3_3, -1_0.5_4_6_8_6_7_3_7_0_6_0_5_4_6_9, -1_0.0_4_3_6_9_0_6_8_1_4_5_7_5_2, -1_0.9_0_7_8_0_9_2_5_7_5_0_7_3_2_4, -1_0.5_0_4_2_1_6_1_9_4_1_5_2_8_3_2,
													    -1_1.1_2_9_1_9_9_0_2_8_0_1_5_1_3_7, -1_0.1_5_1_1_2_4_0_0_0_5_4_9_3_1_6, -2_1.9_6_5_8_6_7_9_9_6_2_1_5_8_2, -9.0_8_6_3_4_9_4_8_7_3_0_4_6_8_8, -1_1.7_3_0_3_3_9_0_5_0_2_9_2_9_6_9,
													    -1_0.4_6_0_6_6_7_6_1_0_1_6_8_4_5_7, -1_0.2_9_8_0_4_9_9_2_6_7_5_7_8_1_2, -1_0.7_8_4_1_4_8_2_1_6_2_4_7_5_5_9, -1_0.8_4_0_6_9_3_4_7_3_8_1_5_9_1_8, -2_2.0_3_1_5_2_8_4_7_2_9_0_0_3_9],
													    # fmt: on
													]
													a__:       Union[str, Any]											=np.array(lowerCamelCase__			).flatten()
													a__:       int											=output_logits[0, :, :2_0].flatten()

													def check(_a						:  Optional[int]      ,       _a						:  Optional[Any]      ,       _a						:  Any=5e-4			):
																				return abs(a - b			) < epsilon * max(abs(lowerCamelCase__			)      ,       abs(lowerCamelCase__			)			)

													self.assertTrue(np.all([check(target[i]      ,       predict[i]			) for i in range(len(lowerCamelCase__			)			)]			)			)


						@slow
						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       Optional[int]											=GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       Tuple											=GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
													model.to(lowerCamelCase__			)

													# set deterministically
													a__:       Optional[int]											=GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       Dict											=1

													# use different length sentences to test batching
													a__:       Optional[Any]											=[
													    "甲斐なら武田と言うほど",
													    "織田信長は、",
													]

													a__:       Tuple											="left"
													a__:       Optional[Any]											=tokenizer(lowerCamelCase__      ,       return_tensors="pt"      ,       padding=lowerCamelCase__			)
													a__:       List[str]											=inputs["input_ids"].to(lowerCamelCase__			)

													self.assertNotEqual(inputs["attention_mask"][0].numpy().tolist()      ,       inputs["attention_mask"][1].numpy().tolist()			)

													a__:       Any											=model.generate(
													    input_ids=lowerCamelCase__      ,       attention_mask=inputs["attention_mask"].to(lowerCamelCase__			)      ,       max_new_tokens=3      ,       generation_config=lowerCamelCase__      ,       )

													a__:       Dict											=tokenizer(sentences[0]      ,       return_tensors="pt"			).input_ids.to(lowerCamelCase__			)
													a__:       Optional[Any]											=model.generate(
													    input_ids=lowerCamelCase__      ,       max_new_tokens=3      ,       generation_config=lowerCamelCase__			)

													a__:       List[str]											=tokenizer(sentences[1]      ,       return_tensors="pt"			).input_ids.to(lowerCamelCase__			)
													a__:       int											=model.generate(input_ids=lowerCamelCase__      ,       max_new_tokens=3      ,       generation_config=lowerCamelCase__			)

													self.assertNotEqual(inputs_non_padded.shape      ,       inputs_padded.shape			)

													a__:       Tuple											=tokenizer.batch_decode(lowerCamelCase__      ,       skip_special_tokens=lowerCamelCase__			)
													a__:       List[Any]											=tokenizer.decode(output_non_padded[0]      ,       skip_special_tokens=lowerCamelCase__			)
													a__:       Union[str, Any]											=tokenizer.decode(output_padded[0]      ,       skip_special_tokens=lowerCamelCase__			)

													a__:       Any											=[
													    "甲斐なら武田と言うほど甲斐の武田",
													    "織田信長は、このような",
													]
													self.assertListEqual(lowerCamelCase__      ,       lowerCamelCase__			)
													self.assertListEqual(lowerCamelCase__      ,       [non_padded_sentence, padded_sentence]			)


						@tooslow
						def   _lowerCamelCase			( self						:  Tuple			):
													a__:       Dict											=GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       str											=GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
													# Output of original model created with mesh-tensoflow
													a__:       List[Any]											=[
													    ("武田信玄は", 3_5_6_7_5),
													    ("武田信玄は、", 4_5),
													    ("武田信玄は、この", 2_9),
													    ("武田信玄は、このよう", 3_0_6_4_2),
													    ("武田信玄は、このような", 3_5_6_8_0),
													    ("武田信玄は、このような「", 8_6_4_0),
													    ("武田信玄は、このような「武田", 3_1_6_1_7),
													    ("武田信玄は、このような「武田家", 3_0_6_4_6),
													    ("武田信玄は、このような「武田家の", 3_1_6_1_7),
													    ("武田信玄は、このような「武田家の家", 3_1_3_8_1),
													]
													for input, output in target:
																				a__:       int											=tokenizer.encode(lowerCamelCase__      ,       return_tensors="pt"			)
																				a__:       List[Any]											=model(lowerCamelCase__			)
																				a__:       Optional[Any]											=outputs.logits.detach().cpu().numpy()[0]
																				a__:       Tuple											=np.argmax(output_logits[-1]			)
																				self.assertEqual(lowerCamelCase__      ,       lowerCamelCase__			)


						@slow
						def   _lowerCamelCase			( self						:  Optional[int]			):
													a__:       str											=GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       Union[str, Any]											=GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
													model.to(lowerCamelCase__			)

													# set deterministically
													a__:       Tuple											=GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       Any											=1

													a__:       Optional[int]											="武田信玄は、"
													a__:       Optional[Any]											=tokenizer(lowerCamelCase__      ,       return_tensors="pt"			).input_ids.to(lowerCamelCase__			)
													a__:       Dict											=tokenizer([input_text, input_text]      ,       return_tensors="pt"			).input_ids.to(lowerCamelCase__			)

													# spout from uniform and one-hot
													a__:       List[str]											=[
													    # fmt: off
													    [0.8_7_8_8_2_2_0_8, 0.3_8_4_2_6_3_9_6, 0.3_3_2_2_0_2_4_8, 0.4_3_8_9_0_4_0_6, 0.1_6_5_6_2_2_5_2,
													    0.0_4_8_0_3_9_8_5, 0.2_1_1_5_7_2  , 0.2_3_1_8_8_4_7_3, 0.3_7_1_5_3_0_6_8, 0.7_8_3_6_3_7_7 ,
													    0.0_2_1_6_0_1_7_2, 0.3_8_7_6_1_7_1_9, 0.7_5_2_9_0_7_7_2, 0.9_0_1_9_8_8_5_7, 0.3_4_3_6_5_7_7_7,
													    0.6_4_1_6_8_1_6_9, 0.4_4_3_1_8_4_7_1, 0.1_4_5_7_5_7_4_6, 0.9_2_5_6_2_8_8_1, 0.4_0_8_1_2_1_4_8,
													    0.2_9_0_1_9_1_2_2, 0.8_8_8_6_1_5_9_9, 0.6_5_5_2_4_8_4_6, 0.4_3_5_6_3_4_5_6, 0.3_8_1_7_7_1_8_7,
													    0.7_0_8_3_2_9_6_5, 0.8_1_5_2_7_8_9_2, 0.6_8_8_3_2_8_1_2, 0.3_8_8_3_3_1_9_2, 0.4_5_6_1_5_2_2 ,
													    0.1_4_8_2_8_8_1_7, 0.4_7_2_4_8_2_1_3, 0.5_4_3_5_7_3_3_5, 0.8_2_0_0_9_5_6_6, 0.1_3_3_8_8_8_4 ,
													    0.0_2_7_5_5_4_1_7, 0.1_9_7_6_4_6_7_7, 0.2_4_2_2_0_8_4 , 0.0_4_7_5_7_6_7_4, 0.6_5_4_0_9_6_0_6,
													    0.0_8_2_4_5_8_9 , 0.0_3_3_0_4_3_8_3, 0.9_4_3_8_7_6_8_9, 0.9_8_7_6_4_5_0_9, 0.8_2_4_3_3_9_0_1,
													    0.2_7_6_4_6_7_4_1, 0.6_4_9_0_7_4_9_3, 0.7_6_0_0_9_4_0_6, 0.3_0_0_8_7_9_1_5, 0.1_7_9_0_4_6_8_9,
													    0.4_1_6_0_1_7_1_4, 0.6_7_0_4_6_3_9_8, 0.1_0_4_2_2_8_2_2, 0.0_8_4_4_7_3_7_4, 0.0_7_3_5_4_3_4_4,
													    0.6_1_4_2_3_5_6_5, 0.7_0_2_8_4_8_6_6, 0.7_5_3_2_3_3_3 , 0.1_9_7_2_0_3_8 , 0.2_9_5_7_5_6_5_9,
													    0.9_0_5_8_3_8_8_6, 0.2_9_2_6_5_3_0_7, 0.5_0_0_0_0_1_7_5, 0.7_0_4_0_7_6_5_5, 0.8_8_9_3_6_3  ,
													    0.8_1_9_0_4_4_1_8, 0.6_6_8_2_9_1_2_8, 0.6_4_4_6_8_8_1_5, 0.5_6_5_6_3_7_2_3, 0.8_5_6_0_1_8_7_5,
													    0.9_4_9_2_4_6_7_2, 0.0_0_1_6_6_7_6_2, 0.2_5_2_2_0_6_4_3, 0.7_4_5_4_0_2_1_9, 0.6_7_9_9_3_2_4_7,
													    0.1_5_4_9_6_7_5 , 0.3_9_3_8_5_3_5_2, 0.9_2_1_5_3_6_0_7, 0.6_3_7_4_5_9_3_1, 0.2_7_7_5_9_0_4_3,
													    0.8_4_7_0_2_2_9_5, 0.6_5_9_0_4_2_7_1, 0.5_8_6_7_6_6_1_4, 0.8_6_6_6_9_3_6 , 0.3_9_6_0_7_4_3_8,
													    0.7_9_9_5_4_9_8_3, 0.4_2_2_2_0_6_9_7, 0.3_9_6_5_0_3_8_1, 0.7_8_4_9_8_6_4 , 0.5_6_1_5_0_2_0_1,
													    0.1_5_6_7_8_9_2_5, 0.1_4_7_4_6_0_3_2, 0.3_4_5_4_2_1_1_4, 0.4_7_0_2_6_7_8_3, 0.1_1_9_5_6_4_8_9,
													    0.2_5_4_2_1_4_3_5, 0.3_3_7_8_8_9_0_1, 0.6_8_9_3_4_8_4_2, 0.3_6_4_2_4_6_8_5, 0.7_1_7_3_7_8_9_8,
													    0.3_8_9_8_3_4_4_9, 0.9_4_3_9_3_7_7_9, 0.3_9_5_7_5_5_8_8, 0.3_6_6_1_6_5_5_3, 0.8_7_1_0_4_6_6_5,
													    0.6_4_6_3_0_2_0_3, 0.2_2_5_1_6_9_0_5, 0.8_8_2_7_0_8_0_4, 0.1_5_0_3_1_3_3_8, 0.7_5_1_4_4_3_4_5,
													    0.4_6_4_5_9_0_2_5, 0.8_5_3_9_6_4_5_4, 0.8_6_3_5_5_6_4_3, 0.6_5_1_3_9_8_5_1, 0.7_0_2_6_6_0_6_1,
													    0.3_0_2_4_1_3_8_9, 0.8_1_0_5_6_4_9_7, 0.8_8_8_6_5_9_6_9, 0.3_8_7_7_3_8_0_7, 0.7_0_6_3_5_8_4_9,
													    0.9_0_7_1_8_4_5_9, 0.4_3_2_4_5_7_8_9, 0.2_8_0_0_0_6_5_4, 0.4_5_9_3_5_5_6_2, 0.0_8_7_7_3_5_1_9,
													    0.9_5_5_2_1_5_1 , 0.9_3_9_0_1_5_1_1, 0.2_2_4_8_9_2_8_8], # uniform
													    [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
													     0., 0., 0., 0., 0., 0., 0., 0.],
													    # fmt: on
													]

													a__:       str											=model.generate(
													    input_ids=lowerCamelCase__      ,       spout=spouts[0]      ,       max_new_tokens=2_0      ,       generation_config=lowerCamelCase__      ,       )

													a__:       int											=model.generate(
													    input_ids=lowerCamelCase__      ,       spout=spouts[1]      ,       max_new_tokens=2_0      ,       generation_config=lowerCamelCase__      ,       )

													a__:       List[str]											=model.generate(
													    input_ids=lowerCamelCase__      ,       spout=lowerCamelCase__      ,       max_new_tokens=2_0      ,       generation_config=lowerCamelCase__      ,       )

													a__:       Union[str, Any]											=tokenizer.decode(outputa[0]			)
													a__:       Optional[Any]											=tokenizer.decode(outputa[0]			)
													a__:       Optional[Any]											=tokenizer.batch_decode(lowerCamelCase__			)

													a__:       Optional[Any]											=[
													    "武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である",
													    "武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討",
													]
													self.assertListEqual(lowerCamelCase__      ,       lowerCamelCase__			)
													self.assertListEqual(lowerCamelCase__      ,       [outa_sentence, outa_sentence]			)







						@slow
						def   _lowerCamelCase			( self						:  Any			):
													a__:       Optional[Any]											=GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       List[Any]											=GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
													model.to(lowerCamelCase__			)

													# set deterministically
													a__:       Any											=GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"			)
													a__:       Tuple											=1

													a__:       Optional[Any]											="武田信玄"
													a__:       int											="織田信長"
													a__:       Any											="は、"
													a__:       int											="が、"
													a__:       str											=tokenizer(lowerCamelCase__      ,       prefix_text=lowerCamelCase__      ,       return_tensors="pt"			)
													a__:       List[str]											=tokenizer(lowerCamelCase__      ,       prefix_text=lowerCamelCase__      ,       return_tensors="pt"			)
													a__:       Dict											=tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]      ,       return_tensors="pt"			)

													a__:       Dict											=model.generate(
													    input_ids=input_tok_a.input_ids.to(lowerCamelCase__			)      ,       token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__			)      ,       max_new_tokens=2_0      ,       generation_config=lowerCamelCase__      ,       )

													a__:       List[str]											=model.generate(
													    input_ids=input_tok_a.input_ids.to(lowerCamelCase__			)      ,       token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__			)      ,       max_new_tokens=2_0      ,       generation_config=lowerCamelCase__      ,       )

													a__:       Optional[int]											=model.generate(
													    input_ids=input_tok_a.input_ids.to(lowerCamelCase__			)      ,       token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__			)      ,       attention_mask=input_tok_a.attention_mask.to(lowerCamelCase__			)      ,       max_new_tokens=2_0      ,       generation_config=lowerCamelCase__      ,       )

													a__:       List[Any]											=tokenizer.decode(outputa[0]			)
													a__:       Any											=tokenizer.decode(outputa[0]			)
													a__:       Union[str, Any]											=tokenizer.batch_decode(lowerCamelCase__			)

													a__:       int											=[
													    "武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して",
													    "織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残",
													]
													self.assertListEqual(lowerCamelCase__      ,       lowerCamelCase__			)
													self.assertListEqual(lowerCamelCase__      ,       [outa_sentence, outa_sentence]			)










import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  Optional[Any]=1						):
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("."						)[n_shave_prefix_segments:]						)
							else:
														return ".".join(path.split("."						)[:n_shave_prefix_segments]						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]     , __magic_name__	:  Tuple=0						):
							a__:       Dict											=[]
							for old_item in old_list:
														a__:       Tuple											=old_item
														a__:       Any											=new_item.replace("block."     , "resnets."						)
														a__:       Optional[int]											=new_item.replace("conv_shorcut"     , "conv1"						)
														a__:       Dict											=new_item.replace("in_shortcut"     , "conv_shortcut"						)
														a__:       Optional[int]											=new_item.replace("temb_proj"     , "time_emb_proj"						)

														a__:       Optional[int]											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)

														mapping.append({"old": old_item, "new": new_item}						)

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]     , __magic_name__	:  Optional[int]=0     , __magic_name__	:  Dict=False						):
							a__:       Optional[int]											=[]
							for old_item in old_list:
														a__:       Dict											=old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					a__:       Optional[int]											=new_item.replace("attn"     , "attentions"						)
														a__:       Dict											=new_item.replace(".k."     , ".key."						)
														a__:       Dict											=new_item.replace(".v."     , ".value."						)
														a__:       str											=new_item.replace(".q."     , ".query."						)

														a__:       Union[str, Any]											=new_item.replace("proj_out"     , "proj_attn"						)
														a__:       Tuple											=new_item.replace("norm"     , "group_norm"						)

														a__:       Any											=shave_segments(__magic_name__     , n_shave_prefix_segments=__magic_name__						)
														mapping.append({"old": old_item, "new": new_item}						)

							return mapping





def 						__lowerCamelCase       (      __magic_name__	:  Tuple     , __magic_name__	:  Tuple     , __magic_name__	:  int     , __magic_name__	:  Union[str, Any]=None     , __magic_name__	:  Tuple=None     , __magic_name__	:  Union[str, Any]=None						):
							assert isinstance(__magic_name__     , __magic_name__						), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."						)

														for path, path_map in attention_paths_to_split.items():
																					a__:       Any											=old_checkpoint[path]
																					a__:       List[Any]											=old_tensor.shape[0] // 3

																					a__:       List[Any]											=(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

																					a__:       Optional[int]											=old_tensor.shape[0] // config.get("num_head_channels"     , 1						) // 3

																					a__:       List[str]											=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
																					a__							,			a__							,			a__:       Any											=old_tensor.split(channels // num_heads     , dim=1						)

																					a__:       Any											=query.reshape(__magic_name__						).squeeze()
																					a__:       Optional[Any]											=key.reshape(__magic_name__						).squeeze()
																					a__:       List[Any]											=value.reshape(__magic_name__						).squeeze()

							for path in paths:
														a__:       Optional[Any]											=path["new"]

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														a__:       Any											=new_path.replace("down."     , "down_blocks."						)
														a__:       Union[str, Any]											=new_path.replace("up."     , "up_blocks."						)

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												a__:       Union[str, Any]											=new_path.replace(replacement["old"]     , replacement["new"]						)

														if "attentions" in new_path:
																					a__:       int											=old_checkpoint[path["old"]].squeeze()
														else:
																					a__:       int											=old_checkpoint[path["old"]]





def 						__lowerCamelCase       (      __magic_name__	:  Any     , __magic_name__	:  List[str]						):
							a__:       Tuple											={}

							a__:       Optional[Any]											=checkpoint["temb.dense.0.weight"]
							a__:       List[str]											=checkpoint["temb.dense.0.bias"]
							a__:       Dict											=checkpoint["temb.dense.1.weight"]
							a__:       int											=checkpoint["temb.dense.1.bias"]

							a__:       Union[str, Any]											=checkpoint["norm_out.weight"]
							a__:       Tuple											=checkpoint["norm_out.bias"]

							a__:       Dict											=checkpoint["conv_in.weight"]
							a__:       Any											=checkpoint["conv_in.bias"]
							a__:       List[Any]											=checkpoint["conv_out.weight"]
							a__:       Tuple											=checkpoint["conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "down" in layer}						)
							a__:       int											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       Any											=len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "up" in layer}						)
							a__:       List[str]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Dict											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"down.{i}.downsample.op.weight"
																					]
																					a__:       int											=checkpoint[F"down.{i}.downsample.op.bias"]
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any("block" in layer for layer in down_blocks[i]						):
																					a__:       List[Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       List[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__						)

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       str											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       str											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       Tuple											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , config=__magic_name__						)

							a__:       Union[str, Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       Dict											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

							# Mid new 2
							a__:       Any											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]     , )

							a__:       Optional[Any]											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]     , )

							a__:       Tuple											=renew_attention_paths(__magic_name__     , in_mid=__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]     , )

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Tuple											=checkpoint[
																					    F"up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[F"up.{i}.upsample.conv.bias"]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       Dict											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Dict											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Optional[int]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Union[str, Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 2						).split("."						)[:2]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       List[Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       List[str]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       List[str]											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       Optional[int]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__lowerCamelCase       (      __magic_name__	:  str     , __magic_name__	:  Optional[Any]						):
							a__:       List[str]											={}

							a__:       Optional[Any]											=checkpoint["encoder.norm_out.weight"]
							a__:       Any											=checkpoint["encoder.norm_out.bias"]

							a__:       List[str]											=checkpoint["encoder.conv_in.weight"]
							a__:       List[str]											=checkpoint["encoder.conv_in.bias"]
							a__:       Union[str, Any]											=checkpoint["encoder.conv_out.weight"]
							a__:       Optional[Any]											=checkpoint["encoder.conv_out.bias"]

							a__:       List[Any]											=checkpoint["decoder.norm_out.weight"]
							a__:       Tuple											=checkpoint["decoder.norm_out.bias"]

							a__:       Optional[int]											=checkpoint["decoder.conv_in.weight"]
							a__:       str											=checkpoint["decoder.conv_in.bias"]
							a__:       Tuple											=checkpoint["decoder.conv_out.weight"]
							a__:       Dict											=checkpoint["decoder.conv_out.bias"]

							a__:       Optional[int]											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "down" in layer}						)
							a__:       Optional[Any]											={
							    layer_id: [key for key in checkpoint if F"down.{layer_id}" in key] for layer_id in range(__magic_name__						)
							}

							a__:       int											=len({".".join(layer.split("."						)[:3]						) for layer in checkpoint if "up" in layer}						)
							a__:       Optional[int]											={layer_id: [key for key in checkpoint if F"up.{layer_id}" in key] for layer_id in range(__magic_name__						)}

							for i in range(__magic_name__						):
														a__:       Union[str, Any]											=(i - 1) // (config["layers_per_block"] + 1)

														if any("downsample" in layer for layer in down_blocks[i]						):
																					a__:       List[str]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.weight"
																					]
																					a__:       Union[str, Any]											=checkpoint[
																					    F"encoder.down.{i}.downsample.conv.bias"
																					]

														if any("block" in layer for layer in down_blocks[i]						):
																					a__:       Union[str, Any]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "block" in layer}						)
																					a__:       List[str]											={
																					    layer_id: [key for key in down_blocks[i] if F"block.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       Tuple											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__						)

														if any("attn" in layer for layer in down_blocks[i]						):
																					a__:       int											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in down_blocks[i] if "attn" in layer}						)
																					a__:       int											={
																					    layer_id: [key for key in down_blocks[i] if F"attn.{layer_id}" in key]
																					    for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"]						):
																																			a__:       str											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , config=__magic_name__						)

							a__:       List[Any]											=[key for key in checkpoint if "mid.block_1" in key]
							a__:       int											=[key for key in checkpoint if "mid.block_2" in key]
							a__:       List[str]											=[key for key in checkpoint if "mid.attn_1" in key]

							# Mid new 2
							a__:       Optional[Any]											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]     , )

							a__:       int											=renew_resnet_paths(__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]     , )

							a__:       Any											=renew_attention_paths(__magic_name__     , in_mid=__magic_name__						)
							assign_to_checkpoint(
							    __magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]     , )

							for i in range(__magic_name__						):
														a__:       List[Any]											=num_up_blocks - 1 - i

														if any("upsample" in layer for layer in up_blocks[i]						):
																					a__:       Optional[Any]											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.weight"
																					]
																					a__:       Any											=checkpoint[
																					    F"decoder.up.{i}.upsample.conv.bias"
																					]

														if any("block" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "block" in layer}						)
																					a__:       Union[str, Any]											={
																					    layer_id: [key for key in up_blocks[i] if F"block.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_blocks > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Union[str, Any]											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       Optional[Any]											=renew_resnet_paths(blocks[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

														if any("attn" in layer for layer in up_blocks[i]						):
																					a__:       List[str]											=len(
																					    {".".join(shave_segments(__magic_name__     , 3						).split("."						)[:3]						) for layer in up_blocks[i] if "attn" in layer}						)
																					a__:       Optional[int]											={
																					    layer_id: [key for key in up_blocks[i] if F"attn.{layer_id}" in key] for layer_id in range(__magic_name__						)
																					}

																					if num_attn > 0:
																												for j in range(config["layers_per_block"] + 1						):
																																			a__:       Dict											={"old": F"up_blocks.{i}", "new": F"up_blocks.{block_id}"}
																																			a__:       int											=renew_attention_paths(attns[j]						)
																																			assign_to_checkpoint(__magic_name__     , __magic_name__     , __magic_name__     , additional_replacements=[replace_indices]						)

							a__:       List[Any]											={k.replace("mid_new_2"     , "mid_block"						): v for k, v in new_checkpoint.items()}
							a__:       List[Any]											=checkpoint["quant_conv.weight"]
							a__:       Tuple											=checkpoint["quant_conv.bias"]
							if "quantize.embedding.weight" in checkpoint:
														a__:       Dict											=checkpoint["quantize.embedding.weight"]
							a__:       Union[str, Any]											=checkpoint["post_quant_conv.weight"]
							a__:       str											=checkpoint["post_quant_conv.bias"]

							return new_checkpoint


if __name__ == "__main__":
		__UpperCAmelCase						  =     argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		__UpperCAmelCase						  =     parser.parse_args()
		__UpperCAmelCase						  =     torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				__UpperCAmelCase						  =     json.loads(f.read())

		# unet case
		__UpperCAmelCase						  =     {key.split('''.''')[0] for key in checkpoint.keys()}
		if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
				__UpperCAmelCase						  =     convert_vq_autoenc_checkpoint(checkpoint, config)
		else:
				__UpperCAmelCase						  =     convert_ddpm_checkpoint(checkpoint, config)

		if "ddpm" in config:
				del config["ddpm"]

		if config["_class_name"] == "VQModel":
				__UpperCAmelCase						  =     VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				__UpperCAmelCase						  =     AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				__UpperCAmelCase						  =     UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				__UpperCAmelCase						  =     DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				__UpperCAmelCase						  =     DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)





