






"""simple docstring"""




import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    ElectraForCausalLM,
						    ElectraForMaskedLM,
						    ElectraForMultipleChoice,
						    ElectraForPreTraining,
						    ElectraForQuestionAnswering,
						    ElectraForSequenceClassification,
						    ElectraForTokenClassification,
						    ElectraModel,
						)
						from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST




class 				_a		:






				def __init__(				self	:					int,				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Optional[int]=1_3,				lowerCAmelCase__	:					str=7,				lowerCAmelCase__	:					Dict=True,				lowerCAmelCase__	:					Optional[Any]=True,				lowerCAmelCase__	:					Optional[Any]=True,				lowerCAmelCase__	:					List[str]=True,				lowerCAmelCase__	:					List[str]=9_9,				lowerCAmelCase__	:					Optional[int]=3_2,				lowerCAmelCase__	:					Union[str, Any]=5,				lowerCAmelCase__	:					Dict=4,				lowerCAmelCase__	:					Union[str, Any]=3_7,				lowerCAmelCase__	:					Union[str, Any]="gelu",				lowerCAmelCase__	:					Dict=0.1,				lowerCAmelCase__	:					Union[str, Any]=0.1,				lowerCAmelCase__	:					Dict=5_1_2,				lowerCAmelCase__	:					Optional[Any]=1_6,				lowerCAmelCase__	:					Optional[Any]=2,				lowerCAmelCase__	:					str=0.02,				lowerCAmelCase__	:					int=3,				lowerCAmelCase__	:					Dict=4,				lowerCAmelCase__	:					Tuple=None,				)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					parent
										_UpperCamelCase    :      Optional[int]						     =					batch_size
										_UpperCamelCase    :      str						     =					seq_length
										_UpperCamelCase    :      Any						     =					is_training
										_UpperCamelCase    :      Any						     =					use_input_mask
										_UpperCamelCase    :      int						     =					use_token_type_ids
										_UpperCamelCase    :      Optional[Any]						     =					use_labels
										_UpperCamelCase    :      List[str]						     =					vocab_size
										_UpperCamelCase    :      List[Any]						     =					hidden_size
										_UpperCamelCase    :      Optional[Any]						     =					num_hidden_layers
										_UpperCamelCase    :      List[Any]						     =					num_attention_heads
										_UpperCamelCase    :      List[str]						     =					intermediate_size
										_UpperCamelCase    :      List[str]						     =					hidden_act
										_UpperCamelCase    :      Tuple						     =					hidden_dropout_prob
										_UpperCamelCase    :      Union[str, Any]						     =					attention_probs_dropout_prob
										_UpperCamelCase    :      Optional[Any]						     =					max_position_embeddings
										_UpperCamelCase    :      Any						     =					type_vocab_size
										_UpperCamelCase    :      Union[str, Any]						     =					type_sequence_label_size
										_UpperCamelCase    :      int						     =					initializer_range
										_UpperCamelCase    :      Any						     =					num_labels
										_UpperCamelCase    :      str						     =					num_choices
										_UpperCamelCase    :      List[Any]						     =					scope






				def        snake_case			(				self	:					str							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					ids_tensor([self.batch_size, self.seq_length],				self.vocab_size							)

										_UpperCamelCase    :      List[Any]						     =					None
										if self.use_input_mask:
																_UpperCamelCase    :      Optional[Any]						     =					random_attention_mask([self.batch_size, self.seq_length]							)

										_UpperCamelCase    :      str						     =					None
										if self.use_token_type_ids:
																_UpperCamelCase    :      List[str]						     =					ids_tensor([self.batch_size, self.seq_length],				self.type_vocab_size							)

										_UpperCamelCase    :      int						     =					None
										_UpperCamelCase    :      str						     =					None
										_UpperCamelCase    :      List[Any]						     =					None
										if self.use_labels:
																_UpperCamelCase    :      List[Any]						     =					ids_tensor([self.batch_size],				self.type_sequence_label_size							)
																_UpperCamelCase    :      List[str]						     =					ids_tensor([self.batch_size, self.seq_length],				self.num_labels							)
																_UpperCamelCase    :      Optional[Any]						     =					ids_tensor([self.batch_size],				self.num_choices							)
																_UpperCamelCase    :      Dict						     =					ids_tensor([self.batch_size, self.seq_length],				1							)

										_UpperCamelCase    :      int						     =					self.get_config()

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    fake_token_labels,
										)






				def        snake_case			(				self	:					Tuple							)			->   Any:




										'''simple docstring'''



										return ElectraConfig(
										    vocab_size=self.vocab_size,				hidden_size=self.hidden_size,				num_hidden_layers=self.num_hidden_layers,				num_attention_heads=self.num_attention_heads,				intermediate_size=self.intermediate_size,				hidden_act=self.hidden_act,				hidden_dropout_prob=self.hidden_dropout_prob,				attention_probs_dropout_prob=self.attention_probs_dropout_prob,				max_position_embeddings=self.max_position_embeddings,				type_vocab_size=self.type_vocab_size,				is_decoder=lowerCAmelCase__,				initializer_range=self.initializer_range,				)






				def        snake_case			(				self	:					List[Any]							)			->   List[Any]:




										'''simple docstring'''



										(
										    _UpperCamelCase							
										)    :      Tuple						     =					self.prepare_config_and_inputs()

										_UpperCamelCase    :      int						     =					True
										_UpperCamelCase    :      Optional[Any]						     =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
										_UpperCamelCase    :      Dict						     =					ids_tensor([self.batch_size, self.seq_length],				vocab_size=2							)

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					Tuple,				)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					ElectraModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      List[str]						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__							)
										_UpperCamelCase    :      Union[str, Any]						     =					model(lowerCAmelCase__,				token_type_ids=lowerCAmelCase__							)
										_UpperCamelCase    :      List[Any]						     =					model(lowerCAmelCase__							)
										self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)							)






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Union[str, Any],				)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					True
										_UpperCamelCase    :      Optional[Any]						     =					ElectraModel(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      int						     =					model(
										    lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				encoder_hidden_states=lowerCAmelCase__,				encoder_attention_mask=lowerCAmelCase__,				)
										_UpperCamelCase    :      Tuple						     =					model(
										    lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				encoder_hidden_states=lowerCAmelCase__,				)
										_UpperCamelCase    :      Optional[int]						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__							)
										self.parent.assertEqual(result.last_hidden_state.shape,				(self.batch_size, self.seq_length, self.hidden_size)							)






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Union[str, Any],				)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					ElectraForMaskedLM(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Dict						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size)							)






				def        snake_case			(				self	:					str,				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					Optional[int],				)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					ElectraForCausalLM(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Union[str, Any]						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.vocab_size)							)






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					int,				)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.num_labels
										_UpperCamelCase    :      int						     =					ElectraForTokenClassification(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Optional[int]						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length, self.num_labels)							)






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					int,				)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					self.num_labels
										_UpperCamelCase    :      int						     =					ElectraForPreTraining(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Any						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.seq_length)							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					int,				)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					self.num_labels
										_UpperCamelCase    :      int						     =					ElectraForSequenceClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Tuple						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__							)
										self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_labels)							)






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					List[str],				)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					ElectraForQuestionAnswering(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      str						     =					model(
										    lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				start_positions=lowerCAmelCase__,				end_positions=lowerCAmelCase__,				)
										self.parent.assertEqual(result.start_logits.shape,				(self.batch_size, self.seq_length)							)
										self.parent.assertEqual(result.end_logits.shape,				(self.batch_size, self.seq_length)							)






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					List[Any],				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					Optional[int],				)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.num_choices
										_UpperCamelCase    :      Optional[Any]						     =					ElectraForMultipleChoice(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Optional[Any]						     =					input_ids.unsqueeze(1							).expand(-1,				self.num_choices,				-1							).contiguous()
										_UpperCamelCase    :      int						     =					token_type_ids.unsqueeze(1							).expand(-1,				self.num_choices,				-1							).contiguous()
										_UpperCamelCase    :      Dict						     =					input_mask.unsqueeze(1							).expand(-1,				self.num_choices,				-1							).contiguous()
										_UpperCamelCase    :      str						     =					model(
										    lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__,				labels=lowerCAmelCase__,				)
										self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_choices)							)






				def        snake_case			(				self	:					int							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					self.prepare_config_and_inputs()
										(
										    _UpperCamelCase							
										)    :      Any						     =					config_and_inputs
										_UpperCamelCase    :      str						     =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




@require_torch
class 				_a		(   _lowerCAmelCase			,       _lowerCAmelCase			,       unittest.TestCase       ):
				UpperCamelCase			=					(
				    (
				        ElectraModel,
				        ElectraForPreTraining,
				        ElectraForMaskedLM,
				        ElectraForCausalLM,
				        ElectraForMultipleChoice,
				        ElectraForTokenClassification,
				        ElectraForSequenceClassification,
				        ElectraForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase			=					(
				    {
				        '''feature-extraction''': ElectraModel,
				        '''fill-mask''': ElectraForMaskedLM,
				        '''question-answering''': ElectraForQuestionAnswering,
				        '''text-classification''': ElectraForSequenceClassification,
				        '''text-generation''': ElectraForCausalLM,
				        '''token-classification''': ElectraForTokenClassification,
				        '''zero-shot''': ElectraForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase			=					True






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					List[Any]=False							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					super()._prepare_for_class(lowerCAmelCase__,				lowerCAmelCase__,				return_labels=lowerCAmelCase__							)

										if return_labels:
																if model_class in get_values(lowerCAmelCase__							):
																						_UpperCamelCase    :      List[str]						     =					torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.seq_length),				dtype=torch.long,				device=lowerCAmelCase__							)
										return inputs_dict






				def        snake_case			(				self	:					Optional[int]							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					ElectraModelTester(self							)
										_UpperCamelCase    :      int						     =					ConfigTester(self,				config_class=lowerCAmelCase__,				hidden_size=3_7							)






				def        snake_case			(				self	:					Optional[int]							)			->   int:




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def        snake_case			(				self	:					Optional[int]							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_electra_model(*lowerCAmelCase__							)






				def        snake_case			(				self	:					str							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_electra_model_as_decoder(*lowerCAmelCase__							)






				def        snake_case			(				self	:					Dict							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																_UpperCamelCase    :      Optional[int]						     =					type
																self.model_tester.create_and_check_electra_model(*lowerCAmelCase__							)






				def        snake_case			(				self	:					Tuple							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_electra_for_masked_lm(*lowerCAmelCase__							)






				def        snake_case			(				self	:					Dict							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_electra_for_token_classification(*lowerCAmelCase__							)






				def        snake_case			(				self	:					int							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_electra_for_pretraining(*lowerCAmelCase__							)






				def        snake_case			(				self	:					str							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_electra_for_sequence_classification(*lowerCAmelCase__							)






				def        snake_case			(				self	:					int							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_electra_for_question_answering(*lowerCAmelCase__							)






				def        snake_case			(				self	:					Any							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_electra_for_multiple_choice(*lowerCAmelCase__							)






				@slow
				def        snake_case			(				self	:					Optional[int]							)			->   Optional[int]:




										'''simple docstring'''



										for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase    :      List[Any]						     =					ElectraModel.from_pretrained(lowerCAmelCase__							)
																self.assertIsNotNone(lowerCAmelCase__							)






				def        snake_case			(				self	:					str							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_electra_for_causal_lm(*lowerCAmelCase__							)




@require_torch
class 				_a		(   unittest.TestCase       ):






				@slow
				def        snake_case			(				self	:					Union[str, Any]							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					ElectraModel.from_pretrained('''google/electra-small-discriminator'''							)
										_UpperCamelCase    :      List[str]						     =					torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
										_UpperCamelCase    :      Union[str, Any]						     =					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
										_UpperCamelCase    :      Dict						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__							)[0]
										_UpperCamelCase    :      List[str]						     =					torch.Size((1, 1_1, 2_5_6)							)
										self.assertEqual(output.shape,				lowerCAmelCase__							)
										_UpperCamelCase    :      str						     =					torch.tensor(
										    [[[0.4_471, 0.6_821, -0.3_265], [0.4_627, 0.5_255, -0.3_668], [0.4_532, 0.3_313, -0.4_344]]]							)

										self.assertTrue(torch.allclose(output[:, 1:4, 1:4],				lowerCAmelCase__,				atol=1e-4							)							)










"""simple docstring"""




import argparse
import json

import torch

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





def  a_       (							_lowercase ,		_lowercase=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  a_       (							_lowercase ,		_lowercase=0     ):
						_UpperCamelCase    :      Dict						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item
												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''block.''' ,		'''resnets.'''     )
												_UpperCamelCase    :      int						     =					new_item.replace('''conv_shorcut''' ,		'''conv1'''     )
												_UpperCamelCase    :      List[str]						     =					new_item.replace('''in_shortcut''' ,		'''conv_shortcut'''     )
												_UpperCamelCase    :      Union[str, Any]						     =					new_item.replace('''temb_proj''' ,		'''time_emb_proj'''     )

												_UpperCamelCase    :      List[Any]						     =					shave_segments(_lowercase ,		n_shave_prefix_segments=_lowercase     )

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

						return mapping





def  a_       (							_lowercase ,		_lowercase=0 ,		_lowercase=False     ):
						_UpperCamelCase    :      str						     =					[]
						for old_item in old_list:
												_UpperCamelCase    :      Tuple						     =					old_item

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

												_UpperCamelCase    :      Optional[Any]						     =					new_item.replace('''proj_out''' ,		'''proj_attn'''     )
												_UpperCamelCase    :      Dict						     =					new_item.replace('''norm''' ,		'''group_norm'''     )

												_UpperCamelCase    :      str						     =					shave_segments(_lowercase ,		n_shave_prefix_segments=_lowercase     )
												mapping.append({'''old''': old_item, '''new''': new_item}     )

						return mapping





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase=None ,		_lowercase=None ,		_lowercase=None     ):
						assert isinstance(_lowercase ,		_lowercase     ), "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():
																		_UpperCamelCase    :      Union[str, Any]						     =					old_checkpoint[path]
																		_UpperCamelCase    :      Any						     =					old_tensor.shape[0] // 3

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

																		_UpperCamelCase    :      Dict						     =					old_tensor.shape[0] // config.get('''num_head_channels''' ,		1     ) // 3

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

																		_UpperCamelCase    :      Union[str, Any]						     =					query.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      Any						     =					key.reshape(_lowercase     ).squeeze()
																		_UpperCamelCase    :      str						     =					value.reshape(_lowercase     ).squeeze()

						for path in paths:
												_UpperCamelCase    :      List[str]						     =					path['''new''']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												_UpperCamelCase    :      List[str]						     =					new_path.replace('''down.''' ,		'''down_blocks.'''     )
												_UpperCamelCase    :      Optional[int]						     =					new_path.replace('''up.''' ,		'''up_blocks.'''     )

												if additional_replacements is not None:
																		for replacement in additional_replacements:
																								_UpperCamelCase    :      str						     =					new_path.replace(replacement['''old'''] ,		replacement['''new''']     )

												if "attentions" in new_path:
																		_UpperCamelCase    :      Optional[int]						     =					old_checkpoint[path['''old''']].squeeze()
												else:
																		_UpperCamelCase    :      Union[str, Any]						     =					old_checkpoint[path['''old''']]





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Union[str, Any]						     =					{}

						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.0.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''temb.dense.1.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''temb.dense.1.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''norm_out.weight''']
						_UpperCamelCase    :      Optional[Any]						     =					checkpoint['''norm_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_in.bias''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''conv_out.weight''']
						_UpperCamelCase    :      str						     =					checkpoint['''conv_out.bias''']

						_UpperCamelCase    :      Optional[int]						     =					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Union[str, Any]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

						_UpperCamelCase    :      int						     =					len({'''.'''.join(layer.split('''.'''     )[:2]     ) for layer in checkpoint if '''up''' in layer}     )
						_UpperCamelCase    :      Any						     =					{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowercase     )}

						for i in range(_lowercase     ):
												_UpperCamelCase    :      Optional[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Tuple						     =					checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		_UpperCamelCase    :      Union[str, Any]						     =					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]     ):
																		_UpperCamelCase    :      Optional[int]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in down_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      Optional[int]						     =					{
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(_lowercase     )
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      str						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase     )

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

																		if num_attn > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Union[str, Any]						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		config=_lowercase     )

						_UpperCamelCase    :      Dict						     =					[key for key in checkpoint if '''mid.block_1''' in key]
						_UpperCamelCase    :      int						     =					[key for key in checkpoint if '''mid.block_2''' in key]
						_UpperCamelCase    :      Tuple						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						_UpperCamelCase    :      Tuple						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,		)

						_UpperCamelCase    :      int						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,		)

						_UpperCamelCase    :      str						     =					renew_attention_paths(_lowercase ,		in_mid=_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}] ,		)

						for i in range(_lowercase     ):
												_UpperCamelCase    :      List[str]						     =					num_up_blocks - 1 - i

												if any('''upsample''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      List[str]						     =					checkpoint[
																		    F"""up.{i}.upsample.conv.weight"""
																		]
																		_UpperCamelCase    :      List[str]						     =					checkpoint[F"""up.{i}.upsample.conv.bias"""]

												if any('''block''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      Union[str, Any]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in up_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      Union[str, Any]						     =					{
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_lowercase     )
																		}

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

												if any('''attn''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      Dict						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		2     ).split('''.'''     )[:2]     ) for layer in up_blocks[i] if '''attn''' in layer}     )
																		_UpperCamelCase    :      Optional[Any]						     =					{
																		    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_lowercase     )
																		}

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

						_UpperCamelCase    :      int						     =					{k.replace('''mid_new_2''' ,		'''mid_block'''     ): v for k, v in new_checkpoint.items()}
						return new_checkpoint





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      List[Any]						     =					{}

						_UpperCamelCase    :      List[str]						     =					checkpoint['''encoder.norm_out.weight''']
						_UpperCamelCase    :      Tuple						     =					checkpoint['''encoder.norm_out.bias''']

						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_in.weight''']
						_UpperCamelCase    :      Any						     =					checkpoint['''encoder.conv_in.bias''']
						_UpperCamelCase    :      Optional[int]						     =					checkpoint['''encoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''encoder.conv_out.bias''']

						_UpperCamelCase    :      Dict						     =					checkpoint['''decoder.norm_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.norm_out.bias''']

						_UpperCamelCase    :      str						     =					checkpoint['''decoder.conv_in.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_in.bias''']
						_UpperCamelCase    :      List[Any]						     =					checkpoint['''decoder.conv_out.weight''']
						_UpperCamelCase    :      Union[str, Any]						     =					checkpoint['''decoder.conv_out.bias''']

						_UpperCamelCase    :      Tuple						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''down''' in layer}     )
						_UpperCamelCase    :      Optional[int]						     =					{
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase     )
						}

						_UpperCamelCase    :      List[str]						     =					len({'''.'''.join(layer.split('''.'''     )[:3]     ) for layer in checkpoint if '''up''' in layer}     )
						_UpperCamelCase    :      Tuple						     =					{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowercase     )}

						for i in range(_lowercase     ):
												_UpperCamelCase    :      List[Any]						     =					(i - 1) // (config['''layers_per_block'''] + 1)

												if any('''downsample''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Any						     =					checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.weight"""
																		]
																		_UpperCamelCase    :      Tuple						     =					checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.bias"""
																		]

												if any('''block''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      Optional[Any]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in down_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      int						     =					{
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(_lowercase     )
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Optional[int]						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase     )

												if any('''attn''' in layer for layer in down_blocks[i]     ):
																		_UpperCamelCase    :      List[Any]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in down_blocks[i] if '''attn''' in layer}     )
																		_UpperCamelCase    :      Optional[Any]						     =					{
																		    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
																		    for layer_id in range(_lowercase     )
																		}

																		if num_attn > 0:
																								for j in range(config['''layers_per_block''']     ):
																														_UpperCamelCase    :      Tuple						     =					renew_attention_paths(attns[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		config=_lowercase     )

						_UpperCamelCase    :      List[Any]						     =					[key for key in checkpoint if '''mid.block_1''' in key]
						_UpperCamelCase    :      Union[str, Any]						     =					[key for key in checkpoint if '''mid.block_2''' in key]
						_UpperCamelCase    :      int						     =					[key for key in checkpoint if '''mid.attn_1''' in key]

						# Mid new 2
						_UpperCamelCase    :      int						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}] ,		)

						_UpperCamelCase    :      Any						     =					renew_resnet_paths(_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}] ,		)

						_UpperCamelCase    :      Any						     =					renew_attention_paths(_lowercase ,		in_mid=_lowercase     )
						assign_to_checkpoint(
						    _lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}] ,		)

						for i in range(_lowercase     ):
												_UpperCamelCase    :      Dict						     =					num_up_blocks - 1 - i

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

												if any('''block''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      int						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in up_blocks[i] if '''block''' in layer}     )
																		_UpperCamelCase    :      str						     =					{
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_lowercase     )
																		}

																		if num_blocks > 0:
																								for j in range(config['''layers_per_block'''] + 1     ):
																														_UpperCamelCase    :      List[str]						     =					{'''old''': F"""up_blocks.{i}""", '''new''': F"""up_blocks.{block_id}"""}
																														_UpperCamelCase    :      str						     =					renew_resnet_paths(blocks[j]     )
																														assign_to_checkpoint(_lowercase ,		_lowercase ,		_lowercase ,		additional_replacements=[replace_indices]     )

												if any('''attn''' in layer for layer in up_blocks[i]     ):
																		_UpperCamelCase    :      Optional[int]						     =					len(
																		    {'''.'''.join(shave_segments(_lowercase ,		3     ).split('''.'''     )[:3]     ) for layer in up_blocks[i] if '''attn''' in layer}     )
																		_UpperCamelCase    :      Any						     =					{
																		    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_lowercase     )
																		}

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

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



