




'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax
			import jax.numpy as jnp

			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)
			from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
			import torch

class      _a       :






					def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=14      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=False      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=[[["global", "local"], 2]]      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=0.0_2      ,)							->    List[Any]:
										_snake_case							=   parent
										_snake_case							=   batch_size
										_snake_case							=   seq_length
										_snake_case							=   is_training
										_snake_case							=   use_input_mask
										_snake_case							=   use_token_type_ids
										_snake_case							=   use_labels
										_snake_case							=   vocab_size
										_snake_case							=   hidden_size
										_snake_case							=   num_hidden_layers
										_snake_case							=   num_attention_heads
										_snake_case							=   attention_types
										_snake_case							=   intermediate_size
										_snake_case							=   hidden_act
										_snake_case							=   hidden_dropout_prob
										_snake_case							=   attention_probs_dropout_prob
										_snake_case							=   max_position_embeddings
										_snake_case							=   window_size
										_snake_case							=   initializer_range
										_snake_case							=   None
										_snake_case							=   vocab_size - 1
										_snake_case							=   vocab_size - 1
										_snake_case							=   vocab_size - 1






					def        _lowercase			(				self					)							->    Union[str, Any]:
										_snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)

										_snake_case							=   None
										if self.use_input_mask:
															_snake_case							=   random_attention_mask([self.batch_size, self.seq_length]					)

										_snake_case							=   GPTNeoConfig(
										    vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,num_layers=self.num_hidden_layers      ,num_heads=self.num_attention_heads      ,max_position_embeddings=self.max_position_embeddings      ,use_cache=lowercase_      ,bos_token_id=self.bos_token_id      ,eos_token_id=self.eos_token_id      ,pad_token_id=self.pad_token_id      ,window_size=self.window_size      ,attention_types=self.attention_types      ,)

										return (config, input_ids, input_mask)






					def        _lowercase			(				self					)							->    List[Any]:
										_snake_case							=   self.prepare_config_and_inputs()
										_snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
										_snake_case							=   {"input_ids": input_ids, "attention_mask": attention_mask}
										return config, inputs_dict






					def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    str:
										_snake_case							=   20
										_snake_case							=   model_class_name(lowercase_					)

										_snake_case							=   model.init_cache(input_ids.shape[0]      ,lowercase_					)
										_snake_case							=   jnp.ones((input_ids.shape[0], max_decoder_length)      ,dtype="i4"					)

										_snake_case							=   jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1					)[None, :]      ,(input_ids.shape[0], input_ids.shape[-1] - 1)					)
										_snake_case							=   model(
										    input_ids[:, :-1]      ,attention_mask=lowercase_      ,past_key_values=lowercase_      ,position_ids=lowercase_      ,)

										_snake_case							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,dtype="i4"					)
										_snake_case							=   model(
										    input_ids[:, -1:]      ,attention_mask=lowercase_      ,past_key_values=outputs_cache.past_key_values      ,position_ids=lowercase_      ,)

										_snake_case							=   model(lowercase_					)

										_snake_case							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
										self.parent.assertTrue(diff < 1e-3      ,msg=f"""Max diff is {diff}"""					)






					def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    int:
										_snake_case							=   20
										_snake_case							=   model_class_name(lowercase_					)

										_snake_case							=   jnp.concatenate(
										    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]      ,axis=-1      ,)

										_snake_case							=   model.init_cache(input_ids.shape[0]      ,lowercase_					)
										_snake_case							=   jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1					)[None, :]      ,(input_ids.shape[0], input_ids.shape[-1] - 1)					)

										_snake_case							=   model(
										    input_ids[:, :-1]      ,attention_mask=lowercase_      ,past_key_values=lowercase_      ,position_ids=lowercase_      ,)
										_snake_case							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,dtype="i4"					)
										_snake_case							=   model(
										    input_ids[:, -1:]      ,past_key_values=outputs_cache.past_key_values      ,attention_mask=lowercase_      ,position_ids=lowercase_      ,)

										_snake_case							=   model(lowercase_      ,attention_mask=lowercase_					)

										_snake_case							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
										self.parent.assertTrue(diff < 1e-3      ,msg=f"""Max diff is {diff}"""					)







@require_flax
class      _a       (		lowerCamelCase__      ,      lowerCamelCase__      ,      unittest.TestCase							):
					SCREAMING_SNAKE_CASE_							:					int					       =		(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					SCREAMING_SNAKE_CASE_							:					int					       =		(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()






					def        _lowercase			(				self					)							->    Any:
										_snake_case							=   FlaxGPTNeoModelTester(self					)






					def        _lowercase			(				self					)							->    Optional[Any]:






										for model_class_name in self.all_model_classes:
															_snake_case							,      _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowercase_      ,lowercase_      ,lowercase_      ,lowercase_					)






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






										for model_class_name in self.all_model_classes:
															_snake_case							,      _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowercase_      ,lowercase_      ,lowercase_      ,lowercase_					)






					@slow
					def        _lowercase			(				self					)							->    int:
										_snake_case							=   GPTaTokenizer.from_pretrained("gpt2"      ,pad_token="<|endoftext|>"      ,padding_side="left"					)
										_snake_case							=   tokenizer(["Hello this is a long string", "Hey"]      ,return_tensors="np"      ,padding=lowercase_      ,truncation=lowercase_					)

										_snake_case							=   FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"					)
										_snake_case							=   False
										_snake_case							=   model.config.eos_token_id

										_snake_case							=   jax.jit(model.generate					)

										_snake_case							=   jit_generate(
										    inputs["input_ids"]      ,attention_mask=inputs["attention_mask"]      ,pad_token_id=tokenizer.pad_token_id					).sequences

										_snake_case							=   tokenizer.batch_decode(lowercase_      ,skip_special_tokens=lowercase_					)

										_snake_case							=   [
										    "Hello this is a long string of text.\n\nI\'m trying to get the text of the",
										    "Hey, I\'m a little late to the party. I\'m going to",
										]

										self.assertListEqual(lowercase_      ,lowercase_					)






					@is_pt_flax_cross_test
					def        _lowercase			(				self					)							->    Any:
										_snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__					):
																				# prepare inputs
																				_snake_case							=   self._prepare_for_class(lowercase_      ,lowercase_					)
																				_snake_case							=   {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				_snake_case							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				_snake_case							=   getattr(lowercase_      ,lowercase_					)

																				_snake_case							,      _snake_case							=   pt_inputs["input_ids"].shape
																				_snake_case							=   np.random.randint(0      ,seq_length - 1      ,size=(batch_size,)					)
																				for batch_idx, start_index in enumerate(lowercase_					):
																									_snake_case							=   0
																									_snake_case							=   1
																									_snake_case							=   0
																									_snake_case							=   1
																				_snake_case							=   pt_model_class(lowercase_					).eval()
																				_snake_case							=   model_class(lowercase_      ,dtype=jnp.floataa					)

																				_snake_case							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,lowercase_					)
																				_snake_case							=   fx_state

																				with torch.no_grad():
																									_snake_case							=   pt_model(**lowercase_					).to_tuple()

																				_snake_case							=   fx_model(**lowercase_					).to_tuple()
																				self.assertEqual(len(lowercase_					)      ,len(lowercase_					)      ,"Output lengths differ between Flax and PyTorch"					)
																				for fx_output, pt_output in zip(lowercase_      ,lowercase_					):
																									self.assert_almost_equals(fx_output[:, -1]      ,pt_output[:, -1].numpy()      ,4e-2					)

																				with tempfile.TemporaryDirectory() as tmpdirname:
																									pt_model.save_pretrained(lowercase_					)
																									_snake_case							=   model_class.from_pretrained(lowercase_      ,from_pt=lowercase_					)

																				_snake_case							=   fx_model_loaded(**lowercase_					).to_tuple()
																				self.assertEqual(
																				    len(lowercase_					)      ,len(lowercase_					)      ,"Output lengths differ between Flax and PyTorch"					)
																				for fx_output_loaded, pt_output in zip(lowercase_      ,lowercase_					):
																									self.assert_almost_equals(fx_output_loaded[:, -1]      ,pt_output[:, -1].numpy()      ,4e-2					)






					@is_pt_flax_cross_test
					def        _lowercase			(				self					)							->    Any:
										_snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__					):
																				# prepare inputs
																				_snake_case							=   self._prepare_for_class(lowercase_      ,lowercase_					)
																				_snake_case							=   {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																				# load corresponding PyTorch class
																				_snake_case							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
																				_snake_case							=   getattr(lowercase_      ,lowercase_					)

																				_snake_case							=   pt_model_class(lowercase_					).eval()
																				_snake_case							=   model_class(lowercase_      ,dtype=jnp.floataa					)

																				_snake_case							=   load_flax_weights_in_pytorch_model(lowercase_      ,fx_model.params					)
																				_snake_case							,      _snake_case							=   pt_inputs["input_ids"].shape
																				_snake_case							=   np.random.randint(0      ,seq_length - 1      ,size=(batch_size,)					)
																				for batch_idx, start_index in enumerate(lowercase_					):
																									_snake_case							=   0
																									_snake_case							=   1
																									_snake_case							=   0
																									_snake_case							=   1

																				# make sure weights are tied in PyTorch
																				pt_model.tie_weights()

																				with torch.no_grad():
																									_snake_case							=   pt_model(**lowercase_					).to_tuple()

																				_snake_case							=   fx_model(**lowercase_					).to_tuple()
																				self.assertEqual(len(lowercase_					)      ,len(lowercase_					)      ,"Output lengths differ between Flax and PyTorch"					)
																				for fx_output, pt_output in zip(lowercase_      ,lowercase_					):
																									self.assert_almost_equals(fx_output[:, -1]      ,pt_output[:, -1].numpy()      ,4e-2					)

																				with tempfile.TemporaryDirectory() as tmpdirname:
																									fx_model.save_pretrained(lowercase_					)
																									_snake_case							=   pt_model_class.from_pretrained(lowercase_      ,from_flax=lowercase_					)

																				with torch.no_grad():
																									_snake_case							=   pt_model_loaded(**lowercase_					).to_tuple()

																				self.assertEqual(
																				    len(lowercase_					)      ,len(lowercase_					)      ,"Output lengths differ between Flax and PyTorch"					)
																				for fx_output, pt_output in zip(lowercase_      ,lowercase_					):
																									self.assert_almost_equals(fx_output[:, -1]      ,pt_output[:, -1].numpy()      ,4e-2					)






					@slow
					def        _lowercase			(				self					)							->    Any:






										for model_class_name in self.all_model_classes:
															_snake_case							=   model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"					)
															_snake_case							=   model(np.ones((1, 1)					)					)
															self.assertIsNotNone(lowercase_					)








'''simple docstring'''





UpperCamelCase_		:    Optional[Any]									=		'''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

UpperCamelCase_		:    List[Any]									=		[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]


