code: 
  
  
  
  '''simple docstring'''
  
  
  
  
  
  # Copyright 2021 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import json
  import os
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import yaml
  
  from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
  from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION
  
  
  __A       					=		os.path.expanduser(
      os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
  )
  __A       					=		os.path.join(hf_cache_home, '''accelerate''')
  __A       					=		os.path.join(cache_dir, '''default_config.yaml''')
  __A       					=		os.path.join(cache_dir, '''default_config.yaml''')
  
  # For backward compatibility: the default config is the json one if it's the only existing file.
  if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
  	__A       					=		default_yaml_config_file
  else:
  	__A       					=		default_json_config_file
  
  
  
  def  __a  (					lowerCAmelCase_     :      int       )   ->				Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if config_file is not None:
  												if not os.path.isfile(lowerCAmelCase_       ):
  																		raise FileNotFoundError(
  																		    F"""The passed configuration file `{config_file}` does not exist. """
  																		    """Please pass an existing file to `accelerate launch`, or use the the default one """
  																		    """created through `accelerate config` and run `accelerate launch` """
  																		    """without the `--config_file` argument."""       )
  						else:
  												UpperCAmelCase_=     default_config_file
  						with open(lowerCAmelCase_    ,"""r"""    ,encoding="""utf-8"""       ) as f:
  												if config_file.endswith(""".json"""       ):
  																		if (
  																		    json.load(lowerCAmelCase_       ).get("""compute_environment"""    ,ComputeEnvironment.LOCAL_MACHINE       )
  																		    == ComputeEnvironment.LOCAL_MACHINE
  																		):
  																								UpperCAmelCase_=     ClusterConfig
  																		else:
  																								UpperCAmelCase_=     SageMakerConfig
  																		return config_class.from_json_file(json_file=lowerCAmelCase_       )
  												else:
  																		if (
  																		    yaml.safe_load(lowerCAmelCase_       ).get("""compute_environment"""    ,ComputeEnvironment.LOCAL_MACHINE       )
  																		    == ComputeEnvironment.LOCAL_MACHINE
  																		):
  																								UpperCAmelCase_=     ClusterConfig
  																		else:
  																								UpperCAmelCase_=     SageMakerConfig
  																		return config_class.from_yaml_file(yaml_file=lowerCAmelCase_       )
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      ComputeEnvironment
  	a__   :      Union[DistributedType, SageMakerDistributedType]
  	a__   :      str
  	a__   :      bool
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Optional[int]:
  							UpperCAmelCase_=     self.__dict__
  							# For serialization, it's best to convert Enums to strings (or their underlying value type).
  							for key, value in result.items():
  													if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																			UpperCAmelCase_=     value.value
  													if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  ) and not bool(__UpperCAmelCase  ):
  																			UpperCAmelCase_=     None
  							UpperCAmelCase_=     {k: v for k, v in result.items() if v is not None}
  							return result
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Optional[Any]		,						__UpperCAmelCase			:		List[Any]=None  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     default_json_config_file if json_file is None else json_file
  							with open(__UpperCAmelCase		,						"""r"""		,						encoding="""utf-8"""  ) as f:
  													UpperCAmelCase_=     json.load(__UpperCAmelCase  )
  							if "compute_environment" not in config_dict:
  													UpperCAmelCase_=     ComputeEnvironment.LOCAL_MACHINE
  							if "mixed_precision" not in config_dict:
  													UpperCAmelCase_=     "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
  							if "fp16" in config_dict:  # Convert the config to the new format.
  													del config_dict["fp16"]
  							if "dynamo_backend" in config_dict:  # Convert the config to the new format.
  													UpperCAmelCase_=     config_dict.pop("""dynamo_backend"""  )
  													UpperCAmelCase_=     {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
  							if "use_cpu" not in config_dict:
  													UpperCAmelCase_=     False
  							return cls(**__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		List[Any]  )    ->				int:
  
  
  
  
  
  
  							with open(__UpperCAmelCase		,						"""w"""		,						encoding="""utf-8"""  ) as f:
  													UpperCAmelCase_=     json.dumps(self.to_dict()		,						indent=2		,						sort_keys=__UpperCAmelCase  ) + "\n"
  													f.write(__UpperCAmelCase  )
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Any		,						__UpperCAmelCase			:		Union[str, Any]=None  )    ->				List[Any]:
  							UpperCAmelCase_=     default_yaml_config_file if yaml_file is None else yaml_file
  							with open(__UpperCAmelCase		,						"""r"""		,						encoding="""utf-8"""  ) as f:
  													UpperCAmelCase_=     yaml.safe_load(__UpperCAmelCase  )
  							if "compute_environment" not in config_dict:
  													UpperCAmelCase_=     ComputeEnvironment.LOCAL_MACHINE
  
  							if "mixed_precision" not in config_dict:
  													UpperCAmelCase_=     "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
  							if isinstance(config_dict["""mixed_precision"""]		,						__UpperCAmelCase  ) and not config_dict["mixed_precision"]:
  													UpperCAmelCase_=     "no"
  							if "fp16" in config_dict:  # Convert the config to the new format.
  													del config_dict["fp16"]
  							if "dynamo_backend" in config_dict:  # Convert the config to the new format.
  													UpperCAmelCase_=     config_dict.pop("""dynamo_backend"""  )
  													UpperCAmelCase_=     {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
  							if "use_cpu" not in config_dict:
  													UpperCAmelCase_=     False
  							return cls(**__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		List[Any]  )    ->				List[str]:
  
  
  
  
  
  
  							with open(__UpperCAmelCase		,						"""w"""		,						encoding="""utf-8"""  ) as f:
  													yaml.safe_dump(self.to_dict()		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Union[str, Any]:
  
  
  
  
  
  
  							if isinstance(self.compute_environment		,						__UpperCAmelCase  ):
  													UpperCAmelCase_=     ComputeEnvironment(self.compute_environment  )
  							if isinstance(self.distributed_type		,						__UpperCAmelCase  ):
  													if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
  																			UpperCAmelCase_=     SageMakerDistributedType(self.distributed_type  )
  													else:
  																			UpperCAmelCase_=     DistributedType(self.distributed_type  )
  							if self.dynamo_config is None:
  													UpperCAmelCase_=     {}
  
  
  
  
  
  
  @dataclass
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      int
  	a__   :      int	     =							0
  	a__   :      int	     =							1
  	a__   :      Optional[str]	     =							None
  	a__   :      Optional[str]	     =							None
  	a__   :      Optional[int]	     =							None
  	a__   :      Optional[str]	     =							"static"
  	a__   :      Optional[bool]	     =							False
  	a__   :      str	     =							"main"
  
  	# args for deepspeed_plugin
  	a__   :      dict	     =							None
  	# args for fsdp
  	a__   :      dict	     =							None
  	# args for megatron_lm
  	a__   :      dict	     =							None
  	# args for ipex
  	a__   :      dict	     =							None
  	# args for TPU
  	a__   :      bool	     =							False
  
  	# args for TPU pods
  	a__   :      str	     =							None
  	a__   :      str	     =							None
  	a__   :      bool	     =							False
  	a__   :      bool	     =							False
  	a__   :      str	     =							None
  	a__   :      List[str]	     =							None
  	a__   :      List[str]	     =							None
  	a__   :      List[str]	     =							None
  
  	# args for dynamo
  	a__   :      dict	     =							None
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Tuple:
  
  
  
  
  
  
  							if self.deepspeed_config is None:
  													UpperCAmelCase_=     {}
  							if self.fsdp_config is None:
  													UpperCAmelCase_=     {}
  							if self.megatron_lm_config is None:
  													UpperCAmelCase_=     {}
  							if self.ipex_config is None:
  													UpperCAmelCase_=     {}
  							return super().__post_init__()
  
  
  
  
  
  
  @dataclass
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      str
  	a__   :      str
  	a__   :      Optional[str]	     =							None
  	a__   :      Optional[str]	     =							None
  	a__   :      str	     =							"us-east-1"
  	a__   :      int	     =							1
  	a__   :      str	     =							"all"
  	a__   :      str	     =							F'accelerate-sagemaker-{num_machines}'
  	a__   :      str	     =							SAGEMAKER_PYTORCH_VERSION
  	a__   :      str	     =							SAGEMAKER_TRANSFORMERS_VERSION
  	a__   :      str	     =							SAGEMAKER_PYTHON_VERSION
  	a__   :      str	     =							None
  	a__   :      str	     =							None
  	a__   :      dict	     =							None
  	a__   :      dict	     =							None
  
  
  
  
  
style_context: 
  
  
  
  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 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		int		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]=14		,						__UpperCAmelCase			:		Tuple=7		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Any=True		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=True		,						__UpperCAmelCase			:		Optional[Any]=99		,						__UpperCAmelCase			:		str=32		,						__UpperCAmelCase			:		Any=4		,						__UpperCAmelCase			:		Optional[Any]=4		,						__UpperCAmelCase			:		Union[str, Any]=[[["global", "local"], 2]]		,						__UpperCAmelCase			:		int=37		,						__UpperCAmelCase			:		str="gelu"		,						__UpperCAmelCase			:		int=0.1		,						__UpperCAmelCase			:		Tuple=0.1		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=7		,						__UpperCAmelCase			:		List[str]=0.02		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     parent
  							UpperCAmelCase_=     batch_size
  							UpperCAmelCase_=     seq_length
  							UpperCAmelCase_=     is_training
  							UpperCAmelCase_=     use_input_mask
  							UpperCAmelCase_=     use_token_type_ids
  							UpperCAmelCase_=     use_labels
  							UpperCAmelCase_=     vocab_size
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     attention_types
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     max_position_embeddings
  							UpperCAmelCase_=     window_size
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     vocab_size - 1
  							UpperCAmelCase_=     vocab_size - 1
  							UpperCAmelCase_=     vocab_size - 1
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Optional[Any]:
  							UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size  )
  
  							UpperCAmelCase_=     None
  							if self.use_input_mask:
  													UpperCAmelCase_=     random_attention_mask([self.batch_size, self.seq_length]  )
  
  							UpperCAmelCase_=     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=__UpperCAmelCase		,						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 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Dict:
  							UpperCAmelCase_=     self.prepare_config_and_inputs()
  							UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     config_and_inputs
  							UpperCAmelCase_=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
  							return config, inputs_dict
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				Dict:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.ones((input_ids.shape[0], max_decoder_length)		,						dtype="""i4"""  )
  
  							UpperCAmelCase_=     jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, -1:]		,						attention_mask=__UpperCAmelCase		,						past_key_values=outputs_cache.past_key_values		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Dict  )    ->				List[Any]:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     jnp.concatenate(
  							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]		,						axis=-1		,						)
  
  							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
  
  							UpperCAmelCase_=     model(
  							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, -1:]		,						past_key_values=outputs_cache.past_key_values		,						attention_mask=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     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 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[Any]	     =							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  	a__   :      List[Any]	     =							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Any:
  							UpperCAmelCase_=     FlaxGPTNeoModelTester(self  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				str:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  													self.model_tester.check_use_cache_forward(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Any:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  													self.model_tester.check_use_cache_forward_with_attn_mask(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained("""gpt2"""		,						pad_token="""<|endoftext|>"""		,						padding_side="""left"""  )
  							UpperCAmelCase_=     tokenizer(["""Hello this is a long string""", """Hey"""]		,						return_tensors="""np"""		,						padding=__UpperCAmelCase		,						truncation=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
  							UpperCAmelCase_=     False
  							UpperCAmelCase_=     model.config.eos_token_id
  
  							UpperCAmelCase_=     jax.jit(model.generate  )
  
  							UpperCAmelCase_=     jit_generate(
  							    inputs["""input_ids"""]		,						attention_mask=inputs["""attention_mask"""]		,						pad_token_id=tokenizer.pad_token_id  ).sequences
  
  							UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase		,						skip_special_tokens=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     [
  							    """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(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	@is_pt_flax_cross_test
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Union[str, Any]:
  							UpperCAmelCase_,     UpperCAmelCase_=     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
  																			UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
  																			# load corresponding PyTorch class
  																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
  																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
  																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
  																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )
  
  																			UpperCAmelCase_=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     fx_state
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()
  
  																			UpperCAmelCase_=     fx_model(**__UpperCAmelCase  ).to_tuple()
  																			self.assertEqual(len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									pt_model.save_pretrained(__UpperCAmelCase  )
  																									UpperCAmelCase_=     model_class.from_pretrained(__UpperCAmelCase		,						from_pt=__UpperCAmelCase  )
  
  																			UpperCAmelCase_=     fx_model_loaded(**__UpperCAmelCase  ).to_tuple()
  																			self.assertEqual(
  																			    len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output_loaded, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output_loaded[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  
  
  
  	@is_pt_flax_cross_test
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Dict:
  							UpperCAmelCase_,     UpperCAmelCase_=     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
  																			UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
  																			# load corresponding PyTorch class
  																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
  																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )
  
  																			UpperCAmelCase_=     load_flax_weights_in_pytorch_model(__UpperCAmelCase		,						fx_model.params  )
  																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
  																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
  																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  
  																			# make sure weights are tied in PyTorch
  																			pt_model.tie_weights()
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()
  
  																			UpperCAmelCase_=     fx_model(**__UpperCAmelCase  ).to_tuple()
  																			self.assertEqual(len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									fx_model.save_pretrained(__UpperCAmelCase  )
  																									UpperCAmelCase_=     pt_model_class.from_pretrained(__UpperCAmelCase		,						from_flax=__UpperCAmelCase  )
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model_loaded(**__UpperCAmelCase  ).to_tuple()
  
  																			self.assertEqual(
  																			    len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Dict:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_=     model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
  													UpperCAmelCase_=     model(np.ones((1, 1)  )  )
  													self.assertIsNotNone(__UpperCAmelCase  )
  
  
  
  
  