

"""simple docstring"""




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     :    Union[str, Any]    =		os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__A     :    Union[str, Any]    =		os.path.join(hf_cache_home, 'accelerate')
__A     :    Any    =		os.path.join(cache_dir, 'default_config.yaml')
__A     :    Optional[Any]    =		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     :    List[Any]    =		default_yaml_config_file
else:
	__A     :    Any    =		default_json_config_file
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if config_file is not None:
										if not os.path.isfile(_UpperCamelCase      ):
															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:
										A			=					default_config_file
					with open(_UpperCamelCase			,							"r"			,							encoding="utf-8"      ) as f:
										if config_file.endswith(".json"      ):
															if (
															    json.load(_UpperCamelCase      ).get("compute_environment"			,							ComputeEnvironment.LOCAL_MACHINE      )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				A			=					ClusterConfig
															else:
																				A			=					SageMakerConfig
															return config_class.from_json_file(json_file=_UpperCamelCase      )
										else:
															if (
															    yaml.safe_load(_UpperCamelCase      ).get("compute_environment"			,							ComputeEnvironment.LOCAL_MACHINE      )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				A			=					ClusterConfig
															else:
																				A			=					SageMakerConfig
															return config_class.from_yaml_file(yaml_file=_UpperCamelCase      )





@dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					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(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE):
																	A			=					value.value
												if isinstance(_SCREAMING_SNAKE_CASE     ,     _SCREAMING_SNAKE_CASE) and not bool(_SCREAMING_SNAKE_CASE):
																	A			=					None
							A			=					{k: v for k, v in result.items() if v is not None}
							return result


		@classmethod
		def 					SCREAMING_SNAKE_CASE__   (cls :		Any     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=None):
							A			=					default_json_config_file if json_file is None else json_file
							with open(_SCREAMING_SNAKE_CASE     ,     "r"     ,     encoding="utf-8") as f:
												A			=					json.load(_SCREAMING_SNAKE_CASE)
							if "compute_environment" not in config_dict:
												A			=					ComputeEnvironment.LOCAL_MACHINE
							if "mixed_precision" not in config_dict:
												A			=					"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.
												A			=					config_dict.pop("dynamo_backend")
												A			=					{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
							if "use_cpu" not in config_dict:
												A			=					False
							return cls(**_SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]):
							with open(_SCREAMING_SNAKE_CASE     ,     "w"     ,     encoding="utf-8") as f:
												A			=					json.dumps(self.to_dict()     ,     indent=2     ,     sort_keys=_SCREAMING_SNAKE_CASE) + "\n"
												f.write(_SCREAMING_SNAKE_CASE)


		@classmethod
		def 					SCREAMING_SNAKE_CASE__   (cls :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=None):
							A			=					default_yaml_config_file if yaml_file is None else yaml_file
							with open(_SCREAMING_SNAKE_CASE     ,     "r"     ,     encoding="utf-8") as f:
												A			=					yaml.safe_load(_SCREAMING_SNAKE_CASE)
							if "compute_environment" not in config_dict:
												A			=					ComputeEnvironment.LOCAL_MACHINE

							if "mixed_precision" not in config_dict:
												A			=					"fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
							if isinstance(config_dict["mixed_precision"]     ,     _SCREAMING_SNAKE_CASE) and not config_dict["mixed_precision"]:
												A			=					"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.
												A			=					config_dict.pop("dynamo_backend")
												A			=					{} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
							if "use_cpu" not in config_dict:
												A			=					False
							return cls(**_SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Any):
							with open(_SCREAMING_SNAKE_CASE     ,     "w"     ,     encoding="utf-8") as f:
												yaml.safe_dump(self.to_dict()     ,     _SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							if isinstance(self.compute_environment     ,     _SCREAMING_SNAKE_CASE):
												A			=					ComputeEnvironment(self.compute_environment)
							if isinstance(self.distributed_type     ,     _SCREAMING_SNAKE_CASE):
												if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																	A			=					SageMakerDistributedType(self.distributed_type)
												else:
																	A			=					DistributedType(self.distributed_type)
							if self.dynamo_config is None:
												A			=					{}





@dataclass
class 			__UpperCamelCase (   _UpperCamelCase						):
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    "static"
		SCREAMING_SNAKE_CASE								=    False
		SCREAMING_SNAKE_CASE								=    "main"

		# args for deepspeed_plugin
		SCREAMING_SNAKE_CASE								=    None
		# args for fsdp
		SCREAMING_SNAKE_CASE								=    None
		# args for megatron_lm
		SCREAMING_SNAKE_CASE								=    None
		# args for ipex
		SCREAMING_SNAKE_CASE								=    None
		# args for TPU
		SCREAMING_SNAKE_CASE								=    False

		# args for TPU pods
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    False
		SCREAMING_SNAKE_CASE								=    False
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None

		# args for dynamo
		SCREAMING_SNAKE_CASE								=    None


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							if self.deepspeed_config is None:
												A			=					{}
							if self.fsdp_config is None:
												A			=					{}
							if self.megatron_lm_config is None:
												A			=					{}
							if self.ipex_config is None:
												A			=					{}
							return super().__post_init__()





@dataclass
class 			__UpperCamelCase (   _UpperCamelCase						):
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    "us-east-1"
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    "all"
		SCREAMING_SNAKE_CASE								=    F'''accelerate-sagemaker-{num_machines}'''
		SCREAMING_SNAKE_CASE								=    SAGEMAKER_PYTORCH_VERSION
		SCREAMING_SNAKE_CASE								=    SAGEMAKER_TRANSFORMERS_VERSION
		SCREAMING_SNAKE_CASE								=    SAGEMAKER_PYTHON_VERSION
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None
		SCREAMING_SNAKE_CASE								=    None






'''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 	__UpperCAmelCase				:





       '''simple docstring'''



       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=0.02    ,     )		->     Dict:
           A_     =   parent
           A_     =   batch_size
           A_     =   seq_length
           A_     =   is_training
           A_     =   use_input_mask
           A_     =   use_token_type_ids
           A_     =   use_labels
           A_     =   vocab_size
           A_     =   hidden_size
           A_     =   num_hidden_layers
           A_     =   num_attention_heads
           A_     =   attention_types
           A_     =   intermediate_size
           A_     =   hidden_act
           A_     =   hidden_dropout_prob
           A_     =   attention_probs_dropout_prob
           A_     =   max_position_embeddings
           A_     =   window_size
           A_     =   initializer_range
           A_     =   None
           A_     =   vocab_size - 1
           A_     =   vocab_size - 1
           A_     =   vocab_size - 1

       def 					__A       (      self )		->     Optional[int]:
           A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size )

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

           A_     =   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=_SCREAMING_SNAKE_CASE    ,     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 					__A       (      self )		->     str:
           A_     =   self.prepare_config_and_inputs()
           A_   ,A_   ,A_     =   config_and_inputs
           A_     =   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Union[str, Any]:
           A_     =   20
           A_     =   model_class_name(_SCREAMING_SNAKE_CASE )

           A_     =   model.init_cache(input_ids.shape[0]    ,     _SCREAMING_SNAKE_CASE )
           A_     =   jnp.ones((input_ids.shape[0], max_decoder_length)    ,     dtype='''i4''' )

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

           A_     =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,     dtype='''i4''' )
           A_     =   model(
               input_ids[:, -1:]    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     past_key_values=outputs_cache.past_key_values    ,     position_ids=_SCREAMING_SNAKE_CASE    ,     )

           A_     =   model(_SCREAMING_SNAKE_CASE )

           A_     =   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 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     List[Any]:
           A_     =   20
           A_     =   model_class_name(_SCREAMING_SNAKE_CASE )

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

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

           A_     =   model(
               input_ids[:, :-1]    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     past_key_values=_SCREAMING_SNAKE_CASE    ,     position_ids=_SCREAMING_SNAKE_CASE    ,     )
           A_     =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,     dtype='''i4''' )
           A_     =   model(
               input_ids[:, -1:]    ,     past_key_values=outputs_cache.past_key_values    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     position_ids=_SCREAMING_SNAKE_CASE    ,     )

           A_     =   model(_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE )

           A_     =   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 	__UpperCAmelCase				(				_UpperCamelCase						,      _UpperCamelCase						,      unittest.TestCase     ):





       '''simple docstring'''



       __lowercase					:					List[str]    =				(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
       __lowercase					:					Dict    =				(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

       def 					__A       (      self )		->     Any:
           A_     =   FlaxGPTNeoModelTester(self )

       def 					__A       (      self )		->     Optional[int]:
           for model_class_name in self.all_model_classes:
               A_   ,A_   ,A_     =   self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Union[str, Any]:
           for model_class_name in self.all_model_classes:
               A_   ,A_   ,A_     =   self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

       @slow
       def 					__A       (      self )		->     Optional[Any]:
           A_     =   GPTaTokenizer.from_pretrained('''gpt2'''    ,     pad_token='''<|endoftext|>'''    ,     padding_side='''left''' )
           A_     =   tokenizer(['''Hello this is a long string''', '''Hey''']    ,     return_tensors='''np'''    ,     padding=_SCREAMING_SNAKE_CASE    ,     truncation=_SCREAMING_SNAKE_CASE )

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

           A_     =   jax.jit(model.generate )

           A_     =   jit_generate(
               inputs['''input_ids''']    ,     attention_mask=inputs['''attention_mask''']    ,     pad_token_id=tokenizer.pad_token_id ).sequences

           A_     =   tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,     skip_special_tokens=_SCREAMING_SNAKE_CASE )

           A_     =   [
               '''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(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )

       @is_pt_flax_cross_test
       def 					__A       (      self )		->     Union[str, Any]:
           A_   ,A_     =   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
                   A_     =   self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
                   A_     =   {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

                   A_     =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,     _SCREAMING_SNAKE_CASE )
                   A_     =   fx_state

                   with torch.no_grad():
                       A_     =   pt_model(**_SCREAMING_SNAKE_CASE ).to_tuple()

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

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

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

       @is_pt_flax_cross_test
       def 					__A       (      self )		->     Union[str, Any]:
           A_   ,A_     =   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
                   A_     =   self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
                   A_     =   {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                   A_     =   pt_model_class(_SCREAMING_SNAKE_CASE ).eval()
                   A_     =   model_class(_SCREAMING_SNAKE_CASE    ,     dtype=jnp.floataa )

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

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

                   with torch.no_grad():
                       A_     =   pt_model(**_SCREAMING_SNAKE_CASE ).to_tuple()

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

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

                   with torch.no_grad():
                       A_     =   pt_model_loaded(**_SCREAMING_SNAKE_CASE ).to_tuple()

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

       @slow
       def 					__A       (      self )		->     List[str]:
           for model_class_name in self.all_model_classes:
               A_     =   model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
               A_     =   model(np.ones((1, 1) ) )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE )






