code: 
  
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[str]   = json.load(open(SCREAMING_SNAKE_CASE_			,  'r'    )    )
       lowercase__					:     Optional[int]   = GPTNeoConfig(
           hidden_size=config_json['n_embd']			,  num_layers=config_json['n_layer']			,  num_heads=config_json['n_head']			,  attention_types=config_json['attention_types']			,  max_position_embeddings=config_json['n_positions']			,  resid_dropout=config_json['res_dropout']			,  embed_dropout=config_json['embed_dropout']			,  attention_dropout=config_json['attn_dropout']			,  )
       print(f"""Building PyTorch model from configuration: {config}"""    )
       lowercase__					:     int   = GPTNeoForCausalLM(SCREAMING_SNAKE_CASE_    )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
       model.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
         )
         parser.add_argument(
             '''--config_file''',
             default=None,
             type=str,
             required=True,
             help=(
                 '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
                 '''This specifies the model architecture.'''
             ),
         )
         parser.add_argument(
             '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
         )
         snake_case_    						=				parser.parse_args()
         convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  