

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)




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__)

