





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase_        =			{"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase_        =			["""BertGenerationTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase_        =			[
          """BertGenerationDecoder""",
          """BertGenerationEncoder""",
          """BertGenerationPreTrainedModel""",
          """load_tf_weights_in_bert_generation""",
      ]


if TYPE_CHECKING:
      from .configuration_bert_generation import BertGenerationConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_generation import BertGenerationTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert_generation import (
                BertGenerationDecoder,
                BertGenerationEncoder,
                BertGenerationPreTrainedModel,
                load_tf_weights_in_bert_generation,
            )

else:
      import sys

      lowerCAmelCase_        =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  # Save pytorch-model
  _snake_case      : List[Any]														=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _snake_case      : Optional[Any]														=		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
  torch.save(model.state_dict()			,							lowerCAmelCase )
  print(F"""Save configuration file to {pytorch_config_dump_path}""" )
  with open(lowerCAmelCase			,							'w'			,							encoding='utf-8' ) as f:
    f.write(config.to_json_string() )


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--imagegpt_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the TensorFlow checkpoint path.""",
      )
      parser.add_argument(
          """--model_size""",
          default=None,
          type=str,
          required=True,
          help="""Size of the model (can be either 'small', 'medium' or 'large').""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )




