






'''simple docstring'''





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 __UpperCAmelCase (			A			:	Optional[Any]	,   A			:	Optional[int]	,   A			:	Optional[int]						)  -> Union[str, Any]:
   # Construct configuration depending on size
   UpperCAmelCase_     :   List[str]       =   {'''small''': (5_1_2, 8, 2_4), '''medium''': (1_0_2_4, 8, 3_6), '''large''': (1_5_3_6, 1_6, 4_8)}
   UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   MODELS[model_size]  # set model hyperparameters
   UpperCAmelCase_     :   Optional[Any]       =   ImageGPTConfig(n_embd=snake_case__	,   n_layer=snake_case__	,   n_head=snake_case__						)
   UpperCAmelCase_     :   Union[str, Any]       =   ImageGPTForCausalLM(snake_case__						)

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

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


if __name__ == "__main__":
      _UpperCamelCase		:		Dict								=			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.'
      )
      _UpperCamelCase		:		str								=			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )










'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase		:		str								=			{}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[Any]								=			['MBart50Tokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		List[str]								=			['MBart50TokenizerFast']


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa import MBartaaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
						import sys

						_UpperCamelCase		:		Optional[int]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


