


'''simple docstring'''



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					# Initialise PyTorch model
					__a			:       int					    =  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE   )
					print(F"""Building PyTorch model from configuration: {config}"""   )
					__a			:       Optional[Any]					    =  AlbertForPreTraining(_SCREAMING_SNAKE_CASE   )

					# Load weights from tf checkpoint
					load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# Save pytorch-model
					print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
					torch.save(model.state_dict()						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Tuple       =	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(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT 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.'
	)
	__lowercase	:						List[str]       =	parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)






'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase__											=	{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__											=	[
     '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPTNeoXJapaneseForCausalLM''',
     '''GPTNeoXJapaneseLayer''',
     '''GPTNeoXJapaneseModel''',
     '''GPTNeoXJapanesePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase__											=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

