import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_lowerCamelCase       : List[Any]      					=		re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')


@total_ordering
@dataclass
class     lowercase     :
							lowercase__    :							str
							lowercase__    :							Optional[str]										=				None
							lowercase__    :							Optional[Union[str, int]]										=				None
							lowercase__    :							Optional[Union[str, int]]										=				None
							lowercase__    :							Optional[Union[str, int]]										=				None




							def 						__snake_case(    self :       List[Any]   )       ->					Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							_str_to_version_tuple(self.version_str   )




							def __repr__(    self :       List[str]   )       ->					List[str]:



												'''simple docstring'''

												return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"




							@property
							def 						__snake_case(    self :       Optional[int]   )       ->					Any:



												'''simple docstring'''

												return self.major, self.minor, self.patch




							def 						__snake_case(    self :       Any						,				_UpperCamelCase :       Optional[int]   )       ->					int:



												'''simple docstring'''

												if isinstance(UpperCamelCase__						,				UpperCamelCase__   ):
																	return Version(UpperCamelCase__   )
												elif isinstance(UpperCamelCase__						,				UpperCamelCase__   ):
																	return other
												raise TypeError(F"{other} (type {type(UpperCamelCase__   )}) cannot be compared to version."   )




							def __eq__(    self :       List[str]						,				_UpperCamelCase :       Optional[int]   )       ->					Optional[int]:



												'''simple docstring'''

												try:
																	SCREAMING_SNAKE_CASE           =							self._validate_operand(UpperCamelCase__   )
												except (TypeError, ValueError):
																	return False
												else:
																	return self.tuple == other.tuple




							def __lt__(    self :       str						,				_UpperCamelCase :       Optional[Any]   )       ->					Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self._validate_operand(UpperCamelCase__   )
												return self.tuple < other.tuple




							def __hash__(    self :       str   )       ->					Tuple:



												'''simple docstring'''

												return hash(_version_tuple_to_str(self.tuple   )   )




							@classmethod
							def 						__snake_case(    cls :       Tuple						,				_UpperCamelCase :       str   )       ->					Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							{f.name for f in dataclasses.fields(cls   )}
												return cls(**{k: v for k, v in dic.items() if k in field_names}   )





							def 						__snake_case(    self :       Optional[int]   )       ->					str:



												'''simple docstring'''

												return self.version_str





def 						__lowerCamelCase				(UpperCAmelCase__  :				List[str]	):
					SCREAMING_SNAKE_CASE           =							_VERSION_REG.match(UpperCAmelCase__	)
					if not res:
										raise ValueError(F"Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits."	)
					return tuple(int(UpperCAmelCase__	) for v in [res.group("major"	), res.group("minor"	), res.group("patch"	)]	)





def 						__lowerCamelCase				(UpperCAmelCase__  :				Union[str, Any]	):
					return ".".join(str(UpperCAmelCase__	) for v in version_tuple	)








"""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		(		lowercase    :    List[Any]		,				lowercase    :    Dict		,				lowercase    :    str					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : Tuple    =       AlbertConfig.from_json_file(lowercase					)
    print(F'Building PyTorch model from configuration: {config}'					)
    snake_case       : int    =       AlbertForPreTraining(lowercase					)

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

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


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(
      """--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."""
  )
  __snake_case       =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





