"""simple docstring"""




# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


__lowerCamelCase							 =     re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class 			UpperCamelCase__:
						lowerCAmelCase__     :  str
						lowerCAmelCase__     :  Optional[str]  							=						None
						lowerCAmelCase__     :  Optional[Union[str, int]]  							=						None
						lowerCAmelCase__     :  Optional[Union[str, int]]  							=						None
						lowerCAmelCase__     :  Optional[Union[str, int]]  							=						None





						def  snake_case__		(				self   )    -> int:
													A__   ,     A__   ,     A__		   =							_str_to_version_tuple(self.version_str   )





						def __repr__(				self   )    -> Union[str, Any]:
													return f'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''





						@property
						def  snake_case__		(				self   )    -> Union[str, Any]:
													return self.major, self.minor, self.patch





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> List[Any]:
													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						,__UpperCAmelCase   )    -> Optional[int]:
													try:
																				A__		   =							self._validate_operand(__UpperCAmelCase   )
													except (TypeError, ValueError):
																				return False
													else:
																				return self.tuple == other.tuple





						def __lt__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							self._validate_operand(__UpperCAmelCase   )
													return self.tuple < other.tuple





						def __hash__(				self   )    -> Union[str, Any]:
													return hash(_version_tuple_to_str(self.tuple   )   )





						@classmethod
						def  snake_case__		(				cls						,__UpperCAmelCase   )    -> Dict:
													A__		   =							{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   )    -> str:
													return self.version_str






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							_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      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

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

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


if __name__ == "__main__":
				__lowerCamelCase							 =     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."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





