


import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     __UpperCamelCase =			torch.load(_lowercase					,  map_location='cpu' )

     __UpperCamelCase =			chkpt['model']

     # We have the base model one level deeper than the original XLM repository
     __UpperCamelCase =			{}
     for k, v in state_dict.items():
          if "pred_layer" in k:
               __UpperCamelCase =			v
          else:
               __UpperCamelCase =			v

     __UpperCamelCase =			chkpt['params']
     __UpperCamelCase =			{n: v for n, v in config.items() if not isinstance(_lowercase					,  (torch.FloatTensor, numpy.ndarray) )}

     __UpperCamelCase =			chkpt['dico_word2id']
     __UpperCamelCase =			{s + '</w>' if s.find('@@' ) == -1 and i > 13 else s.replace('@@'					,  '' ): i for s, i in vocab.items()}

     # Save pytorch-model
     __UpperCamelCase =			pytorch_dump_folder_path + '/' + WEIGHTS_NAME
     __UpperCamelCase =			pytorch_dump_folder_path + '/' + CONFIG_NAME
     __UpperCamelCase =			pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

     print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
     torch.save(_lowercase					,  _lowercase )

     print(f'''Save configuration file to {pytorch_config_dump_path}''' )
     with open(_lowercase					,  'w'					,  encoding='utf-8' ) as f:
          f.write(json.dumps(_lowercase					,  indent=2 ) + '\n' )

     print(f'''Save vocab file to {pytorch_config_dump_path}''' )
     with open(_lowercase					,  'w'					,  encoding='utf-8' ) as f:
          f.write(json.dumps(_lowercase					,  indent=2 ) + '\n' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __snake_case   							=						parser.parse_args()
    convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''





# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  			=				re.compile(r'''([A-Z]+)([A-Z][a-z])''')
UpperCAmelCase__  			=				re.compile(r'''([a-z\d])([A-Z])''')

UpperCAmelCase__  			=				re.compile(r'''(?<!_)_(?!_)''')
UpperCAmelCase__  			=				re.compile(r'''(_{2,})''')

UpperCAmelCase__  			=				r'''^\w+(\.\w+)*$'''

UpperCAmelCase__  			=				r'''<>:/\|?*'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A=	_uppercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			__A=	_lowercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			return name.lower()






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	_single_underscore_re.split(_SCREAMING_SNAKE_CASE )
			__A=	[_multiple_underscores_re.split(_SCREAMING_SNAKE_CASE ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) if n != '' )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			return camelcase_to_snakecase(_SCREAMING_SNAKE_CASE )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			if not re.match(_split_re,_SCREAMING_SNAKE_CASE ):
						raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
			return f"""{filename_prefix_for_name(_SCREAMING_SNAKE_CASE )}-{split}"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Union[str, Any]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			if filetype_suffix:
						prefix += f""".{filetype_suffix}"""
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			return f"""{filepath}*"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[Any]=None,_SCREAMING_SNAKE_CASE					: List[str]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			if shard_lengths:
						__A=	len(_SCREAMING_SNAKE_CASE )
						__A=	[f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(_SCREAMING_SNAKE_CASE )]
						if filetype_suffix:
									__A=	[filename + f""".{filetype_suffix}""" for filename in filenames]
						return filenames
			else:
						__A=	prefix
						if filetype_suffix:
									filename += f""".{filetype_suffix}"""
						return [filename]



