
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__SCREAMING_SNAKE_CASE						       =						get_logger(__name__)





class 							lowerCAmelCase_						(    enum.Enum							):




			'''simple docstring'''


			_lowercase      =							'all_checks'
			_lowercase      =							'basic_checks'
			_lowercase      =							'no_checks'





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass




def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			str       ,lowerCAmelCase_				:			Optional[int]       ,lowerCAmelCase_				:			Optional[Any]=None							)    ->   List[str]:







							"""simple docstring"""






							if expected_checksums is None:
														logger.info('Unable to verify checksums.'							)
														return
							if len(set(_A							) - set(_A							)							) > 0:
														raise ExpectedMoreDownloadedFiles(str(set(_A							) - set(_A							)							)							)
							if len(set(_A							) - set(_A							)							) > 0:
														raise UnexpectedDownloadedFile(str(set(_A							) - set(_A							)							)							)
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
							SCREAMING_SNAKE_CASE_	:							str											=' for ' + verification_name if verification_name is not None else ''
							if len(_A							) > 0:
														raise NonMatchingChecksumError(
														    F"""Checksums didn't match{for_verification_name}:\n"""
														    F"""{bad_urls}\n"""
														    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'							)
							logger.info('All the checksums matched successfully' + for_verification_name							)





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			pass




def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Union[str, Any]       ,lowerCAmelCase_				:			Optional[Any]							)    ->   Union[str, Any]:







							"""simple docstring"""






							if expected_splits is None:
														logger.info('Unable to verify splits sizes.'							)
														return
							if len(set(_A							) - set(_A							)							) > 0:
														raise ExpectedMoreSplits(str(set(_A							) - set(_A							)							)							)
							if len(set(_A							) - set(_A							)							) > 0:
														raise UnexpectedSplits(str(set(_A							) - set(_A							)							)							)
							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=[
							    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
							    for name in expected_splits
							    if expected_splits[name].num_examples != recorded_splits[name].num_examples
							]
							if len(_A							) > 0:
														raise NonMatchingSplitsSizesError(str(_A							)							)
							logger.info('All the splits matched successfully.'							)
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Dict       ,lowerCAmelCase_				:			Any = True							)    ->   str:







							"""simple docstring"""






							if record_checksum:
														SCREAMING_SNAKE_CASE_	:							List[str]											=shaaaa()
														with open(_A       ,'rb'							) as f:
																					for chunk in iter(lambda: f.read(1 << 20							)       ,B''							):
																												m.update(_A							)
																					SCREAMING_SNAKE_CASE_	:							str											=m.hexdigest()
							else:
														SCREAMING_SNAKE_CASE_	:							Dict											=None
							return {"num_bytes": os.path.getsize(_A							), "checksum": checksum}

def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			List[str]							)    ->   List[Any]:







							"""simple docstring"""






							if dataset_size and config.IN_MEMORY_MAX_SIZE:
														return dataset_size < config.IN_MEMORY_MAX_SIZE
							else:
														return False






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		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

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

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

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

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

 print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
 torch.save(_A				,					_A       )

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		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."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


