"""simple docstring"""





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    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	List[Any]      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		str		   =     torch.load(_lowercase						,   map_location="cpu"      )

							__lowerCamelCase					:		Union[str, Any]		   =     chkpt['model']

							# We have the base model one level deeper than the original XLM repository
							__lowerCamelCase					:		Union[str, Any]		   =     {}
							for k, v in state_dict.items():
														if "pred_layer" in k:
																					__lowerCamelCase					:		int		   =     v
														else:
																					__lowerCamelCase					:		Tuple		   =     v

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

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

							# Save pytorch-model
							__lowerCamelCase					:		int		   =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							__lowerCamelCase					:		Union[str, Any]		   =     pytorch_dump_folder_path + '/' + CONFIG_NAME
							__lowerCamelCase					:		str		   =     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__":
				__A  =				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.'''
				)
				__A  =				parser.parse_args()
				convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__A  =				4
__A  =				3
class  _snake_case     (					a__		):
						pass


def    lowercase_   (   _lowerCamelCase:	List[str]      ) ->      List[str]:



							'''simple docstring'''

							for shard in shards:
														for i in range(_lowerCamelCase      ):
																					yield {"i": i, "shard": shard}


def    lowercase_   (   ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     int(os.environ["RANK"]      )
							__lowerCamelCase					:		Optional[int]		   =     int(os.environ["WORLD_SIZE"]      )

							__lowerCamelCase					:		Any		   =     ArgumentParser()
							parser.add_argument("--streaming"						,   type=_lowerCamelCase      )
							parser.add_argument("--local_rank"						,   type=_lowerCamelCase      )
							parser.add_argument("--num_workers"						,   type=_lowerCamelCase						,   default=0      )
							__lowerCamelCase					:		Dict		   =     parser.parse_args()
							__lowerCamelCase					:		str		   =     args.streaming
							__lowerCamelCase					:		List[Any]		   =     args.num_workers

							__lowerCamelCase					:		Optional[Any]		   =     {"shards": [F"""shard_{shard_idx}""" for shard_idx in range(_lowerCamelCase      )]}
							__lowerCamelCase					:		int		   =     IterableDataset.from_generator(_lowerCamelCase						,   gen_kwargs=_lowerCamelCase      )
							if not streaming:
														__lowerCamelCase					:		Optional[int]		   =     Dataset.from_list(list(_lowerCamelCase      )      )

							__lowerCamelCase					:		Union[str, Any]		   =     split_dataset_by_node(_lowerCamelCase						,   rank=_lowerCamelCase						,   world_size=_lowerCamelCase      )
							__lowerCamelCase					:		Optional[Any]		   =     torch.utils.data.DataLoader(_lowerCamelCase						,   num_workers=_lowerCamelCase      )

							__lowerCamelCase					:		List[Any]		   =     NUM_SHARDS * NUM_ITEMS_PER_SHARD
							__lowerCamelCase					:		Optional[Any]		   =     full_size // world_size
							expected_local_size += int(rank < (full_size % world_size)      )

							__lowerCamelCase					:		Optional[Any]		   =     sum(1 for _ in dataloader      )
							if local_size != expected_local_size:
														raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""      )


if __name__ == "__main__":
				main()