





import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCAmelCase_        =							logging.get_logger(__name__)



class    __lowerCAmelCase			:






			def __init__(self   ,				__magic_name__   ,				__magic_name__       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    question_encoder
							snake_case_		:       Optional[int]           =    generator
							snake_case_		:       Optional[Any]           =    self.question_encoder






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							if os.path.isfile(__magic_name__       ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''       )
							os.makedirs(__magic_name__   ,				exist_ok=__magic_name__       )
							snake_case_		:       str           =    os.path.join(__magic_name__   ,				'''question_encoder_tokenizer'''       )
							snake_case_		:       List[Any]           =    os.path.join(__magic_name__   ,				'''generator_tokenizer'''       )
							self.question_encoder.save_pretrained(__magic_name__       )
							self.generator.save_pretrained(__magic_name__       )






			@classmethod
			def      lowerCamelCase    (cls   ,				__magic_name__   ,				**__magic_name__       )	->  Any:


							'''simple docstring'''



							from ..auto.tokenization_auto import AutoTokenizer

							snake_case_		:       List[str]           =    kwargs.pop('''config'''   ,				__magic_name__       )

							if config is None:
											snake_case_		:       int           =    RagConfig.from_pretrained(__magic_name__       )

							snake_case_		:       Dict           =    AutoTokenizer.from_pretrained(
							    __magic_name__   ,				config=config.question_encoder   ,				subfolder='''question_encoder_tokenizer'''       )
							snake_case_		:       Dict           =    AutoTokenizer.from_pretrained(
							    __magic_name__   ,				config=config.generator   ,				subfolder='''generator_tokenizer'''       )
							return cls(question_encoder=__magic_name__   ,				generator=__magic_name__       )






			def __call__(self   ,				*__magic_name__   ,				**__magic_name__       )	->  Tuple:


							'''simple docstring'''



							return self.current_tokenizer(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  Dict:


							'''simple docstring'''



							return self.generator.batch_decode(*__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				*__magic_name__   ,				**__magic_name__       )	->  int:


							'''simple docstring'''



							return self.generator.decode(*__magic_name__   ,				**__magic_name__       )






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


							'''simple docstring'''



							snake_case_		:       Any           =    self.question_encoder






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.generator






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = "longest"   ,				__magic_name__ = None   ,				__magic_name__ = True   ,				**__magic_name__   ,				)	->  BatchEncoding:


							'''simple docstring'''



							warnings.warn(
							    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
							    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
							    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
							    '''details'''   ,				__magic_name__   ,				)
							if max_length is None:
											snake_case_		:       Dict           =    self.current_tokenizer.model_max_length
							snake_case_		:       List[str]           =    self(
							    __magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				max_length=__magic_name__   ,				padding=__magic_name__   ,				truncation=__magic_name__   ,				**__magic_name__   ,				)
							if tgt_texts is None:
											return model_inputs
							# Process tgt_texts
							if max_target_length is None:
											snake_case_		:       Optional[int]           =    self.current_tokenizer.model_max_length
							snake_case_		:       Union[str, Any]           =    self(
							    text_target=__magic_name__   ,				add_special_tokens=__magic_name__   ,				return_tensors=__magic_name__   ,				padding=__magic_name__   ,				max_length=__magic_name__   ,				truncation=__magic_name__   ,				**__magic_name__   ,				)
							snake_case_		:       str           =    labels['''input_ids''']
							return model_inputs


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


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


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


def    a      ( )     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= int(os.environ['''RANK''']	)
			SCREAMING_SNAKE_CASE     						= int(os.environ['''WORLD_SIZE''']	)

			SCREAMING_SNAKE_CASE     						= ArgumentParser()
			parser.add_argument('''--streaming'''		,						type=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						default=0	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= args.streaming
			SCREAMING_SNAKE_CASE     						= args.num_workers

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			SCREAMING_SNAKE_CASE     						= NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE     						= full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)	)

			SCREAMING_SNAKE_CASE     						= 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()