




'''simple docstring'''





import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType




class 			_snake_case		(     lowerCamelCase__							):




   def __init__( self    ,							a__							)    ->    Any:

          '''simple docstring'''




          snake_case_      =				data




   def __iter__( self							)    ->    Any:

          '''simple docstring'''




          for element in self.data:
                 yield element


def 					UpperCamelCase_(     snake_case							:	List[str]=True					):

       '''simple docstring'''






       snake_case_      =				Accelerator(even_batches=_A					)
       assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
       return accelerator


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	int							,    snake_case							:	List[Any]							,    snake_case							:	Union[str, Any] = False					):

       '''simple docstring'''






       if iterable:
              snake_case_      =				DummyIterableDataset(torch.as_tensor(range(_A					)					)					)
       else:
              snake_case_      =				TensorDataset(torch.as_tensor(range(_A					)					)					)

       snake_case_      =				DataLoader(_A							,    batch_size=_A					)
       snake_case_      =				accelerator.prepare(_A					)

       return dl


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	Dict							,    snake_case							:	Tuple							,    snake_case							:	Any							,    snake_case							:	Any							,    ):

       '''simple docstring'''






       snake_case_      =				create_dataloader(accelerator=_A							,    dataset_size=_A							,    batch_size=_A					)

       snake_case_      =				[len(batch[0]					) for batch in dl]

       if accelerator.process_index == 0:
              assert batch_sizes == process_0_expected_batch_sizes
       elif accelerator.process_index == 1:
              assert batch_sizes == process_1_expected_batch_sizes


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				create_accelerator()

       # without padding, we would expect a different number of batches
       verify_dataloader_batch_sizes(
           _A							,    dataset_size=3							,    batch_size=1							,    process_0_expected_batch_sizes=[1, 1]							,    process_1_expected_batch_sizes=[1, 1]							,    )

       # without padding, we would expect the same number of batches, but different sizes
       verify_dataloader_batch_sizes(
           _A							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 2]							,    )


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				create_accelerator(even_batches=_A					)

       verify_dataloader_batch_sizes(
           _A							,    dataset_size=3							,    batch_size=1							,    process_0_expected_batch_sizes=[1, 1]							,    process_1_expected_batch_sizes=[1]							,    )

       verify_dataloader_batch_sizes(
           _A							,    dataset_size=7							,    batch_size=2							,    process_0_expected_batch_sizes=[2, 2]							,    process_1_expected_batch_sizes=[2, 1]							,    )


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				create_accelerator(even_batches=_A					)

       snake_case_      =				torch.nn.Linear(1							,    1					)
       snake_case_      =				accelerator.prepare(_A					)

       snake_case_      =				create_dataloader(_A							,    dataset_size=3							,    batch_size=1					)

       snake_case_      =				[]
       with accelerator.join_uneven_inputs([ddp_model]					):
              for batch_idx, batch in enumerate(_A					):
                     snake_case_      =				ddp_model(batch[0].float()					)
                     snake_case_      =				output.sum()
                     loss.backward()
                     batch_idxs.append(_A					)

       accelerator.wait_for_everyone()

       if accelerator.process_index == 0:
              assert batch_idxs == [0, 1]
       elif accelerator.process_index == 1:
              assert batch_idxs == [0]


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]					):

       '''simple docstring'''






       with warnings.catch_warnings(record=_A					) as w:
              with accelerator.join_uneven_inputs([Mock()]					):
                     pass

              assert issubclass(w[-1].category							,    _A					)
              assert "only supported for multi-GPU" in str(w[-1].message					)


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				True
       snake_case_      =				False
       snake_case_      =				create_accelerator(even_batches=_A					)
       snake_case_      =				torch.nn.Linear(1							,    1					)
       snake_case_      =				accelerator.prepare(_A					)
       snake_case_      =				create_dataloader(_A							,    dataset_size=3							,    batch_size=1					)
       snake_case_      =				create_dataloader(_A							,    dataset_size=3							,    batch_size=1					)

       with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=_A					):
              snake_case_      =				train_dl.batch_sampler.even_batches
              snake_case_      =				valid_dl.batch_sampler.even_batches

       assert train_dl_overridden_value == overridden_even_batches
       assert valid_dl_overridden_value == overridden_even_batches
       assert train_dl.batch_sampler.even_batches == default_even_batches
       assert valid_dl.batch_sampler.even_batches == default_even_batches


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				True
       snake_case_      =				False
       snake_case_      =				create_accelerator(even_batches=_A					)
       snake_case_      =				torch.nn.Linear(1							,    1					)
       snake_case_      =				accelerator.prepare(_A					)
       create_dataloader(_A							,    dataset_size=3							,    batch_size=1							,    iterable=_A					)
       snake_case_      =				create_dataloader(_A							,    dataset_size=3							,    batch_size=1					)

       with warnings.catch_warnings():
              warnings.filterwarnings("ignore"					)
              try:
                     with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=_A					):
                            snake_case_      =				batch_dl.batch_sampler.even_batches
              except AttributeError:
                     # ensure attribute error is not raised when processing iterable dl
                     raise AssertionError

       assert batch_dl_overridden_value == overridden_even_batches
       assert batch_dl.batch_sampler.even_batches == default_even_batches


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				create_accelerator()
       snake_case_      =				torch.nn.Linear(1							,    1					)
       snake_case_      =				accelerator.prepare(_A					)
       create_dataloader(_A							,    dataset_size=3							,    batch_size=1							,    iterable=_A					)

       with warnings.catch_warnings(record=_A					) as w:
              with accelerator.join_uneven_inputs([ddp_model]							,    even_batches=_A					):
                     pass

              assert issubclass(w[-1].category							,    _A					)
              assert "only supported for map-style datasets" in str(w[-1].message					)


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				create_accelerator()

       accelerator.print("Test that even_batches variable ensures uniform batches across processes"					)
       test_default_ensures_even_batch_sizes()

       accelerator.print("Run tests with even_batches disabled"					)
       test_can_disable_even_batches()

       accelerator.print("Test joining uneven inputs"					)
       test_can_join_uneven_inputs()

       accelerator.print("Test overriding even_batches when joining uneven inputs"					)
       test_join_can_override_even_batches()

       accelerator.print("Test overriding even_batches for mixed dataloader types"					)
       test_join_can_override_for_mixed_type_dataloaders()

       accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"					)
       test_join_raises_warning_for_iterable_when_overriding_even_batches()

       accelerator.print("Test join with non DDP distributed raises warning"					)
       snake_case_      =				accelerator.state.distributed_type
       snake_case_      =				DistributedType.FSDP
       test_join_raises_warning_for_non_ddp_distributed(_A					)
       snake_case_      =				original_state


if __name__ == "__main__":
  main()










'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{"vocab_file": "sentencepiece.bpe.model"}

_SCREAMING_SNAKE_CASE							:       Tuple						=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_SCREAMING_SNAKE_CASE							:       Any						=			"▁"




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				List[Any]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				Tuple							=   ["input_ids", "attention_mask"]




			def __init__( self    ,							a__    ,							a__="<s>"    ,							a__="</s>"    ,							a__="</s>"    ,							a__="<s>"    ,							a__="<unk>"    ,							a__="<pad>"    ,							a__="<mask>"    ,							a__ = None    ,							**a__    ,							)    ->    None:

										'''simple docstring'''




										snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else mask_token

										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=a__    ,							eos_token=a__    ,							unk_token=a__    ,							sep_token=a__    ,							cls_token=a__    ,							pad_token=a__    ,							mask_token=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				vocab_file
										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(str(a__							)							)

										snake_case_      =				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										snake_case_      =				len(self.sp_model							) - 1
										snake_case_      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										snake_case_      =				[self.cls_token_id]
										snake_case_      =				[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

										'''simple docstring'''




										if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=a__    ,							token_ids_a=a__    ,							already_has_special_tokens=a__							)

										if token_ids_a is None:
																	return [1] + ([0] * len(a__							)) + [1]
										return [1] + ([0] * len(a__							)) + [1, 1] + ([0] * len(a__							)) + [1]




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										snake_case_      =				[self.sep_token_id]
										snake_case_      =				[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										return len(self.sp_model							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				{self.convert_ids_to_tokens(a__							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




										return self.sp_model.encode(a__    ,							out_type=a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

										'''simple docstring'''




										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										snake_case_      =				self.sp_model.PieceToId(a__							)

										return spm_id if spm_id else self.unk_token_id




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				[]
										snake_case_      =				""
										snake_case_      =				False
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								if not prev_is_special:
																															out_string += " "
																								out_string += self.sp_model.decode(a__							) + token
																								snake_case_      =				True
																								snake_case_      =				[]
																	else:
																								current_sub_tokens.append(a__							)
																								snake_case_      =				False
										out_string += self.sp_model.decode(a__							)
										return out_string.strip()




			def __getstate__( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.__dict__.copy()
										snake_case_      =				None
										return state




			def __setstate__( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				d

										# for backward compatibility
										if not hasattr(self    ,							"sp_model_kwargs"							):
																	snake_case_      =				{}

										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

										'''simple docstring'''




										if not os.path.isdir(a__							):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																	return
										snake_case_      =				os.path.join(
										    a__    ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(a__							) and os.path.isfile(self.vocab_file							):
																	copyfile(self.vocab_file    ,							a__							)
										elif not os.path.isfile(self.vocab_file							):
																	with open(a__    ,							"wb"							) as fi:
																								snake_case_      =				self.sp_model.serialized_model_proto()
																								fi.write(a__							)

										return (out_vocab_file,)




