




"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class __lowercase(      _snake_case				):




     '''simple docstring'''


     def __init__(    self						,	__a						,	__a						,	__a     ):
        __lowerCamelCase					:	Optional[int]	       =				dataset
        __lowerCamelCase					:	str	       =				process
        __lowerCamelCase					:	int	       =				params
     def __len__(    self     ):
        return len(self.dataset     )





     def __getitem__(    self						,	__a     ):
        __lowerCamelCase					:	Union[str, Any]	       =				self.dataset[i]
        __lowerCamelCase					:	Optional[Any]	       =				self.process(snake_case_						,	**self.params     )
        return processed



class __lowercase(      _snake_case				):




     '''simple docstring'''


     def __init__(    self						,	__a						,	__a						,	__a						,	__a=None     ):
        __lowerCamelCase					:	Union[str, Any]	       =				loader
        __lowerCamelCase					:	Tuple	       =				infer
        __lowerCamelCase					:	List[Any]	       =				params
        if loader_batch_size == 1:
           # Let's spare some time by deactivating altogether
           __lowerCamelCase					:	int	       =				None
        __lowerCamelCase					:	int	       =				loader_batch_size

        # Internal bookkeeping
        __lowerCamelCase					:	Any	       =				None
        __lowerCamelCase					:	Dict	       =				None
     def __len__(    self     ):
        return len(self.loader     )
     def __iter__(    self     ):
        __lowerCamelCase					:	int	       =				iter(self.loader     )
        return self
     def     snake_case_				(    self     ):
        if isinstance(self._loader_batch_data						,	torch.Tensor     ):
           # Batch data is simple tensor, just fetch the slice
           __lowerCamelCase					:	List[Any]	       =				self._loader_batch_data[self._loader_batch_index]
        else:
           # Batch data is assumed to be BaseModelOutput (or dict)
           __lowerCamelCase					:	int	       =				{}
           for k, element in self._loader_batch_data.items():
              if isinstance(snake_case_						,	snake_case_     ):
                 # Convert ModelOutput to tuple first
                 __lowerCamelCase					:	Tuple	       =				element.to_tuple()
                 if isinstance(element[0]						,	torch.Tensor     ):
                    __lowerCamelCase					:	Union[str, Any]	       =				tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
                 elif isinstance(element[0]						,	np.ndarray     ):
                    __lowerCamelCase					:	int	       =				tuple(np.expand_dims(el[self._loader_batch_index]						,	0     ) for el in element     )
                 continue
              if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(snake_case_						,	snake_case_     ):
                 # Those are stored as lists of tensors so need specific unbatching.
                 if isinstance(element[0]						,	torch.Tensor     ):
                    __lowerCamelCase					:	Tuple	       =				tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
                 elif isinstance(element[0]						,	np.ndarray     ):
                    __lowerCamelCase					:	Tuple	       =				tuple(np.expand_dims(el[self._loader_batch_index]						,	0     ) for el in element     )
                 continue
              if element is None:
                 # This can happen for optional data that get passed around
                 __lowerCamelCase					:	Tuple	       =				None
              elif isinstance(element[self._loader_batch_index]						,	torch.Tensor     ):
                 # Take correct batch data, but make it looked like batch_size=1
                 # For compatibility with other methods within transformers

                 __lowerCamelCase					:	List[Any]	       =				element[self._loader_batch_index].unsqueeze(0     )
              elif isinstance(element[self._loader_batch_index]						,	np.ndarray     ):
                 # Take correct batch data, but make it looked like batch_size=1
                 # For compatibility with other methods within transformers
                 __lowerCamelCase					:	Union[str, Any]	       =				np.expand_dims(element[self._loader_batch_index]						,	0     )
              else:
                 # This is typically a list, so no need to `unsqueeze`.
                 __lowerCamelCase					:	List[Any]	       =				element[self._loader_batch_index]
                 # Recreate the element by reusing the original class to make it look
                 # batch_size=1
           __lowerCamelCase					:	int	       =				self._loader_batch_data.__class__(snake_case_     )
        self._loader_batch_index += 1
        return result





     def     snake_case_				(    self     ):
        if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
           # We are currently unrolling a batch so we just need to return
           # the current item within a batch
           return self.loader_batch_item()

        # We're out of items within a batch
        __lowerCamelCase					:	Tuple	       =				next(self.iterator     )
        __lowerCamelCase					:	Any	       =				self.infer(snake_case_						,	**self.params     )
        # We now have a batch of "inferred things".
        if self.loader_batch_size is not None:
           # Try to infer the size of the batch
           if isinstance(snake_case_						,	torch.Tensor     ):
              __lowerCamelCase					:	Union[str, Any]	       =				processed
           else:
              __lowerCamelCase					:	Optional[int]	       =				list(processed.keys()     )[0]
              __lowerCamelCase					:	List[str]	       =				processed[key]
           if isinstance(snake_case_						,	snake_case_     ):
              __lowerCamelCase					:	Dict	       =				len(snake_case_     )
           else:
              __lowerCamelCase					:	Optional[int]	       =				first_tensor.shape[0]
           if 0 < observed_batch_size < self.loader_batch_size:
              # could be last batch so we can't unroll as many
              # elements.
              __lowerCamelCase					:	Union[str, Any]	       =				observed_batch_size
           # Setting internal index to unwrap the batch
           __lowerCamelCase					:	str	       =				processed
           __lowerCamelCase					:	List[Any]	       =				0
           return self.loader_batch_item()
        else:
           # We're not unrolling batches
           return processed



class __lowercase(      _snake_case				):




     '''simple docstring'''


     def __init__(    self						,	__a						,	__a						,	__a						,	__a=None     ):
        super().__init__(snake_case_						,	snake_case_						,	snake_case_     )
     def __iter__(    self     ):
        __lowerCamelCase					:	Tuple	       =				iter(self.loader     )
        __lowerCamelCase					:	List[Any]	       =				None
        return self





     def     snake_case_				(    self     ):
        if self.subiterator is None:
           __lowerCamelCase					:	Optional[Any]	       =				self.infer(next(self.iterator     )						,	**self.params     )
        try:
           # Try to return next item
           __lowerCamelCase					:	Union[str, Any]	       =				next(self.subiterator     )
        except StopIteration:
           # When a preprocess iterator ends, we can start lookig at the next item
           # ChunkIterator will keep feeding until ALL elements of iterator
           # all have created their subiterator and have been iterating against.
           #
           # Another way to look at it, is we're basically flattening lists of lists
           # into a single list, but with generators
           __lowerCamelCase					:	str	       =				self.infer(next(self.iterator     )						,	**self.params     )
           __lowerCamelCase					:	Tuple	       =				next(self.subiterator     )
        return processed



class __lowercase(      _snake_case				):




     '''simple docstring'''


     def __iter__(    self     ):
        __lowerCamelCase					:	Optional[Any]	       =				iter(self.loader     )
        return self





     def     snake_case_				(    self     ):
        # Extremely similar to PipelineIterator in its unpacking mechanism
        # BUT, we have an extra required item which is the presence of `is_last`
        # That is because everything is flattened by `PipelineChunkIterator` we
        # need to keep track of how to regroup here in the original `process`
        # boundaries so that `process` and `postprocess` see the same data.

        # This iterator accumulates items (possibly while unbatching) until it
        # its a `is_last` and then just passes it on to the caller.
        __lowerCamelCase					:	Optional[Any]	       =				False
        __lowerCamelCase					:	Tuple	       =				[]
        if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
           while self._loader_batch_index < self.loader_batch_size:
              __lowerCamelCase					:	Union[str, Any]	       =				self.loader_batch_item()
              __lowerCamelCase					:	str	       =				item.pop('is_last'     )
              accumulator.append(snake_case_     )
              if is_last:
                 return accumulator

        while not is_last:
           __lowerCamelCase					:	List[str]	       =				self.infer(next(self.iterator     )						,	**self.params     )
           if self.loader_batch_size is not None:
              if isinstance(snake_case_						,	torch.Tensor     ):
                 __lowerCamelCase					:	Union[str, Any]	       =				processed
              else:
                 __lowerCamelCase					:	Tuple	       =				list(processed.keys()     )[0]
                 __lowerCamelCase					:	Tuple	       =				processed[key]
              if isinstance(snake_case_						,	snake_case_     ):
                 __lowerCamelCase					:	Any	       =				len(snake_case_     )
              else:
                 __lowerCamelCase					:	List[Any]	       =				first_tensor.shape[0]
              if 0 < observed_batch_size < self.loader_batch_size:
                 # could be last batch so we can't unroll as many
                 # elements.
                 __lowerCamelCase					:	Dict	       =				observed_batch_size
              __lowerCamelCase					:	List[Any]	       =				processed
              __lowerCamelCase					:	List[str]	       =				0
              while self._loader_batch_index < self.loader_batch_size:
                 __lowerCamelCase					:	Union[str, Any]	       =				self.loader_batch_item()
                 __lowerCamelCase					:	int	       =				item.pop('is_last'     )
                 accumulator.append(snake_case_     )
                 if is_last:
                    return accumulator
           else:
              __lowerCamelCase					:	Dict	       =				processed
              __lowerCamelCase					:	Dict	       =				item.pop('is_last'     )
              accumulator.append(snake_case_     )
        return accumulator



class __lowercase(      _snake_case				):




     '''simple docstring'''


     def __init__(    self						,	__a						,	__a     ):
        __lowerCamelCase					:	str	       =				dataset
        __lowerCamelCase					:	Any	       =				key
     def __len__(    self     ):
        return len(self.dataset     )





     def __getitem__(    self						,	__a     ):
        return self.dataset[i][self.key]



class __lowercase(      _snake_case				):




     '''simple docstring'''


     def __init__(    self						,	__a						,	__a						,	__a     ):
        __lowerCamelCase					:	int	       =				dataset
        __lowerCamelCase					:	Any	       =				keya
        __lowerCamelCase					:	int	       =				keya
     def __len__(    self     ):
        return len(self.dataset     )





     def __getitem__(    self						,	__a     ):
        return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}









"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_barthez import BarthezTokenizer
else:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

a_				:    List[Any]										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_				:    int										=     {
    '''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'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Union[str, Any]     		= ['input_ids', 'attention_mask']
					__a						:						Optional[Any]     		= BarthezTokenizer
					def __init__(    self						,	__a=None						,	__a=None						,	__a="<s>"						,	__a="</s>"						,	__a="</s>"						,	__a="<s>"						,	__a="<unk>"						,	__a="<pad>"						,	__a="<mask>"						,	**__a						,	):
								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase					:	Optional[Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else mask_token

								super().__init__(
								    __a						,	tokenizer_file=__a						,	bos_token=__a						,	eos_token=__a						,	unk_token=__a						,	sep_token=__a						,	cls_token=__a						,	pad_token=__a						,	mask_token=__a						,	**__a						,	)

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	int	       =				[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]





					def     snake_case_				(    self						,	__a						,	__a = None     ):
								if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'     )

								if not os.path.isdir(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[Any]	       =				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     ):
											copyfile(self.vocab_file						,	__a     )

								return (out_vocab_file,)



