






'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_A			:    Tuple					  =logging.get_logger(__name__)


_A			:    int					  ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

_A			:    Any					  ={
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

_A			:    Dict					  ={
    '''allenai/longformer-base-4096''': 4_096,
    '''allenai/longformer-large-4096''': 4_096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_096,
}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 				SCREAMING_SNAKE_CASE_ ()   -> Union[str, Any]:
       lowerCamelCase__      :			Dict 						= (
           list(range(ord("""!"""   )       ,    ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   )       ,    ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   )       ,    ord("""ÿ"""   ) + 1   )   )
       )
       lowerCamelCase__      :			Union[str, Any] 						= bs[:]
       lowerCamelCase__      :			Union[str, Any] 						= 0
       for b in range(2**8   ):
              if b not in bs:
                     bs.append(lowerCAmelCase__   )
                     cs.append(2**8 + n   )
                     n += 1
       lowerCamelCase__      :			List[Any] 						= [chr(lowerCAmelCase__   ) for n in cs]
       return dict(zip(lowerCAmelCase__       ,    lowerCAmelCase__   )   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Tuple:
       lowerCamelCase__      :			Optional[Any] 						= set()
       lowerCamelCase__      :			Optional[Any] 						= word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)   )
              lowerCamelCase__      :			int 						= char
       return pairs



class _lowercase			(       __SCREAMING_SNAKE_CASE	):
       a          = VOCAB_FILES_NAMES
       a          = PRETRAINED_VOCAB_FILES_MAP
       a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a          = ['input_ids', 'attention_mask']




       def __init__(							self:       List[str]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       List[Any]="replace"    ,							UpperCamelCase__:       Optional[int]="<s>"    ,							UpperCamelCase__:       Optional[Any]="</s>"    ,							UpperCamelCase__:       List[str]="</s>"    ,							UpperCamelCase__:       List[str]="<s>"    ,							UpperCamelCase__:       int="<unk>"    ,							UpperCamelCase__:       List[Any]="<pad>"    ,							UpperCamelCase__:       Optional[int]="<mask>"    ,							UpperCamelCase__:       List[str]=False    ,							**UpperCamelCase__:       str    ,							):
              lowerCamelCase__      :			str 						= AddedToken(_a    ,							lstrip=_a    ,							rstrip=_a  ) if isinstance(_a    ,							_a  ) else bos_token
              lowerCamelCase__      :			List[Any] 						= AddedToken(_a    ,							lstrip=_a    ,							rstrip=_a  ) if isinstance(_a    ,							_a  ) else eos_token
              lowerCamelCase__      :			List[Any] 						= AddedToken(_a    ,							lstrip=_a    ,							rstrip=_a  ) if isinstance(_a    ,							_a  ) else sep_token
              lowerCamelCase__      :			int 						= AddedToken(_a    ,							lstrip=_a    ,							rstrip=_a  ) if isinstance(_a    ,							_a  ) else cls_token
              lowerCamelCase__      :			str 						= AddedToken(_a    ,							lstrip=_a    ,							rstrip=_a  ) if isinstance(_a    ,							_a  ) else unk_token
              lowerCamelCase__      :			Any 						= AddedToken(_a    ,							lstrip=_a    ,							rstrip=_a  ) if isinstance(_a    ,							_a  ) else pad_token

              # 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__(
                  errors=_a    ,							bos_token=_a    ,							eos_token=_a    ,							unk_token=_a    ,							sep_token=_a    ,							cls_token=_a    ,							pad_token=_a    ,							mask_token=_a    ,							add_prefix_space=_a    ,							**_a    ,							)

              with open(_a    ,							encoding="""utf-8"""  ) as vocab_handle:
                     lowerCamelCase__      :			Any 						= json.load(_a  )
              lowerCamelCase__      :			Union[str, Any] 						= {v: k for k, v in self.encoder.items()}
              lowerCamelCase__      :			Union[str, Any] 						= errors  # how to handle errors in decoding
              lowerCamelCase__      :			Union[str, Any] 						= bytes_to_unicode()
              lowerCamelCase__      :			Tuple 						= {v: k for k, v in self.byte_encoder.items()}
              with open(_a    ,							encoding="""utf-8"""  ) as merges_handle:
                     lowerCamelCase__      :			Optional[Any] 						= merges_handle.read().split("""\n"""  )[1:-1]
              lowerCamelCase__      :			str 						= [tuple(merge.split()  ) for merge in bpe_merges]
              lowerCamelCase__      :			Dict 						= dict(zip(_a    ,							range(len(_a  )  )  )  )
              lowerCamelCase__      :			Optional[Any] 						= {}
              lowerCamelCase__      :			Union[str, Any] 						= add_prefix_space

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              lowerCamelCase__      :			Dict 						= re.compile(R"""\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""  )




       @property
       def 	lowerCamelCase_       (							self:       Any  ):
              return len(self.encoder  )




       def 	lowerCamelCase_       (							self:       Tuple  ):
              return dict(self.encoder    ,							**self.added_tokens_encoder  )




       def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Tuple  ):
              if token in self.cache:
                     return self.cache[token]
              lowerCamelCase__      :			str 						= tuple(_a  )
              lowerCamelCase__      :			Optional[int] 						= get_pairs(_a  )

              if not pairs:
                     return token

              while True:
                     lowerCamelCase__      :			List[str] 						= min(_a    ,							key=lambda UpperCamelCase__  : self.bpe_ranks.get(_a    ,							float("""inf"""  )  )  )
                     if bigram not in self.bpe_ranks:
                            break
                     lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= bigram
                     lowerCamelCase__      :			Dict 						= []
                     lowerCamelCase__      :			Optional[int] 						= 0
                     while i < len(_a  ):
                            try:
                                   lowerCamelCase__      :			int 						= word.index(_a    ,							_a  )
                            except ValueError:
                                   new_word.extend(word[i:]  )
                                   break
                            else:
                                   new_word.extend(word[i:j]  )
                                   lowerCamelCase__      :			Tuple 						= j

                            if word[i] == first and i < len(_a  ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second  )
                                   i += 2
                            else:
                                   new_word.append(word[i]  )
                                   i += 1
                     lowerCamelCase__      :			str 						= tuple(_a  )
                     lowerCamelCase__      :			List[Any] 						= new_word
                     if len(_a  ) == 1:
                            break
                     else:
                            lowerCamelCase__      :			Optional[Any] 						= get_pairs(_a  )
              lowerCamelCase__      :			List[str] 						= """ """.join(_a  )
              lowerCamelCase__      :			Optional[int] 						= word
              return word




       def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       str  ):
              lowerCamelCase__      :			Any 						= []
              for token in re.findall(self.pat    ,							_a  ):
                     lowerCamelCase__      :			Optional[Any] 						= """""".join(
                         self.byte_encoder[b] for b in token.encode("""utf-8"""  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_a  ).split(""" """  )  )
              return bpe_tokens




       def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       List[Any]  ):
              return self.encoder.get(_a    ,							self.encoder.get(self.unk_token  )  )




       def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       str  ):
              return self.decoder.get(_a  )




       def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       List[str]  ):
              lowerCamelCase__      :			Union[str, Any] 						= """""".join(_a  )
              lowerCamelCase__      :			List[Any] 						= bytearray([self.byte_decoder[c] for c in text]  ).decode("""utf-8"""    ,							errors=self.errors  )
              return text




       def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[str] = None  ):
              if not os.path.isdir(_a  ):
                     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
                     return
              lowerCamelCase__      :			Optional[int] 						= os.path.join(
                  _a    ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
              lowerCamelCase__      :			Optional[Any] 						= os.path.join(
                  _a    ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

              with open(_a    ,							"""w"""    ,							encoding="""utf-8"""  ) as f:
                     f.write(json.dumps(self.encoder    ,							indent=2    ,							sort_keys=_a    ,							ensure_ascii=_a  ) + """\n"""  )

              lowerCamelCase__      :			List[str] 						= 0
              with open(_a    ,							"""w"""    ,							encoding="""utf-8"""  ) as writer:
                     writer.write("""#version: 0.2\n"""  )
                     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,							key=lambda UpperCamelCase__  : kv[1]  ):
                            if index != token_index:
                                   logger.warning(
                                       F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                       """ Please check that the tokenizer is not corrupted!"""  )
                                   lowerCamelCase__      :			str 						= token_index
                            writer.write(""" """.join(_a  ) + """\n"""  )
                            index += 1

              return vocab_file, merge_file




       def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       List[Any] = None  ):
              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              lowerCamelCase__      :			str 						= [self.cls_token_id]
              lowerCamelCase__      :			Optional[int] 						= [self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Union[str, Any] = None    ,							UpperCamelCase__:       Any = False  ):
              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:       List[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       int = None  ):
              lowerCamelCase__      :			str 						= [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 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Optional[int]=False    ,							**UpperCamelCase__:       Optional[Any]  ):
              lowerCamelCase__      :			str 						= kwargs.pop("""add_prefix_space"""    ,							self.add_prefix_space  )
              if (is_split_into_words or add_prefix_space) and (len(_a  ) > 0 and not text[0].isspace()):
                     lowerCamelCase__      :			Any 						= """ """ + text
              return (text, kwargs)









'''simple docstring'''







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

from ..utils.generic import ModelOutput

class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= dataset
														lowerCamelCase__      :			Optional[int] 						= process
														lowerCamelCase__      :			List[str] 						= params




							def __len__(							self:       List[str]  ):
														return len(self.dataset  )




							def __getitem__(							self:       Any    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Dict 						= self.dataset[i]
														lowerCamelCase__      :			Union[str, Any] 						= self.process(UpperCamelCase__    ,							**self.params  )
														return processed



class _lowercase			(       _lowercase	):




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

														# Internal bookkeeping
														lowerCamelCase__      :			Optional[Any] 						= None
														lowerCamelCase__      :			Optional[Any] 						= None




							def __len__(							self:       Dict  ):
														return len(self.loader  )




							def __iter__(							self:       Optional[int]  ):
														lowerCamelCase__      :			List[Any] 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Any  ):
														if isinstance(self._loader_batch_data    ,							torch.Tensor  ):
																					# Batch data is simple tensor, just fetch the slice
																					lowerCamelCase__      :			str 						= 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(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Convert ModelOutput to tuple first
																																			lowerCamelCase__      :			str 						= element.to_tuple()
																																			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__      :			str 						= 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(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			List[str] 						= 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 element is None:
																																			# This can happen for optional data that get passed around
																																			lowerCamelCase__      :			List[str] 						= 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__      :			Optional[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__      :			int 						= np.expand_dims(element[self._loader_batch_index]    ,							0  )
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			lowerCamelCase__      :			str 						= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					lowerCamelCase__      :			Optional[int] 						= self._loader_batch_data.__class__(UpperCamelCase__  )
														self._loader_batch_index += 1
														return result




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														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__      :			Optional[Any] 						= next(self.iterator  )
														lowerCamelCase__      :			List[str] 						= self.infer(UpperCamelCase__    ,							**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(UpperCamelCase__    ,							torch.Tensor  ):
																												lowerCamelCase__      :			Optional[Any] 						= processed
																					else:
																												lowerCamelCase__      :			Union[str, Any] 						= list(processed.keys()  )[0]
																												lowerCamelCase__      :			Any 						= processed[key]
																					if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																												lowerCamelCase__      :			Any 						= len(UpperCamelCase__  )
																					else:
																												lowerCamelCase__      :			List[str] 						= 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__      :			List[Any] 						= processed
																					lowerCamelCase__      :			List[Any] 						= 0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[Any]=None  ):
														super().__init__(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def __iter__(							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= iter(self.loader  )
														lowerCamelCase__      :			int 						= None
														return self




							def 	lowerCamelCase_       (							self:       str  ):
														if self.subiterator is None:
																					lowerCamelCase__      :			Optional[Any] 						= self.infer(next(self.iterator  )    ,							**self.params  )
														try:
																					# Try to return next item
																					lowerCamelCase__      :			Tuple 						= 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__      :			Any 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					lowerCamelCase__      :			Union[str, Any] 						= next(self.subiterator  )
														return processed



class _lowercase			(       _lowercase	):




							def __iter__(							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Tuple  ):
														# 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__      :			List[str] 						= False
														lowerCamelCase__      :			Union[str, Any] 						= []
														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__      :			Any 						= self.loader_batch_item()
																												lowerCamelCase__      :			Tuple 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
																												if is_last:
																																			return accumulator

														while not is_last:
																					lowerCamelCase__      :			str 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					if self.loader_batch_size is not None:
																												if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																																			lowerCamelCase__      :			Dict 						= processed
																												else:
																																			lowerCamelCase__      :			Dict 						= list(processed.keys()  )[0]
																																			lowerCamelCase__      :			Dict 						= processed[key]
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			lowerCamelCase__      :			List[Any] 						= len(UpperCamelCase__  )
																												else:
																																			lowerCamelCase__      :			Dict 						= 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__      :			str 						= observed_batch_size
																												lowerCamelCase__      :			str 						= processed
																												lowerCamelCase__      :			Optional[int] 						= 0
																												while self._loader_batch_index < self.loader_batch_size:
																																			lowerCamelCase__      :			List[Any] 						= self.loader_batch_item()
																																			lowerCamelCase__      :			str 						= item.pop("""is_last"""  )
																																			accumulator.append(UpperCamelCase__  )
																																			if is_last:
																																										return accumulator
																					else:
																												lowerCamelCase__      :			Optional[Any] 						= processed
																												lowerCamelCase__      :			Optional[int] 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
														return accumulator



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= dataset
														lowerCamelCase__      :			str 						= key




							def __len__(							self:       Optional[Any]  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Any  ):
														return self.dataset[i][self.key]



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			str 						= dataset
														lowerCamelCase__      :			Dict 						= keya
														lowerCamelCase__      :			List[str] 						= keya




							def __len__(							self:       str  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]  ):
														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

