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

import regex as re

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


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

__UpperCamelCase	: List[Any]    		=  logging.get_logger(__name__)


__UpperCamelCase	: List[str]    		=  {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__UpperCamelCase	: Optional[int]    		=  {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

__UpperCamelCase	: str    		=  {'facebook/blenderbot-3B': 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(
						    list(range(ord('''!'''		)	,						ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)	,						ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)	,						ord('''ÿ'''		) + 1		)		)
						)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							bs[:]
						SCREAMING_SNAKE_CASE							:					int           =							0
						for b in range(2**8		):
												if b not in bs:
																		bs.append(_lowercase		)
																		cs.append(2**8 + n		)
																		n += 1
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[chr(_lowercase		) for n in cs]
						return dict(zip(_lowercase	,						_lowercase		)		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							set()
						SCREAMING_SNAKE_CASE							:					int           =							word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												SCREAMING_SNAKE_CASE							:					int           =							char
						return pairs





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_  =	["""input_ids""", """attention_mask"""]


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Union[str, Any]="replace"					,	UpperCamelCase__			:       Any="<s>"					,	UpperCamelCase__			:       Optional[int]="</s>"					,	UpperCamelCase__			:       int="</s>"					,	UpperCamelCase__			:       Any="<s>"					,	UpperCamelCase__			:       str="<unk>"					,	UpperCamelCase__			:       str="<pad>"					,	UpperCamelCase__			:       int="<mask>"					,	UpperCamelCase__			:       List[str]=False					,	**UpperCamelCase__			:       Union[str, Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else bos_token
												SCREAMING_SNAKE_CASE							:					Dict           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else eos_token
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else sep_token
												SCREAMING_SNAKE_CASE							:					str           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else cls_token
												SCREAMING_SNAKE_CASE							:					Any           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else unk_token
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else pad_token

												# Mask token behave like a normal word, i.e. include the space before it
												SCREAMING_SNAKE_CASE							:					Tuple           =							AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__		) if isinstance(UpperCamelCase__					,	UpperCamelCase__		) else mask_token

												super().__init__(
												    errors=UpperCamelCase__					,	bos_token=UpperCamelCase__					,	eos_token=UpperCamelCase__					,	unk_token=UpperCamelCase__					,	sep_token=UpperCamelCase__					,	cls_token=UpperCamelCase__					,	pad_token=UpperCamelCase__					,	mask_token=UpperCamelCase__					,	add_prefix_space=UpperCamelCase__					,	**UpperCamelCase__					,	)

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

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


						@property
						# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return len(self.encoder		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return dict(self.encoder					,	**self.added_tokens_encoder		)


						def  __A ( self			:       int					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												if token in self.cache:
																		return self.cache[token]
												SCREAMING_SNAKE_CASE							:					str           =							tuple(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_pairs(UpperCamelCase__		)

												if not pairs:
																		return token

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

																								if word[i] == first and i < len(UpperCamelCase__		) - 1 and word[i + 1] == second:
																														new_word.append(first + second		)
																														i += 2
																								else:
																														new_word.append(word[i]		)
																														i += 1
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							tuple(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Tuple           =							new_word
																		if len(UpperCamelCase__		) == 1:
																								break
																		else:
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							get_pairs(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							''' '''.join(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							word
												return word


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							[]
												for token in re.findall(self.pat					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					Dict           =							''''''.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(UpperCamelCase__		).split(''' '''		)		)
												return bpe_tokens


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												return self.encoder.get(UpperCamelCase__					,	self.encoder.get(self.unk_token		)		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												return self.decoder.get(UpperCamelCase__		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							''''''.join(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''					,	errors=self.errors		)
												return text


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





												if not os.path.isdir(UpperCamelCase__		):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
																		return
												SCREAMING_SNAKE_CASE							:					Dict           =							os.path.join(
												    UpperCamelCase__					,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							os.path.join(
												    UpperCamelCase__					,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

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

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							0
												with open(UpperCamelCase__					,	'''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!'''		)
																														SCREAMING_SNAKE_CASE							:					List[str]           =							token_index
																								writer.write(''' '''.join(UpperCamelCase__		) + '''\n'''		)
																								index += 1

												return vocab_file, merge_file


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None					,	UpperCamelCase__			:       bool = False		):





												'''simple docstring'''





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

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


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[self.sep_token_id]
												SCREAMING_SNAKE_CASE							:					Dict           =							[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  __A ( self			:       Dict					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       int=False					,	**UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							kwargs.pop('''add_prefix_space'''					,	self.add_prefix_space		)
												if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__		) > 0 and not text[0].isspace()):
																		SCREAMING_SNAKE_CASE							:					str           =							''' ''' + text
												return (text, kwargs)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												return token_ids_a + [self.eos_token_id]



						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       "Conversation"		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							[]
												for is_user, text in conversation.iter_texts():
																		if is_user:
																								# We need to space prefix as it's being done within blenderbot
																								inputs.append(''' ''' + text		)
																		else:
																								# Generated responses should contain them already.
																								inputs.append(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							'''  '''.join(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							self.encode(UpperCamelCase__		)
												if len(UpperCamelCase__		) > self.model_max_length:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							input_ids[-self.model_max_length :]
																		logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""		)
												return input_ids






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

from ..utils.generic import ModelOutput





class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							process
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							params


						def __len__( self			:       Tuple		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       List[str]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.dataset[i]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.process(UpperCamelCase__					,	**self.params		)
												return processed






class       lowercase__		(     UpperCamelCase_):


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							loader
												SCREAMING_SNAKE_CASE							:					List[Any]           =							infer
												SCREAMING_SNAKE_CASE							:					int           =							params
												if loader_batch_size == 1:
																		# Let's spare some time by deactivating altogether
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					List[str]           =							loader_batch_size

												# Internal bookkeeping
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					int           =							None


						def __len__( self			:       int		):





												'''simple docstring'''





												return len(self.loader		)


						def __iter__( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							iter(self.loader		)
												return self


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if isinstance(self._loader_batch_data					,	torch.Tensor		):
																		# Batch data is simple tensor, just fetch the slice
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._loader_batch_data[self._loader_batch_index]
												else:
																		# Batch data is assumed to be BaseModelOutput (or dict)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{}
																		for k, element in self._loader_batch_data.items():
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Convert ModelOutput to tuple first
																														SCREAMING_SNAKE_CASE							:					Dict           =							element.to_tuple()
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Dict           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					List[str]           =							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
																														SCREAMING_SNAKE_CASE							:					int           =							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

																														SCREAMING_SNAKE_CASE							:					Union[str, 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
																														SCREAMING_SNAKE_CASE							:					Tuple           =							np.expand_dims(element[self._loader_batch_index]					,	0		)
																								else:
																														# This is typically a list, so no need to `unsqueeze`.
																														SCREAMING_SNAKE_CASE							:					Tuple           =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																		SCREAMING_SNAKE_CASE							:					Any           =							self._loader_batch_data.__class__(UpperCamelCase__		)
												self._loader_batch_index += 1
												return result



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												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
												SCREAMING_SNAKE_CASE							:					Tuple           =							next(self.iterator		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							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		):
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							processed
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							list(processed.keys()		)[0]
																								SCREAMING_SNAKE_CASE							:					int           =							processed[key]
																		if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																		else:
																								SCREAMING_SNAKE_CASE							:					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.
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							observed_batch_size
																		# Setting internal index to unwrap the batch
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																		SCREAMING_SNAKE_CASE							:					int           =							0
																		return self.loader_batch_item()
												else:
																		# We're not unrolling batches
																		return processed






class       lowercase__		(     UpperCamelCase_):


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





												'''simple docstring'''





												super().__init__(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


						def __iter__( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							iter(self.loader		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							None
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





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






class       lowercase__		(     UpperCamelCase_):


						def __iter__( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							iter(self.loader		)
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							False
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]
												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:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							self.loader_batch_item()
																								SCREAMING_SNAKE_CASE							:					Any           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
																								if is_last:
																														return accumulator

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






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Dict           =							key


						def __len__( self			:       Optional[int]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Dict					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return self.dataset[i][self.key]






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							dataset
												SCREAMING_SNAKE_CASE							:					List[str]           =							keya
												SCREAMING_SNAKE_CASE							:					Tuple           =							keya


						def __len__( self			:       List[str]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





