






'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       RobertaTokenizer
		_UpperCamelCase						=       RobertaTokenizerFast
		_UpperCamelCase						=       True
		_UpperCamelCase						=       {'cls_token': '<s>'}






		def       UpperCamelCase_       (   self							):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase__								=	[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								lowerCamelCase__								=	dict(zip(_lowerCAmelCase ,range(len(_lowerCAmelCase							)							)							)							)
								lowerCamelCase__								=	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								lowerCamelCase__								=	{"""unk_token""": """<unk>"""}

								lowerCamelCase__								=	os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase__								=	os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""]							)
								with open(self.vocab_file ,"""w""" ,encoding="""utf-8"""							) as fp:
														fp.write(json.dumps(_lowerCAmelCase							) + """\n"""							)
								with open(self.merges_file ,"""w""" ,encoding="""utf-8"""							) as fp:
														fp.write("""\n""".join(_lowerCAmelCase							)							)






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								kwargs.update(self.special_tokens_map							)
								return self.tokenizer_class.from_pretrained(self.tmpdirname ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								kwargs.update(self.special_tokens_map							)
								return RobertaTokenizerFast.from_pretrained(self.tmpdirname ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	"""lower newer"""
								lowerCamelCase__								=	"""lower newer"""
								return input_text, output_text






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.tokenizer_class(self.vocab_file ,self.merges_file ,**self.special_tokens_map							)
								lowerCamelCase__								=	"""lower newer"""
								lowerCamelCase__								=	["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								lowerCamelCase__								=	tokenizer.tokenize(_lowerCAmelCase							)  # , add_prefix_space=True)
								self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	tokens + [tokenizer.unk_token]
								lowerCamelCase__								=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase							) ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_tokenizer()

								self.assertListEqual(tokenizer.encode("""Hello world!""" ,add_special_tokens=_lowerCAmelCase							) ,[0, 3_14_14, 2_32, 3_28, 2]							)
								self.assertListEqual(
								    tokenizer.encode("""Hello world! cécé herlolip 418""" ,add_special_tokens=_lowerCAmelCase							) ,[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] ,)






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.tokenizer_class.from_pretrained("""roberta-base"""							)

								lowerCamelCase__								=	tokenizer.encode("""sequence builders""" ,add_special_tokens=_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.encode("""multi-sequence build""" ,add_special_tokens=_lowerCAmelCase							)

								lowerCamelCase__								=	tokenizer.encode(
								    """sequence builders""" ,add_special_tokens=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.encode(
								    """sequence builders""" ,"""multi-sequence build""" ,add_special_tokens=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase							)

								lowerCamelCase__								=	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase ,_lowerCAmelCase							)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_tokenizer()

								lowerCamelCase__								=	"""Encode this sequence."""
								lowerCamelCase__								=	tokenizer.byte_encoder[""" """.encode("""utf-8"""							)[0]]

								# Testing encoder arguments
								lowerCamelCase__								=	tokenizer.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
								self.assertNotEqual(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	tokenizer.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
								self.assertEqual(_lowerCAmelCase ,_lowerCAmelCase							)

								tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
								lowerCamelCase__								=	tokenizer.encode(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
								self.assertNotEqual(_lowerCAmelCase ,_lowerCAmelCase							)

								# Testing spaces after special tokens
								lowerCamelCase__								=	"""<mask>"""
								tokenizer.add_special_tokens(
								    {"""mask_token""": AddedToken(_lowerCAmelCase ,lstrip=_lowerCAmelCase ,rstrip=_lowerCAmelCase							)}							)  # mask token has a left space
								lowerCamelCase__								=	tokenizer.convert_tokens_to_ids(_lowerCAmelCase							)

								lowerCamelCase__								=	"""Encode <mask> sequence"""
								lowerCamelCase__								=	"""Encode <mask>sequence"""

								lowerCamelCase__								=	tokenizer.encode(_lowerCAmelCase							)
								lowerCamelCase__								=	encoded.index(_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
								self.assertEqual(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	tokenizer.encode(_lowerCAmelCase							)
								lowerCamelCase__								=	encoded.index(_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
								self.assertNotEqual(_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self							):
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,**_lowerCAmelCase							)
																				lowerCamelCase__								=	self.tokenizer_class.from_pretrained(_lowerCAmelCase ,**_lowerCAmelCase							)
																				lowerCamelCase__								=	"""A, <mask> AllenNLP sentence."""
																				lowerCamelCase__								=	tokenizer_r.encode_plus(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase ,return_token_type_ids=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_p.encode_plus(_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase ,return_token_type_ids=_lowerCAmelCase							)

																				# token_type_ids should put 0 everywhere
																				self.assertEqual(sum(tokens_r["""token_type_ids"""]							) ,sum(tokens_p["""token_type_ids"""]							)							)

																				# attention_mask should put 1 everywhere, so sum over length should be 1
																				self.assertEqual(
																				    sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							) ,sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							) ,)

																				lowerCamelCase__								=	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
																				lowerCamelCase__								=	tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

																				# Rust correctly handles the space before the mask while python doesnt
																				self.assertSequenceEqual(tokens_p["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
																				self.assertSequenceEqual(tokens_r["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

																				self.assertSequenceEqual(
																				    _lowerCAmelCase ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
																				self.assertSequenceEqual(
																				    _lowerCAmelCase ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)






		def       UpperCamelCase_       (   self							):
								for trim_offsets, add_prefix_space in itertools.product([True, False] ,repeat=2							):
														lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
														    self.tmpdirname ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)

														lowerCamelCase__								=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
														lowerCamelCase__								=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

														self.assertEqual(pre_tokenizer_state["""add_prefix_space"""] ,_lowerCAmelCase							)

														self.assertEqual(post_processor_state["""add_prefix_space"""] ,_lowerCAmelCase							)
														self.assertEqual(post_processor_state["""trim_offsets"""] ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
								# `trim_offsets`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																				lowerCamelCase__								=	"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																				lowerCamelCase__								=	F'''{text_of_1_token} {text_of_1_token}'''

																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_r(_lowerCAmelCase ,return_offsets_mapping=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
																				self.assertEqual(encoding.offset_mapping[0] ,(0, len(_lowerCAmelCase							))							)
																				self.assertEqual(
																				    encoding.offset_mapping[1] ,(len(_lowerCAmelCase							) + 1, len(_lowerCAmelCase							) + 1 + len(_lowerCAmelCase							)) ,)

																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_r(_lowerCAmelCase ,return_offsets_mapping=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
																				self.assertEqual(encoding.offset_mapping[0] ,(0, len(_lowerCAmelCase							))							)
																				self.assertEqual(
																				    encoding.offset_mapping[1] ,(len(_lowerCAmelCase							) + 1, len(_lowerCAmelCase							) + 1 + len(_lowerCAmelCase							)) ,)

																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_r(_lowerCAmelCase ,return_offsets_mapping=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
																				self.assertEqual(encoding.offset_mapping[0] ,(0, len(_lowerCAmelCase							))							)
																				self.assertEqual(
																				    encoding.offset_mapping[1] ,(len(_lowerCAmelCase							), len(_lowerCAmelCase							) + 1 + len(_lowerCAmelCase							)) ,)

																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_r(_lowerCAmelCase ,return_offsets_mapping=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
																				self.assertEqual(encoding.offset_mapping[0] ,(0, len(_lowerCAmelCase							))							)
																				self.assertEqual(
																				    encoding.offset_mapping[1] ,(len(_lowerCAmelCase							), len(_lowerCAmelCase							) + 1 + len(_lowerCAmelCase							)) ,)

																				lowerCamelCase__								=	F''' {text}'''

																				# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																				#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																				# )
																				# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																				# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																				# self.assertEqual(
																				#     encoding.offset_mapping[1],
																				#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																				# )

																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_r(_lowerCAmelCase ,return_offsets_mapping=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
																				self.assertEqual(encoding.offset_mapping[0] ,(1, 1 + len(_lowerCAmelCase							))							)
																				self.assertEqual(
																				    encoding.offset_mapping[1] ,(1 + len(_lowerCAmelCase							) + 1, 1 + len(_lowerCAmelCase							) + 1 + len(_lowerCAmelCase							)) ,)

																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_r(_lowerCAmelCase ,return_offsets_mapping=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
																				self.assertEqual(encoding.offset_mapping[0] ,(0, 1 + len(_lowerCAmelCase							))							)
																				self.assertEqual(
																				    encoding.offset_mapping[1] ,(1 + len(_lowerCAmelCase							), 1 + len(_lowerCAmelCase							) + 1 + len(_lowerCAmelCase							)) ,)

																				lowerCamelCase__								=	self.rust_tokenizer_class.from_pretrained(
																				    _lowerCAmelCase ,use_fast=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ,trim_offsets=_lowerCAmelCase							)
																				lowerCamelCase__								=	tokenizer_r(_lowerCAmelCase ,return_offsets_mapping=_lowerCAmelCase ,add_special_tokens=_lowerCAmelCase							)
																				self.assertEqual(encoding.offset_mapping[0] ,(0, 1 + len(_lowerCAmelCase							))							)
																				self.assertEqual(
																				    encoding.offset_mapping[1] ,(1 + len(_lowerCAmelCase							), 1 + len(_lowerCAmelCase							) + 1 + len(_lowerCAmelCase							)) ,)



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

from ..utils.generic import ModelOutput




class   __lowercase						(						__snake_case       ):






	def __init__(self				: List[Any]      ,	snake_case				: Optional[int]      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	Tuple           =   dataset
							_lowercase       :	List[str]           =   process
							_lowercase       :	Any           =   params






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






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			int:
							_lowercase       :	Optional[Any]           =   self.dataset[i]
							_lowercase       :	Union[str, Any]           =   self.process(snake_case      ,	**self.params    )
							return processed




class   __lowercase						(						__snake_case       ):






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

							# Internal bookkeeping
							_lowercase       :	Dict           =   None
							_lowercase       :	Union[str, Any]           =   None






	def __len__(self				: Any    ) ->			Any:
							return len(self.loader    )






	def __iter__(self				: int    ) ->			Optional[Any]:
							_lowercase       :	List[str]           =   iter(self.loader    )
							return self






	def       _a(self				: int    ) ->			List[str]:
							if isinstance(self._loader_batch_data      ,	torch.Tensor    ):
													# Batch data is simple tensor, just fetch the slice
													_lowercase       :	Any           =   self._loader_batch_data[self._loader_batch_index]
							else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													_lowercase       :	List[str]           =   {}
													for k, element in self._loader_batch_data.items():
																			if isinstance(snake_case      ,	snake_case    ):
																									# Convert ModelOutput to tuple first
																									_lowercase       :	Dict           =   element.to_tuple()
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	Optional[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(snake_case      ,	snake_case    ):
																									# Those are stored as lists of tensors so need specific unbatching.
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	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
																									_lowercase       :	Optional[Any]           =   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

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






	def       _a(self				: Any    ) ->			str:
							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
							_lowercase       :	str           =   next(self.iterator    )
							_lowercase       :	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    ):
																			_lowercase       :	int           =   processed
													else:
																			_lowercase       :	List[str]           =   list(processed.keys()    )[0]
																			_lowercase       :	List[str]           =   processed[key]
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	int           =   len(snake_case    )
													else:
																			_lowercase       :	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.
																			_lowercase       :	Union[str, Any]           =   observed_batch_size
													# Setting internal index to unwrap the batch
													_lowercase       :	Any           =   processed
													_lowercase       :	Dict           =   0
													return self.loader_batch_item()
							else:
													# We're not unrolling batches
													return processed




class   __lowercase						(						__snake_case       ):






	def __init__(self				: Union[str, Any]      ,	snake_case				: str      ,	snake_case				: Dict      ,	snake_case				: Optional[Any]      ,	snake_case				: List[str]=None    ) ->			List[str]:
							super().__init__(snake_case      ,	snake_case      ,	snake_case    )






	def __iter__(self				: List[Any]    ) ->			str:
							_lowercase       :	int           =   iter(self.loader    )
							_lowercase       :	Optional[int]           =   None
							return self






	def       _a(self				: Optional[Any]    ) ->			Optional[int]:
							if self.subiterator is None:
													_lowercase       :	List[str]           =   self.infer(next(self.iterator    )      ,	**self.params    )
							try:
													# Try to return next item
													_lowercase       :	str           =   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
													_lowercase       :	Any           =   self.infer(next(self.iterator    )      ,	**self.params    )
													_lowercase       :	Optional[int]           =   next(self.subiterator    )
							return processed




class   __lowercase						(						__snake_case       ):






	def __iter__(self				: List[str]    ) ->			List[str]:
							_lowercase       :	Any           =   iter(self.loader    )
							return self






	def       _a(self				: List[Any]    ) ->			Union[str, Any]:
							# 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.
							_lowercase       :	Dict           =   False
							_lowercase       :	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:
																			_lowercase       :	Tuple           =   self.loader_batch_item()
																			_lowercase       :	List[Any]           =   item.pop("is_last"    )
																			accumulator.append(snake_case    )
																			if is_last:
																									return accumulator

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




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str    ) ->			List[Any]:
							_lowercase       :	Optional[Any]           =   dataset
							_lowercase       :	Any           =   key






	def __len__(self				: Any    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			Any:
							return self.dataset[i][self.key]




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	int           =   dataset
							_lowercase       :	Optional[Any]           =   keya
							_lowercase       :	Tuple           =   keya






	def __len__(self				: List[str]    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: Optional[Any]      ,	snake_case				: Dict    ) ->			int:
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




