






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

from ..utils.generic import ModelOutput







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Union[str, Any]	,	lowerCAmelCase_					: Tuple	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: List[str]						)						->			Tuple:
				UpperCAmelCase_     : Union[str, Any]			     =					dataset
				UpperCAmelCase_     : int			     =					process
				UpperCAmelCase_     : int			     =					params





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





	def __getitem__(      self					: Tuple	,	lowerCAmelCase_					: List[str]						)						->			int:
				UpperCAmelCase_     : Any			     =					self.dataset[i]
				UpperCAmelCase_     : int			     =					self.process(lowerCAmelCase_	,	**self.params						)
				return processed







class 			UpperCamelCase_       (__A     ):





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

				# Internal bookkeeping
				UpperCAmelCase_     : Union[str, Any]			     =					None
				UpperCAmelCase_     : Optional[Any]			     =					None





	def __len__(      self					: List[Any]						)						->			Optional[int]:
				return len(self.loader						)





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





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

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





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Optional[int]:
				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
				UpperCAmelCase_     : List[str]			     =					next(self.iterator						)
				UpperCAmelCase_     : List[Any]			     =					self.infer(lowerCAmelCase_	,	**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(lowerCAmelCase_	,	torch.Tensor						):
										UpperCAmelCase_     : Optional[int]			     =					processed
							else:
										UpperCAmelCase_     : List[Any]			     =					list(processed.keys()						)[0]
										UpperCAmelCase_     : Dict			     =					processed[key]
							if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Optional[int]			     =					len(lowerCAmelCase_						)
							else:
										UpperCAmelCase_     : List[Any]			     =					first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
										# could be last batch so we can't unroll as many
										# elements.
										UpperCAmelCase_     : List[Any]			     =					observed_batch_size
							# Setting internal index to unwrap the batch
							UpperCAmelCase_     : Any			     =					processed
							UpperCAmelCase_     : Dict			     =					0
							return self.loader_batch_item()
				else:
							# We're not unrolling batches
							return processed







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[Any]=None						)						->			Tuple:
				super().__init__(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)





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





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







class 			UpperCamelCase_       (__A     ):





	def __iter__(      self					: Union[str, Any]						)						->			Tuple:
				UpperCAmelCase_     : int			     =					iter(self.loader						)
				return self





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			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.
				UpperCAmelCase_     : Optional[Any]			     =					False
				UpperCAmelCase_     : List[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:
										UpperCAmelCase_     : List[str]			     =					self.loader_batch_item()
										UpperCAmelCase_     : Any			     =					item.pop("is_last"						)
										accumulator.append(lowerCAmelCase_						)
										if is_last:
													return accumulator

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







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: Union[str, Any]	,	lowerCAmelCase_					: Dataset	,	lowerCAmelCase_					: str						)						->			str:
				UpperCAmelCase_     : str			     =					dataset
				UpperCAmelCase_     : List[Any]			     =					key





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





	def __getitem__(      self					: str	,	lowerCAmelCase_					: Any						)						->			Union[str, Any]:
				return self.dataset[i][self.key]







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: str	,	lowerCAmelCase_					: Dataset	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: str						)						->			List[str]:
				UpperCAmelCase_     : List[Any]			     =					dataset
				UpperCAmelCase_     : Tuple			     =					keya
				UpperCAmelCase_     : Union[str, Any]			     =					keya





	def __len__(      self					: Tuple						)						->			int:
				return len(self.dataset						)





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











"""simple docstring"""
import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class 			UpperCamelCase_       (__A				,						unittest.TestCase     ):
	__magic_name__	      =				BertTokenizer
	__magic_name__	      =				BertTokenizerFast
	__magic_name__	      =				True
	__magic_name__	      =				True
	__magic_name__	      =				filter_non_english





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			List[Any]:
				super().setUp()

				UpperCAmelCase_     : Tuple			     =					[
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				UpperCAmelCase_     : Any			     =					os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"]						)
				with open(self.vocab_file	,	"w"	,	encoding="utf-8"						) as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: str						)						->			Union[str, Any]:
				UpperCAmelCase_     : Tuple			     =					"UNwant\u00E9d,running"
				UpperCAmelCase_     : Any			     =					"unwanted, running"
				return input_text, output_text





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			List[str]:
				UpperCAmelCase_     : Any			     =					self.tokenizer_class(self.vocab_file						)

				UpperCAmelCase_     : Tuple			     =					tokenizer.tokenize("UNwant\u00E9d,running"						)
				self.assertListEqual(lowerCAmelCase_	,	["un", "##want", "##ed", ",", "runn", "##ing"]						)
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_						)	,	[9, 6, 7, 12, 10, 11]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Any:
				if not self.test_rust_tokenizer:
							return

				UpperCAmelCase_     : Union[str, Any]			     =					self.get_tokenizer()
				UpperCAmelCase_     : List[Any]			     =					self.get_rust_tokenizer()

				UpperCAmelCase_     : List[Any]			     =					"UNwant\u00E9d,running"

				UpperCAmelCase_     : Union[str, Any]			     =					tokenizer.tokenize(lowerCAmelCase_						)
				UpperCAmelCase_     : int			     =					rust_tokenizer.tokenize(lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Any			     =					tokenizer.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					rust_tokenizer.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Optional[Any]			     =					self.get_rust_tokenizer()
				UpperCAmelCase_     : Dict			     =					tokenizer.encode(lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					rust_tokenizer.encode(lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

				# With lower casing
				UpperCAmelCase_     : Tuple			     =					self.get_tokenizer(do_lower_case=lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					self.get_rust_tokenizer(do_lower_case=lowerCAmelCase_						)

				UpperCAmelCase_     : List[Any]			     =					"UNwant\u00E9d,running"

				UpperCAmelCase_     : List[Any]			     =					tokenizer.tokenize(lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					rust_tokenizer.tokenize(lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Union[str, Any]			     =					tokenizer.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					rust_tokenizer.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Optional[Any]			     =					self.get_rust_tokenizer()
				UpperCAmelCase_     : Union[str, Any]			     =					tokenizer.encode(lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					rust_tokenizer.encode(lowerCAmelCase_						)
				self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			str:
				UpperCAmelCase_     : Optional[Any]			     =					BasicTokenizer()

				self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"						)	,	["ah", "\u535A", "\u63A8", "zz"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Any:
				UpperCAmelCase_     : Optional[Any]			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "						)	,	["hello", "!", "how", "are", "you", "?"]						)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)	,	["hello"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Any:
				UpperCAmelCase_     : List[str]			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_	,	strip_accents=lowerCAmelCase_						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)	,	["hällo", "!", "how", "are", "you", "?"]						)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)	,	["h\u00E9llo"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			int:
				UpperCAmelCase_     : Any			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_	,	strip_accents=lowerCAmelCase_						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)	,	["hallo", "!", "how", "are", "you", "?"]						)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)	,	["hello"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Optional[int]:
				UpperCAmelCase_     : Tuple			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)	,	["hallo", "!", "how", "are", "you", "?"]						)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"						)	,	["hello"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				UpperCAmelCase_     : List[str]			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "						)	,	["HeLLo", "!", "how", "Are", "yoU", "?"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			List[Any]:
				UpperCAmelCase_     : Optional[int]			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_	,	strip_accents=lowerCAmelCase_						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)	,	["HäLLo", "!", "how", "Are", "yoU", "?"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Optional[Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_	,	strip_accents=lowerCAmelCase_						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "						)	,	["HaLLo", "!", "how", "Are", "yoU", "?"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				UpperCAmelCase_     : Tuple			     =					BasicTokenizer(do_lower_case=lowerCAmelCase_	,	never_split=["[UNK]"]						)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"						)	,	["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					BasicTokenizer()
				UpperCAmelCase_     : Dict			     =					"a\n'll !!to?'d of, can't."
				UpperCAmelCase_     : List[str]			     =					["a", "'", "ll", "!", "!", "to", "?", "'", "d", "of", ",", "can", "'", "t", "."]
				self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_						)	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			int:
				UpperCAmelCase_     : int			     =					["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

				UpperCAmelCase_     : Tuple			     =					{}
				for i, token in enumerate(lowerCAmelCase_						):
							UpperCAmelCase_     : Optional[int]			     =					i
				UpperCAmelCase_     : Optional[Any]			     =					WordpieceTokenizer(vocab=lowerCAmelCase_	,	unk_token="[UNK]"						)

				self.assertListEqual(tokenizer.tokenize(""						)	,	[]						)

				self.assertListEqual(tokenizer.tokenize("unwanted running"						)	,	["un", "##want", "##ed", "runn", "##ing"]						)

				self.assertListEqual(tokenizer.tokenize("unwantedX running"						)	,	["[UNK]", "runn", "##ing"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Optional[int]:
				self.assertTrue(_is_whitespace(" "						)						)
				self.assertTrue(_is_whitespace("\t"						)						)
				self.assertTrue(_is_whitespace("\r"						)						)
				self.assertTrue(_is_whitespace("\n"						)						)
				self.assertTrue(_is_whitespace("\u00A0"						)						)

				self.assertFalse(_is_whitespace("A"						)						)
				self.assertFalse(_is_whitespace("-"						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Optional[int]:
				self.assertTrue(_is_control("\u0005"						)						)

				self.assertFalse(_is_control("A"						)						)
				self.assertFalse(_is_control(" "						)						)
				self.assertFalse(_is_control("\t"						)						)
				self.assertFalse(_is_control("\r"						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			List[str]:
				self.assertTrue(_is_punctuation("-"						)						)
				self.assertTrue(_is_punctuation("$"						)						)
				self.assertTrue(_is_punctuation("`"						)						)
				self.assertTrue(_is_punctuation("."						)						)

				self.assertFalse(_is_punctuation("A"						)						)
				self.assertFalse(_is_punctuation(" "						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Optional[Any]:
				UpperCAmelCase_     : Dict			     =					self.get_tokenizer()
				UpperCAmelCase_     : List[str]			     =					self.get_rust_tokenizer()

				# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
				self.assertListEqual([tokenizer.tokenize(lowerCAmelCase_						) for t in ["Test", "\xad", "test"]]	,	[["[UNK]"], [], ["[UNK]"]]						)

				self.assertListEqual(
				    [rust_tokenizer.tokenize(lowerCAmelCase_						) for t in ["Test", "\xad", "test"]]	,	[["[UNK]"], [], ["[UNK]"]]						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : List[str]			     =					self.tokenizer_class.from_pretrained("bert-base-uncased"						)

				UpperCAmelCase_     : Any			     =					tokenizer.encode("sequence builders"	,	add_special_tokens=lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					tokenizer.encode("multi-sequence build"	,	add_special_tokens=lowerCAmelCase_						)

				UpperCAmelCase_     : Tuple			     =					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_	,	lowerCAmelCase_						)

				assert encoded_sentence == [101] + text + [102]
				assert encoded_pair == [101] + text + [102] + text_a + [102]





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			str:
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
										UpperCAmelCase_     : str			     =					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_	,	**lowerCAmelCase_						)

										UpperCAmelCase_     : Union[str, Any]			     =					f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										UpperCAmelCase_     : Tuple			     =					tokenizer_r.encode_plus(
										    lowerCAmelCase_	,	return_attention_mask=lowerCAmelCase_	,	return_token_type_ids=lowerCAmelCase_	,	return_offsets_mapping=lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_	,	)

										UpperCAmelCase_     : Optional[int]			     =					tokenizer_r.do_lower_case if hasattr(lowerCAmelCase_	,	"do_lower_case"						) else False
										UpperCAmelCase_     : List[Any]			     =					(
										    [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), "A"),
										        ((1, 2), ","),
										        ((3, 5), "na"),
										        ((5, 6), "##ï"),
										        ((6, 8), "##ve"),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), "Allen"),
										        ((21, 23), "##NL"),
										        ((23, 24), "##P"),
										        ((25, 33), "sentence"),
										        ((33, 34), "."),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										    if not do_lower_case
										    else [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), "a"),
										        ((1, 2), ","),
										        ((3, 8), "naive"),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), "allen"),
										        ((21, 23), "##nl"),
										        ((23, 24), "##p"),
										        ((25, 33), "sentence"),
										        ((33, 34), "."),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										)

										self.assertEqual(
										    [e[1] for e in expected_results]	,	tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]						)						)
										self.assertEqual([e[0] for e in expected_results]	,	tokens["offset_mapping"]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : List[Any]			     =					["的", "人", "有"]
				UpperCAmelCase_     : Tuple			     =					"".join(lowerCAmelCase_						)
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
										UpperCAmelCase_     : Optional[Any]			     =					True
										UpperCAmelCase_     : Any			     =					self.tokenizer_class.from_pretrained(lowerCAmelCase_	,	**lowerCAmelCase_						)
										UpperCAmelCase_     : Optional[Any]			     =					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_	,	**lowerCAmelCase_						)

										UpperCAmelCase_     : Dict			     =					tokenizer_p.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
										UpperCAmelCase_     : Dict			     =					tokenizer_r.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)

										UpperCAmelCase_     : Union[str, Any]			     =					tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_						)
										UpperCAmelCase_     : Any			     =					tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_						)

										# it is expected that each Chinese character is not preceded by "##"
										self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)
										self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)

										UpperCAmelCase_     : List[Any]			     =					False
										UpperCAmelCase_     : Optional[Any]			     =					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_	,	**lowerCAmelCase_						)
										UpperCAmelCase_     : List[Any]			     =					self.tokenizer_class.from_pretrained(lowerCAmelCase_	,	**lowerCAmelCase_						)

										UpperCAmelCase_     : int			     =					tokenizer_r.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)
										UpperCAmelCase_     : List[str]			     =					tokenizer_p.encode(lowerCAmelCase_	,	add_special_tokens=lowerCAmelCase_						)

										UpperCAmelCase_     : Union[str, Any]			     =					tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_						)
										UpperCAmelCase_     : List[str]			     =					tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_						)

										# it is expected that only the first Chinese character is not preceded by "##".
										UpperCAmelCase_     : Tuple			     =					[
										    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowerCAmelCase_						)
										]
										self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)
										self.assertListEqual(lowerCAmelCase_	,	lowerCAmelCase_						)



