code: 
  
  
  '''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
  
  
  lowerCAmelCase   :List[str]								=    logging.get_logger(__name__)
  
  
  lowerCAmelCase   :Tuple								=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
  
  # See all MVP models at https://huggingface.co/models?filter=mvp
  lowerCAmelCase   :Optional[Any]								=    {
      '''vocab_file''': {
          '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
      },
      '''added_tokens.json''': {
          '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
      },
      '''merges_file''': {
          '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
      },
  }
  
  lowerCAmelCase   :Any								=    {
      '''RUCAIBox/mvp''': 1_0_2_4,
  }
  
  
  
  
  
  @lru_cache()
  def lowerCamelCase    (    ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		__magic_name__ :					List[str]  			=  (
  		    list(range(ord('!'	)	,	ord('~'	) + 1	)	) + list(range(ord('¡'	)	,	ord('¬'	) + 1	)	) + list(range(ord('®'	)	,	ord('ÿ'	) + 1	)	)
  		)
  		__magic_name__ :					List[Any]  			=  bs[:]
  		__magic_name__ :					Tuple  			=  0
  		for b in range(2**8	):
  				if b not in bs:
  						bs.append(lowerCAmelCase	)
  						cs.append(2**8 + n	)
  						n += 1
  		__magic_name__ :					Dict  			=  [chr(lowerCAmelCase	) for n in cs]
  		return dict(zip(lowerCAmelCase	,	lowerCAmelCase	)	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		__magic_name__ :					int  			=  set()
  		__magic_name__ :					Optional[int]  			=  word[0]
  		for char in word[1:]:
  				pairs.add((prev_char, char)	)
  				__magic_name__ :					int  			=  char
  		return pairs
  
  
  
  
  
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    str   					=       VOCAB_FILES_NAMES
  		A_			:    str   					=       PRETRAINED_VOCAB_FILES_MAP
  		A_			:    List[Any]   					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  		A_			:    int   					=       ["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
  
  		def __init__(      self	:		Optional[int]						,  _A	:		Optional[int]						,  _A	:		Union[str, Any]						,  _A	:		Union[str, Any]="replace"						,  _A	:		Tuple="<s>"						,  _A	:		str="</s>"						,  _A	:		List[Any]="</s>"						,  _A	:		List[str]="<s>"						,  _A	:		Dict="<unk>"						,  _A	:		List[Any]="<pad>"						,  _A	:		List[str]="<mask>"						,  _A	:		Optional[Any]=False						,  **_A	:		Union[str, Any]						,  )						->				Dict:
  				__magic_name__ :					List[Any]  			=  AddedToken(_A						,  lstrip=_A						,  rstrip=_A    ) if isinstance(_A						,  _A    ) else bos_token
  				__magic_name__ :					Tuple  			=  AddedToken(_A						,  lstrip=_A						,  rstrip=_A    ) if isinstance(_A						,  _A    ) else eos_token
  				__magic_name__ :					List[Any]  			=  AddedToken(_A						,  lstrip=_A						,  rstrip=_A    ) if isinstance(_A						,  _A    ) else sep_token
  				__magic_name__ :					Optional[Any]  			=  AddedToken(_A						,  lstrip=_A						,  rstrip=_A    ) if isinstance(_A						,  _A    ) else cls_token
  				__magic_name__ :					Optional[int]  			=  AddedToken(_A						,  lstrip=_A						,  rstrip=_A    ) if isinstance(_A						,  _A    ) else unk_token
  				__magic_name__ :					str  			=  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
  				__magic_name__ :					List[str]  			=  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:
  						__magic_name__ :					int  			=  json.load(_A    )
  				__magic_name__ :					str  			=  {v: k for k, v in self.encoder.items()}
  				__magic_name__ :					Optional[Any]  			=  errors  # how to handle errors in decoding
  				__magic_name__ :					int  			=  bytes_to_unicode()
  				__magic_name__ :					Union[str, Any]  			=  {v: k for k, v in self.byte_encoder.items()}
  				with open(_A						,  encoding='utf-8'    ) as merges_handle:
  						__magic_name__ :					Dict  			=  merges_handle.read().split('\n'    )[1:-1]
  				__magic_name__ :					Any  			=  [tuple(merge.split()    ) for merge in bpe_merges]
  				__magic_name__ :					List[Any]  			=  dict(zip(_A						,  range(len(_A    )    )    )    )
  				__magic_name__ :					Optional[int]  			=  {}
  				__magic_name__ :					Optional[int]  			=  add_prefix_space
  
  				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  				__magic_name__ :					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    )						->				Optional[int]:
  				return len(self.encoder    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
  				return dict(self.encoder						,  **self.added_tokens_encoder    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str						,  _A	:		Optional[Any]    )						->				Tuple:
  				if token in self.cache:
  						return self.cache[token]
  				__magic_name__ :					Any  			=  tuple(_A    )
  				__magic_name__ :					List[str]  			=  get_pairs(_A    )
  
  				if not pairs:
  						return token
  
  				while True:
  						__magic_name__ :					List[str]  			=  min(_A						,  key=lambda _A    : self.bpe_ranks.get(_A						,  float('inf'    )    )    )
  						if bigram not in self.bpe_ranks:
  								break
  						__magic_name__ :					List[str]  			=  bigram
  						__magic_name__ :					List[Any]  			=  []
  						__magic_name__ :					str  			=  0
  						while i < len(_A    ):
  								try:
  										__magic_name__ :					Optional[int]  			=  word.index(_A						,  _A    )
  								except ValueError:
  										new_word.extend(word[i:]    )
  										break
  								else:
  										new_word.extend(word[i:j]    )
  										__magic_name__ :					str  			=  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
  						__magic_name__ :					Union[str, Any]  			=  tuple(_A    )
  						__magic_name__ :					List[Any]  			=  new_word
  						if len(_A    ) == 1:
  								break
  						else:
  								__magic_name__ :					Tuple  			=  get_pairs(_A    )
  				__magic_name__ :					Union[str, Any]  			=  ' '.join(_A    )
  				__magic_name__ :					str  			=  word
  				return word
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		List[Any]    )						->				int:
  				__magic_name__ :					int  			=  []
  				for token in re.findall(self.pat						,  _A    ):
  						__magic_name__ :					Union[str, 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	:		Optional[Any]						,  _A	:		Any    )						->				Any:
  				return self.encoder.get(_A						,  self.encoder.get(self.unk_token    )    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		List[str]    )						->				str:
  				return self.decoder.get(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple						,  _A	:		Any    )						->				Union[str, Any]:
  				__magic_name__ :					Tuple  			=  ''.join(_A    )
  				__magic_name__ :					Tuple  			=  bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'						,  errors=self.errors    )
  				return text
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		str						,  _A	:		Optional[str] = None    )						->				Tuple[str]:
  				if not os.path.isdir(_A    ):
  						logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
  						return
  				__magic_name__ :					Optional[int]  			=  os.path.join(
  				    _A						,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
  				__magic_name__ :					Dict  			=  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'    )
  
  				__magic_name__ :					Dict  			=  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 _A    : 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!'    )
  										__magic_name__ :					Any  			=  token_index
  								writer.write(' '.join(_A    ) + '\n'    )
  								index += 1
  
  				return vocab_file, merge_file
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		List[int]						,  _A	:		Optional[List[int]] = None    )						->				List[int]:
  				if token_ids_a is None:
  						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__ :					Union[str, Any]  			=  [self.cls_token_id]
  				__magic_name__ :					Tuple  			=  [self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		List[int]						,  _A	:		Optional[List[int]] = None						,  _A	:		bool = False    )						->				List[int]:
  				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	:		str						,  _A	:		List[int]						,  _A	:		Optional[List[int]] = None    )						->				List[int]:
  				__magic_name__ :					Optional[Any]  			=  [self.sep_token_id]
  				__magic_name__ :					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						,  _A	:		List[Any]						,  _A	:		str=False						,  **_A	:		List[Any]    )						->				List[str]:
  				__magic_name__ :					List[Any]  			=  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()):
  						__magic_name__ :					Tuple  			=  ' ' + text
  				return (text, kwargs)
style_context: 
  
  
  '''simple docstring'''
  
  import os
  import pickle
  import tempfile
  import unittest
  from typing import Callable, Optional
  
  import numpy as np
  
  from transformers import (
      BatchEncoding,
      BertTokenizer,
      BertTokenizerFast,
      PreTrainedTokenizer,
      PreTrainedTokenizerFast,
      TensorType,
      TokenSpan,
      is_tokenizers_available,
  )
  from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
  from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow
  
  
  if is_tokenizers_available():
  							from tokenizers import Tokenizer
  							from tokenizers.models import WordPiece
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Optional[Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  list(tokenizer_class.max_model_input_sizes.keys()    )
  			for model_name in sa_models[:1]:
  				__magic_name__ :					List[str]  			=  tokenizer_class.from_pretrained(_A    )
  				self.assertIsNotNone(_A    )
  				self.assertIsInstance(_A						,  _A    )
  				self.assertIsInstance(_A						,  _A    )
  
  				for special_tok in tokenizer.all_special_tokens:
  					self.assertIsInstance(_A						,  _A    )
  					__magic_name__ :					Optional[Any]  			=  tokenizer.convert_tokens_to_ids(_A    )
  					self.assertIsInstance(_A						,  _A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		BatchEncoding						,  _A	:		Optional[Callable] = None    )						->				Tuple:
  			__magic_name__ :					Union[str, Any]  			=  pickle.dumps(_A    )
  			self.assertIsNotNone(_A    )
  
  			__magic_name__ :					Optional[Any]  			=  pickle.loads(_A    )
  
  			# Ensure is_fast is correctly restored
  			self.assertEqual(be_restored.is_fast						,  be_original.is_fast    )
  
  			# Ensure encodings are potentially correctly restored
  			if be_original.is_fast:
  				self.assertIsNotNone(be_restored.encodings    )
  			else:
  				self.assertIsNone(be_restored.encodings    )
  
  			# Ensure the keys are the same
  			for original_v, restored_v in zip(be_original.values()						,  be_restored.values()    ):
  				if equal_op:
  					self.assertTrue(equal_op(_A						,  _A    )    )
  				else:
  					self.assertEqual(_A						,  _A    )
  
  
  
  
  
  
  
  		@slow
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[Any]:
  			self.check_tokenizer_from_pretrained(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				List[Any]:
  			self.assertEqual(TensorType('tf'    )						,  TensorType.TENSORFLOW    )
  			self.assertEqual(TensorType('pt'    )						,  TensorType.PYTORCH    )
  			self.assertEqual(TensorType('np'    )						,  TensorType.NUMPY    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Optional[int]:
  			import numpy as np
  
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					str  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			# Python no tensor
  			with self.subTest('BatchEncoding (Python, return_tensors=None)'    ):
  				self.assert_dump_and_restore(tokenizer_p('Small example to encode'    )    )
  
  			with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_p('Small example to encode'						,  return_tensors=TensorType.NUMPY    )						,  np.array_equal    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=None)'    ):
  				self.assert_dump_and_restore(tokenizer_r('Small example to encode'    )    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_r('Small example to encode'						,  return_tensors=TensorType.NUMPY    )						,  np.array_equal    )
  
  
  
  
  
  
  
  		@require_tf
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[str]:
  			import tensorflow as tf
  
  			def tf_array_equals(_A	:		str						,  _A	:		Union[str, Any]    ):
  				return tf.reduce_all(tf.equal(_A						,  _A    )    )
  
  			__magic_name__ :					str  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Optional[Any]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_p('Small example to encode'						,  return_tensors=TensorType.TENSORFLOW    )						,  _A    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_r('Small example to encode'						,  return_tensors=TensorType.TENSORFLOW    )						,  _A    )
  
  
  
  
  
  
  
  		@require_torch
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Any    )						->				List[str]:
  			import torch
  
  			__magic_name__ :					Union[str, Any]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Optional[int]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_p('Small example to encode'						,  return_tensors=TensorType.PYTORCH    )						,  torch.equal    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_r('Small example to encode'						,  return_tensors=TensorType.PYTORCH    )						,  torch.equal    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Dict:
  			__magic_name__ :					Optional[int]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Dict  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			with self.subTest('Python Tokenizer'    ):
  				self.assertFalse(tokenizer_p('Small example to_encode'    ).is_fast    )
  
  			with self.subTest('Rust Tokenizer'    ):
  				self.assertTrue(tokenizer_r('Small example to_encode'    ).is_fast    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Tuple  			=  tokenizer_r(['Test', '\xad', 'test']						,  is_split_into_words=_A    )
  
  			self.assertEqual(encoded.word_to_tokens(0    )						,  TokenSpan(start=1						,  end=2    )    )
  			self.assertEqual(encoded.word_to_tokens(1    )						,  _A    )
  			self.assertEqual(encoded.word_to_tokens(2    )						,  TokenSpan(start=2						,  end=3    )    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[Any]:
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					Dict  			=  batch.convert_to_tensors(tensor_type='np'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Any  			=  batch.convert_to_tensors(tensor_type='np'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Optional[Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					List[Any]  			=  batch.convert_to_tensors(tensor_type='np'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_torch
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Any:
  			__magic_name__ :					Optional[int]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[Any]  			=  batch.convert_to_tensors(tensor_type='pt'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Union[str, Any]  			=  batch.convert_to_tensors(tensor_type='pt'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					str  			=  batch.convert_to_tensors(tensor_type='pt'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_tf
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[str]  			=  batch.convert_to_tensors(tensor_type='tf'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Optional[int]  			=  batch.convert_to_tensors(tensor_type='tf'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Union[str, Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Union[str, Any]  			=  batch.convert_to_tensors(tensor_type='tf'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_flax
  		def      __lowerCAmelCase	(      self	:		str    )						->				Optional[Any]:
  			__magic_name__ :					List[Any]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					int  			=  batch.convert_to_tensors(tensor_type='jax'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Optional[Any]  			=  batch.convert_to_tensors(tensor_type='jax'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Dict  			=  batch.convert_to_tensors(tensor_type='jax'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				int:
  			__magic_name__ :					int  			=  [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					Optional[int]  			=  tokenizer.pad(_A						,  padding=_A    )
  			self.assertTrue(isinstance(batch['input_ids']						,  np.ndarray    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  			__magic_name__ :					Dict  			=  tokenizer.pad(_A						,  padding=_A						,  return_tensors='np'    )
  			self.assertTrue(isinstance(batch['input_ids']						,  np.ndarray    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  
  
  
  
  
  
  
  		@require_torch
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				str:
  			import torch
  
  			__magic_name__ :					List[str]  			=  [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
  			__magic_name__ :					List[str]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					List[Any]  			=  tokenizer.pad(_A						,  padding=_A    )
  			self.assertTrue(isinstance(batch['input_ids']						,  torch.Tensor    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  			__magic_name__ :					Union[str, Any]  			=  tokenizer.pad(_A						,  padding=_A						,  return_tensors='pt'    )
  			self.assertTrue(isinstance(batch['input_ids']						,  torch.Tensor    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  
  
  
  
  
  
  
  		@require_tf
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
  			import tensorflow as tf
  
  			__magic_name__ :					Tuple  			=  [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
  			__magic_name__ :					Any  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					Any  			=  tokenizer.pad(_A						,  padding=_A    )
  			self.assertTrue(isinstance(batch['input_ids']						,  tf.Tensor    )    )
  			self.assertEqual(batch['input_ids'].numpy().tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  			__magic_name__ :					Union[str, Any]  			=  tokenizer.pad(_A						,  padding=_A						,  return_tensors='tf'    )
  			self.assertTrue(isinstance(batch['input_ids']						,  tf.Tensor    )    )
  			self.assertEqual(batch['input_ids'].numpy().tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Any    )						->				str:
  			__magic_name__ :					str  			=  Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  			PreTrainedTokenizerFast(tokenizer_object=_A    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Any:
  			__magic_name__ :					List[Any]  			=  Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				bert_tokenizer.save(os.path.join(_A						,  'tokenizer.json'    )    )
  				PreTrainedTokenizerFast(tokenizer_file=os.path.join(_A						,  'tokenizer.json'    )    )