





'''simple docstring'''






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class  lowerCamelCase__				(						tf.keras.layers.Layer						):


	"""simple docstring"""





	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = "longest"   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = 5_1_2   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = True   ,     **UpperCAmelCase__   ,     )       ->				List[str]:
					super().__init__()
					if use_fast_bert_tokenizer:
									_A :				Tuple				=  FastBertTokenizer(
									    __UpperCAmelCase   ,     token_out_type=tf.intaa   ,     lower_case_nfd_strip_accents=__UpperCAmelCase   ,     **__UpperCAmelCase			)
					else:
									_A :				Optional[int]				=  tf.lookup.StaticVocabularyTable(
									    tf.lookup.KeyValueTensorInitializer(
									        keys=__UpperCAmelCase   ,     key_dtype=tf.string   ,     values=tf.range(tf.size(__UpperCAmelCase   ,     out_type=tf.intaa			)   ,     dtype=tf.intaa			)   ,     value_dtype=tf.intaa   ,     )   ,     num_oov_buckets=1   ,     )
									_A :				List[Any]				=  BertTokenizerLayer(
									    __UpperCAmelCase   ,     token_out_type=tf.intaa   ,     lower_case=__UpperCAmelCase   ,     **__UpperCAmelCase			)

					_A :				Optional[int]				=  vocab_list
					_A :				Union[str, Any]				=  do_lower_case
					_A :				Optional[Any]				=  cls_token_id or vocab_list.index('''[CLS]'''			)
					_A :				Tuple				=  sep_token_id or vocab_list.index('''[SEP]'''			)
					_A :				Union[str, Any]				=  pad_token_id or vocab_list.index('''[PAD]'''			)
					_A :				Optional[int]				=  ShrinkLongestTrimmer(max_length - 3   ,     axis=1			)  # Allow room for special tokens
					_A :				Tuple				=  max_length
					_A :				Tuple				=  padding
					_A :				str				=  truncation
					_A :				List[str]				=  pad_to_multiple_of
					_A :				int				=  return_token_type_ids
					_A :				Union[str, Any]				=  return_attention_mask




	@classmethod
	def 				_lowerCamelCase				(					cls   ,     UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				int:  # noqa: F821
					_A :				Tuple				=  kwargs.pop('''do_lower_case'''   ,     __UpperCAmelCase			)
					_A :				Any				=  tokenizer.do_lower_case if do_lower_case is None else do_lower_case
					_A :				Union[str, Any]				=  kwargs.pop('''cls_token_id'''   ,     __UpperCAmelCase			)
					_A :				int				=  tokenizer.cls_token_id if cls_token_id is None else cls_token_id
					_A :				Tuple				=  kwargs.pop('''sep_token_id'''   ,     __UpperCAmelCase			)
					_A :				Union[str, Any]				=  tokenizer.sep_token_id if sep_token_id is None else sep_token_id
					_A :				Union[str, Any]				=  kwargs.pop('''pad_token_id'''   ,     __UpperCAmelCase			)
					_A :				str				=  tokenizer.pad_token_id if pad_token_id is None else pad_token_id

					_A :				List[Any]				=  tokenizer.get_vocab()
					_A :				str				=  sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]   ,     key=lambda UpperCAmelCase__			: x[1]			)
					_A :				str				=  [entry[0] for entry in vocab]
					return cls(
					    vocab_list=__UpperCAmelCase   ,     do_lower_case=__UpperCAmelCase   ,     cls_token_id=__UpperCAmelCase   ,     sep_token_id=__UpperCAmelCase   ,     pad_token_id=__UpperCAmelCase   ,     **__UpperCAmelCase   ,     )




	@classmethod
	def 				_lowerCamelCase				(					cls   ,     UpperCAmelCase__   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Union[str, Any]:



					try:
									_A :				List[Any]				=  BertTokenizer.from_pretrained(__UpperCAmelCase   ,     *__UpperCAmelCase   ,     **__UpperCAmelCase			)
					except:  # noqa: E722
									from .tokenization_bert_fast import BertTokenizerFast

									_A :				Tuple				=  BertTokenizerFast.from_pretrained(__UpperCAmelCase   ,     *__UpperCAmelCase   ,     **__UpperCAmelCase			)
					return cls.from_tokenizer(__UpperCAmelCase   ,     **__UpperCAmelCase			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				int:



					if self.do_lower_case:
									_A :				List[str]				=  case_fold_utfa(__UpperCAmelCase			)
					_A :				List[str]				=  self.tf_tokenizer.tokenize(__UpperCAmelCase			)
					return tokens.merge_dims(1   ,     -1			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None   ,     )       ->				List[str]:



					if padding is None:
									_A :				str				=  self.padding
					if padding not in ("longest", "max_length"):
									raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''			)
					if max_length is not None and text_pair is not None:
									# Because we have to instantiate a Trimmer to do it properly
									raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''			)
					if max_length is None:
									_A :				Tuple				=  self.max_length
					if truncation is None:
									_A :				List[str]				=  self.truncation
					if pad_to_multiple_of is None:
									_A :				Union[str, Any]				=  self.pad_to_multiple_of
					if return_token_type_ids is None:
									_A :				List[Any]				=  self.return_token_type_ids
					if return_attention_mask is None:
									_A :				List[str]				=  self.return_attention_mask
					if not isinstance(__UpperCAmelCase   ,     tf.Tensor			):
									_A :				Optional[int]				=  tf.convert_to_tensor(__UpperCAmelCase			)
					if text_pair is not None and not isinstance(__UpperCAmelCase   ,     tf.Tensor			):
									_A :				Dict				=  tf.convert_to_tensor(__UpperCAmelCase			)
					if text_pair is not None:
									if text.shape.rank > 1:
													raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''			)
									if text_pair.shape.rank > 1:
													raise ValueError('''text_pair should not be multidimensional!'''			)
					if text.shape.rank == 2:
									_A :				Dict				=  text[:, 0], text[:, 1]
					_A :				List[str]				=  self.unpaired_tokenize(__UpperCAmelCase			)
					if text_pair is None:  # Unpaired text
									if truncation:
													_A :				Tuple				=  text[:, : max_length - 2]  # Allow room for special tokens
									_A :				List[Any]				=  combine_segments(
									    (text,)   ,     start_of_sequence_id=self.cls_token_id   ,     end_of_segment_id=self.sep_token_id			)
					else:  # Paired text
									_A :				Union[str, Any]				=  self.unpaired_tokenize(__UpperCAmelCase			)
									if truncation:
													_A :				List[str]				=  self.paired_trimmer.trim([text, text_pair]			)
									_A :				int				=  combine_segments(
									    (text, text_pair)   ,     start_of_sequence_id=self.cls_token_id   ,     end_of_segment_id=self.sep_token_id			)
					if padding == "longest":
									_A :				Tuple				=  input_ids.bounding_shape(axis=1			)
									if pad_to_multiple_of is not None:
													# No ceiling division in tensorflow, so we negate floordiv instead
													_A :				str				=  pad_to_multiple_of * (-tf.math.floordiv(-pad_length   ,     __UpperCAmelCase			))
					else:
									_A :				Tuple				=  max_length

					_A :				Tuple				=  pad_model_inputs(__UpperCAmelCase   ,     max_seq_length=__UpperCAmelCase   ,     pad_value=self.pad_token_id			)
					_A :				Any				=  {'''input_ids''': input_ids}
					if return_attention_mask:
									_A :				Optional[int]				=  attention_mask
					if return_token_type_ids:
									_A :				int				=  pad_model_inputs(
									    __UpperCAmelCase   ,     max_seq_length=__UpperCAmelCase   ,     pad_value=self.pad_token_id			)
									_A :				int				=  token_type_ids
					return output




	def 				_lowerCamelCase				(					self			)       ->				str:
					return {
					    "vocab_list": self.vocab_list,
					    "do_lower_case": self.do_lower_case,
					    "cls_token_id": self.cls_token_id,
					    "sep_token_id": self.sep_token_id,
					    "pad_token_id": self.pad_token_id,
					}

"""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        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				def   lowercase_		(    self    :Optional[Any]      ,__UpperCAmelCase    :Dict  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					list(tokenizer_class.max_model_input_sizes.keys()  )
									for model_name in sa_models[:1]:
														lowerCamelCase__		:   Tuple  				=					tokenizer_class.from_pretrained(__UpperCAmelCase  )
														self.assertIsNotNone(__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
														self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )

														for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )
																			lowerCamelCase__		:   str  				=					tokenizer.convert_tokens_to_ids(__UpperCAmelCase  )
																			self.assertIsInstance(__UpperCAmelCase      ,__UpperCAmelCase  )







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :BatchEncoding      ,__UpperCAmelCase    :Optional[Callable] = None  )      ->					Optional[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[str]  				=					pickle.dumps(__UpperCAmelCase  )
									self.assertIsNotNone(__UpperCAmelCase  )

									lowerCamelCase__		:   int  				=					pickle.loads(__UpperCAmelCase  )

									# 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(__UpperCAmelCase      ,__UpperCAmelCase  )  )
														else:
																			self.assertEqual(__UpperCAmelCase      ,__UpperCAmelCase  )







				@slow
				def   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									self.check_tokenizer_from_pretrained(__UpperCAmelCase  )







				def   lowercase_		(    self    :Optional[int]  )      ->					int:




									"""simple docstring"""



									self.assertEqual(TensorType('''tf'''  )      ,TensorType.TENSORFLOW  )
									self.assertEqual(TensorType('''pt'''  )      ,TensorType.PYTORCH  )
									self.assertEqual(TensorType('''np'''  )      ,TensorType.NUMPY  )







				@require_tokenizers
				def   lowercase_		(    self    :List[Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									import numpy as np

									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Optional[int]  				=					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   lowercase_		(    self    :Optional[int]  )      ->					int:




									"""simple docstring"""



									import tensorflow as tf

									def tf_array_equals(__UpperCAmelCase    :List[Any]      ,__UpperCAmelCase    :List[Any]  ):
														return tf.reduce_all(tf.equal(__UpperCAmelCase      ,__UpperCAmelCase  )  )

									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[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  )      ,__UpperCAmelCase  )

									with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''  ):
														self.assert_dump_and_restore(
														    tokenizer_r('''Small example to encode'''      ,return_tensors=TensorType.TENSORFLOW  )      ,__UpperCAmelCase  )







				@require_torch
				@require_tokenizers
				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Optional[int]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   List[str]  				=					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   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   Any  				=					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   lowercase_		(    self    :List[str]  )      ->					str:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizerFast.from_pretrained('''bert-base-cased'''  )
									lowerCamelCase__		:   str  				=					tokenizer_r(['''Test''', '''\xad''', '''test''']      ,is_split_into_words=__UpperCAmelCase  )

									self.assertEqual(encoded.word_to_tokens(0  )      ,TokenSpan(start=1      ,end=2  )  )
									self.assertEqual(encoded.word_to_tokens(1  )      ,__UpperCAmelCase  )
									self.assertEqual(encoded.word_to_tokens(2  )      ,TokenSpan(start=2      ,end=3  )  )







				def   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Tuple  				=					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:
														lowerCamelCase__		:   List[str]  				=					batch.convert_to_tensors(tensor_type='''np'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Union[str, Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''np'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_torch
				def   lowercase_		(    self    :Optional[int]  )      ->					List[Any]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   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:
														lowerCamelCase__		:   Optional[int]  				=					batch.convert_to_tensors(tensor_type='''pt'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   Optional[int]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''pt'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_tf
				def   lowercase_		(    self    :Optional[Any]  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   Union[str, Any]  				=					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:
														lowerCamelCase__		:   Optional[Any]  				=					batch.convert_to_tensors(tensor_type='''tf'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   int  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Dict  				=					batch.convert_to_tensors(tensor_type='''tf'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				@require_flax
				def   lowercase_		(    self    :Optional[int]  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}  )
									lowerCamelCase__		:   str  				=					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:
														lowerCamelCase__		:   Any  				=					batch.convert_to_tensors(tensor_type='''jax'''  )
									self.assertFalse(len(cs.err  )      ,msg=F"""should have no warning, but got {cs.err}"""  )

									lowerCamelCase__		:   List[Any]  				=					BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}  )
									lowerCamelCase__		:   Tuple  				=					batch.convert_to_tensors(tensor_type='''jax'''      ,prepend_batch_axis=__UpperCAmelCase  )
									self.assertEqual(tensor_batch['''inputs'''].shape      ,(1, 3)  )
									self.assertEqual(tensor_batch['''labels'''].shape      ,(1,)  )







				def   lowercase_		(    self    :str  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					[{'''input_ids''': np.array([0, 1, 2]  )}, {'''input_ids''': np.array([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Optional[Any]  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Dict  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									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]]  )
									lowerCamelCase__		:   Optional[Any]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,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   lowercase_		(    self    :Tuple  )      ->					List[str]:




									"""simple docstring"""



									import torch

									lowerCamelCase__		:   Union[str, Any]  				=					[{'''input_ids''': torch.tensor([0, 1, 2]  )}, {'''input_ids''': torch.tensor([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   Any  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									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]]  )
									lowerCamelCase__		:   Optional[int]  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,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   lowercase_		(    self    :int  )      ->					Optional[int]:




									"""simple docstring"""



									import tensorflow as tf

									lowerCamelCase__		:   List[Any]  				=					[{'''input_ids''': tf.constant([0, 1, 2]  )}, {'''input_ids''': tf.constant([0, 1, 2, 3]  )}]
									lowerCamelCase__		:   str  				=					BertTokenizer.from_pretrained('''bert-base-cased'''  )

									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase  )
									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]]  )
									lowerCamelCase__		:   Tuple  				=					tokenizer.pad(__UpperCAmelCase      ,padding=__UpperCAmelCase      ,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   lowercase_		(    self    :Dict  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase  )







				@require_tokenizers
				def   lowercase_		(    self    :str  )      ->					Union[str, Any]:




									"""simple docstring"""



									lowerCamelCase__		:   int  				=					Tokenizer(WordPiece(unk_token='''[UNK]'''  )  )
									with tempfile.TemporaryDirectory() as tmpdirname:
														bert_tokenizer.save(os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )
														PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCAmelCase      ,'''tokenizer.json'''  )  )






