
"""simple docstring"""






from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


UpperCAmelCase          =							{
    'id': '5733be284776f41900661182',
    'title': 'University_of_Notre_Dame',
    'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
    'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
    'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [5_1_5]},
}




@pytest.mark.parametrize(
    '''task_cls''',				[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ],				)
def __magic_name__  (							_lowerCamelCase:		int )				->	str:

 '''simple docstring'''




 lowerCAmelCase				       =     task_cls()
 lowerCAmelCase				       =     asdict(_SCREAMING_SNAKE_CASE )
 lowerCAmelCase				       =     task_template_from_dict(_SCREAMING_SNAKE_CASE )
 assert task == reloaded



class  lowercase				:






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





    """simple docstring"""


    lowerCAmelCase				       =     LanguageModeling(text_column='''input_text''' )
    assert {"input_text": "text"} == task.column_mapping






   def 				UpperCAmelCase       (self     :  List[Any] )					->  Any:





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''text''': Value('''string''' )} )
    lowerCAmelCase				       =     {'text_column': 'input_text'}
    lowerCAmelCase				       =     LanguageModeling.from_dict(__a )
    assert "language-modeling" == task.task
    assert input_schema == task.input_schema





class  lowercase				(   lowerCAmelCase_		):






   def 				UpperCAmelCase       (self     :  str )					->  List[Any]:





    """simple docstring"""


    lowerCAmelCase				       =     sorted(['''pos''', '''neg'''] )






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





    """simple docstring"""


    lowerCAmelCase				       =     TextClassification(text_column='''input_text'''			,label_column='''input_label''' )
    self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}			,task.column_mapping )






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





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''text''': Value('''string''' )} )
    # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
    lowerCAmelCase				       =     Features({'''labels''': ClassLabel} )
    lowerCAmelCase				       =     {'text_column': 'input_text', 'label_column': 'input_labels'}
    lowerCAmelCase				       =     TextClassification.from_dict(__a )
    self.assertEqual('''text-classification'''			,task.task )
    self.assertEqual(__a			,task.input_schema )
    self.assertEqual(__a			,task.label_schema )






   def 				UpperCAmelCase       (self     :  Tuple )					->  int:





    """simple docstring"""


    lowerCAmelCase				       =     TextClassification(text_column='''input_text'''			,label_column='''input_label''' )
    self.assertEqual(task.label_schema['''labels''']			,__a )
    lowerCAmelCase				       =     task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels )} ) )
    self.assertEqual(task.label_schema['''labels''']			,ClassLabel(names=self.labels ) )





class  lowercase				(   lowerCAmelCase_		):






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





    """simple docstring"""


    lowerCAmelCase				       =     QuestionAnsweringExtractive(
        context_column='''input_context'''			,question_column='''input_question'''			,answers_column='''input_answers''' )
    self.assertDictEqual(
        {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}			,task.column_mapping )






   def 				UpperCAmelCase       (self     :  str )					->  Optional[int]:





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
    lowerCAmelCase				       =     Features(
        {
            '''answers''': Sequence(
                {
                    '''text''': Value('''string''' ),
                    '''answer_start''': Value('''int32''' ),
                } )
        } )
    lowerCAmelCase				       =     {
        'context_column': 'input_input_context',
        'question_column': 'input_question',
        'answers_column': 'input_answers',
    }
    lowerCAmelCase				       =     QuestionAnsweringExtractive.from_dict(__a )
    self.assertEqual('''question-answering-extractive'''			,task.task )
    self.assertEqual(__a			,task.input_schema )
    self.assertEqual(__a			,task.label_schema )





class  lowercase				(   lowerCAmelCase_		):






   def 				UpperCAmelCase       (self     :  List[str] )					->  int:





    """simple docstring"""


    lowerCAmelCase				       =     Summarization(text_column='''input_text'''			,summary_column='''input_summary''' )
    self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}			,task.column_mapping )






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





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''text''': Value('''string''' )} )
    lowerCAmelCase				       =     Features({'''summary''': Value('''string''' )} )
    lowerCAmelCase				       =     {'text_column': 'input_text', 'summary_column': 'input_summary'}
    lowerCAmelCase				       =     Summarization.from_dict(__a )
    self.assertEqual('''summarization'''			,task.task )
    self.assertEqual(__a			,task.input_schema )
    self.assertEqual(__a			,task.label_schema )





class  lowercase				(   lowerCAmelCase_		):






   def 				UpperCAmelCase       (self     :  Optional[Any] )					->  List[str]:





    """simple docstring"""


    lowerCAmelCase				       =     AutomaticSpeechRecognition(audio_column='''input_audio'''			,transcription_column='''input_transcription''' )
    self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}			,task.column_mapping )






   def 				UpperCAmelCase       (self     :  Tuple )					->  List[Any]:





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''audio''': Audio()} )
    lowerCAmelCase				       =     Features({'''transcription''': Value('''string''' )} )
    lowerCAmelCase				       =     {
        'audio_column': 'input_audio',
        'transcription_column': 'input_transcription',
    }
    lowerCAmelCase				       =     AutomaticSpeechRecognition.from_dict(__a )
    self.assertEqual('''automatic-speech-recognition'''			,task.task )
    self.assertEqual(__a			,task.input_schema )
    self.assertEqual(__a			,task.label_schema )





class  lowercase				(   lowerCAmelCase_		):






   def 				UpperCAmelCase       (self     :  List[str] )					->  Tuple:





    """simple docstring"""


    lowerCAmelCase				       =     sorted(['''pos''', '''neg'''] )






   def 				UpperCAmelCase       (self     :  Optional[int] )					->  Union[str, Any]:





    """simple docstring"""


    lowerCAmelCase				       =     AudioClassification(audio_column='''input_audio'''			,label_column='''input_label''' )
    self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}			,task.column_mapping )






   def 				UpperCAmelCase       (self     :  Dict )					->  List[str]:





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''audio''': Audio()} )
    lowerCAmelCase				       =     Features({'''labels''': ClassLabel} )
    lowerCAmelCase				       =     {
        'audio_column': 'input_image',
        'label_column': 'input_label',
    }
    lowerCAmelCase				       =     AudioClassification.from_dict(__a )
    self.assertEqual('''audio-classification'''			,task.task )
    self.assertEqual(__a			,task.input_schema )
    self.assertEqual(__a			,task.label_schema )






   def 				UpperCAmelCase       (self     :  Any )					->  int:





    """simple docstring"""


    lowerCAmelCase				       =     AudioClassification(audio_column='''input_audio'''			,label_column='''input_label''' )
    self.assertEqual(task.label_schema['''labels''']			,__a )
    lowerCAmelCase				       =     task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels )} ) )
    self.assertEqual(task.label_schema['''labels''']			,ClassLabel(names=self.labels ) )





class  lowercase				(   lowerCAmelCase_		):






   def 				UpperCAmelCase       (self     :  Any )					->  Any:





    """simple docstring"""


    lowerCAmelCase				       =     sorted(['''pos''', '''neg'''] )






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





    """simple docstring"""


    lowerCAmelCase				       =     ImageClassification(image_column='''input_image'''			,label_column='''input_label''' )
    self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}			,task.column_mapping )






   def 				UpperCAmelCase       (self     :  Any )					->  Tuple:





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''image''': Image()} )
    lowerCAmelCase				       =     Features({'''labels''': ClassLabel} )
    lowerCAmelCase				       =     {
        'image_column': 'input_image',
        'label_column': 'input_label',
    }
    lowerCAmelCase				       =     ImageClassification.from_dict(__a )
    self.assertEqual('''image-classification'''			,task.task )
    self.assertEqual(__a			,task.input_schema )
    self.assertEqual(__a			,task.label_schema )






   def 				UpperCAmelCase       (self     :  Any )					->  List[Any]:





    """simple docstring"""


    lowerCAmelCase				       =     ImageClassification(image_column='''input_image'''			,label_column='''input_label''' )
    self.assertEqual(task.label_schema['''labels''']			,__a )
    lowerCAmelCase				       =     task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels )} ) )
    self.assertEqual(task.label_schema['''labels''']			,ClassLabel(names=self.labels ) )





class  lowercase				(   lowerCAmelCase_		):






   def 				UpperCAmelCase       (self     :  Optional[Any] )					->  List[str]:





    """simple docstring"""


    lowerCAmelCase				       =     DatasetInfo(task_templates=QuestionAnsweringExtractive() )
    lowerCAmelCase				       =     Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}			,info=__a )
    assert isinstance(dataset.info.task_templates			,__a )
    assert len(dataset.info.task_templates ) == 1

    def keep_task(SCREAMING_SNAKE_CASE_     :  Optional[Any] ):
     return x

    def dont_keep_task(SCREAMING_SNAKE_CASE_     :  Dict ):
     lowerCAmelCase				       =     deepcopy(__a )
     lowerCAmelCase				       =     0
     return out

    lowerCAmelCase				       =     dataset.map(__a )
    assert mapped_dataset.info.task_templates == dataset.info.task_templates
    # reload from cache
    lowerCAmelCase				       =     dataset.map(__a )
    assert mapped_dataset.info.task_templates == dataset.info.task_templates

    lowerCAmelCase				       =     dataset.map(__a )
    assert mapped_dataset.info.task_templates == []
    # reload from cache
    lowerCAmelCase				       =     dataset.map(__a )
    assert mapped_dataset.info.task_templates == []






   def 				UpperCAmelCase       (self     :  Optional[int] )					->  Optional[Any]:





    """simple docstring"""


    lowerCAmelCase				       =     Features({'''text''': Value('''string''' ), '''label''': ClassLabel(names=('''pos''', '''neg''') )} )
    lowerCAmelCase				       =     TextClassification(text_column='''text'''			,label_column='''label''' )
    lowerCAmelCase				       =     DatasetInfo(features=__a			,task_templates=__a )
    lowerCAmelCase				       =     Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}			,info=__a )

    def process(SCREAMING_SNAKE_CASE_     :  Any ):
     return example

    lowerCAmelCase				       =     dataset.remove_columns('''label''' )
    lowerCAmelCase				       =     modified_dataset.map(__a )
    assert mapped_dataset.info.task_templates == []




"""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  lowercase				(   unittest.TestCase		):






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  Dict )					->  int:





				"""simple docstring"""


				lowerCAmelCase				       =     list(tokenizer_class.max_model_input_sizes.keys() )
				for model_name in sa_models[:1]:
					lowerCAmelCase				       =     tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					for special_tok in tokenizer.all_special_tokens:
						self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
						lowerCAmelCase				       =     tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
						self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  BatchEncoding			,SCREAMING_SNAKE_CASE_     :  Optional[Callable] = None )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     pickle.dumps(SCREAMING_SNAKE_CASE_ )
				self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     pickle.loads(SCREAMING_SNAKE_CASE_ )

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






			@slow
			def 				UpperCAmelCase       (self     :  List[str] )					->  int:





				"""simple docstring"""


				self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str )					->  Dict:





				"""simple docstring"""


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






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  int )					->  Tuple:





				"""simple docstring"""


				import numpy as np

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 				UpperCAmelCase       (self     :  str )					->  int:





				"""simple docstring"""


				import tensorflow as tf

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

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 )			,SCREAMING_SNAKE_CASE_ )

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






			@require_torch
			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Union[str, Any]:





				"""simple docstring"""


				import torch

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""simple docstring"""


				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 				UpperCAmelCase       (self     :  Any )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     BertTokenizerFast.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     tokenizer_r(['''Test''', '''\xad''', '''test''']			,is_split_into_words=SCREAMING_SNAKE_CASE_ )

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






			def 				UpperCAmelCase       (self     :  List[Any] )					->  int:





				"""simple docstring"""


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

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''np'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_torch
			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""simple docstring"""


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

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''pt'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_tf
			def 				UpperCAmelCase       (self     :  List[Any] )					->  List[Any]:





				"""simple docstring"""


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

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






			@require_flax
			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""simple docstring"""


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

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''jax'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






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





				"""simple docstring"""


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

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				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				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,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 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""simple docstring"""


				import torch

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

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				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				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,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 				UpperCAmelCase       (self     :  Tuple )					->  Any:





				"""simple docstring"""


				import tensorflow as tf

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

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				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				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,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 				UpperCAmelCase       (self     :  Union[str, Any] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
				PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_ )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Tuple )					->  Optional[Any]:





				"""simple docstring"""


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


