


"""simple docstring"""






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     int				  =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__magic_name__					:     int				  =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




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







								'''simple docstring'''




								if isinstance(model.config	,					lowerCamelCase__):
									snake_case__,					snake_case__  :						str		=      list(tokenizer.lang_code_to_id.keys())[:2]
									snake_case__  :						Union[str, Any]		=      TranslationPipeline(model=lowerCamelCase__	,					tokenizer=lowerCamelCase__	,					src_lang=lowerCamelCase__	,					tgt_lang=lowerCamelCase__)
								else:
									snake_case__  :						Any		=      TranslationPipeline(model=lowerCamelCase__	,					tokenizer=lowerCamelCase__)
								return translator, ["Some string", "Some other text"]




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      translator("Some string")
								self.assertEqual(lowerCamelCase__	,					[{"translation_text": ANY(lowerCamelCase__)}])

								snake_case__  :						Union[str, Any]		=      translator(["Some string"])
								self.assertEqual(lowerCamelCase__	,					[{"translation_text": ANY(lowerCamelCase__)}])

								snake_case__  :						Optional[Any]		=      translator(["Some string", "other string"])
								self.assertEqual(lowerCamelCase__	,					[{"translation_text": ANY(lowerCamelCase__)}, {"translation_text": ANY(lowerCamelCase__)}])




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







								'''simple docstring'''




								snake_case__  :						Dict		=      pipeline("translation_en_to_ro"	,					model="patrickvonplaten/t5-tiny-random"	,					framework="pt")
								snake_case__  :						Optional[Any]		=      translator("This is a test string"	,					max_length=20)
								self.assertEqual(
								    lowerCamelCase__	,					[
								        {
								            "translation_text": (
								                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
								                " Beide Beide"
								            )
								        }
								    ]	,					)




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







								'''simple docstring'''




								snake_case__  :						str		=      pipeline("translation_en_to_ro"	,					model="patrickvonplaten/t5-tiny-random"	,					framework="tf")
								snake_case__  :						Union[str, Any]		=      translator("This is a test string"	,					max_length=20)
								self.assertEqual(
								    lowerCamelCase__	,					[
								        {
								            "translation_text": (
								                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
								                " Beide Beide"
								            )
								        }
								    ]	,					)




							@require_torch
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      pipeline("translation_en_to_de"	,					model="patrickvonplaten/t5-tiny-random"	,					framework="pt")
								snake_case__  :						List[str]		=      translator("This is a test string"	,					max_length=20)
								self.assertEqual(
								    lowerCamelCase__	,					[
								        {
								            "translation_text": (
								                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
								                " urine urine urine urine urine urine urine"
								            )
								        }
								    ]	,					)







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







								'''simple docstring'''




								snake_case__  :						int		=      pipeline("translation_en_to_de"	,					model="patrickvonplaten/t5-tiny-random"	,					framework="tf")
								snake_case__  :						List[str]		=      translator("This is a test string"	,					max_length=20)
								self.assertEqual(
								    lowerCamelCase__	,					[
								        {
								            "translation_text": (
								                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
								                " urine urine urine urine urine urine urine"
								            )
								        }
								    ]	,					)


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@require_torch
							@slow
							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								with self.assertRaises(lowerCamelCase__):
									pipeline(task="translation_cn_to_ar")

								# but we do for this one
								snake_case__  :						str		=      pipeline(task="translation_en_to_de")
								self.assertEqual(translator._preprocess_params["src_lang"]	,					"en")
								self.assertEqual(translator._preprocess_params["tgt_lang"]	,					"de")




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







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
								snake_case__  :						Any		=      MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")

								snake_case__  :						Union[str, Any]		=      pipeline(task="translation"	,					model=lowerCamelCase__	,					tokenizer=lowerCamelCase__)
								# Missing src_lang, tgt_lang
								with self.assertRaises(lowerCamelCase__):
									translator("This is a test")

								snake_case__  :						Any		=      translator("This is a test"	,					src_lang="en_XX"	,					tgt_lang="ar_AR")
								self.assertEqual(lowerCamelCase__	,					[{"translation_text": "هذا إختبار"}])

								snake_case__  :						int		=      translator("This is a test"	,					src_lang="en_XX"	,					tgt_lang="hi_IN")
								self.assertEqual(lowerCamelCase__	,					[{"translation_text": "यह एक परीक्षण है"}])

								# src_lang, tgt_lang can be defined at pipeline call time
								snake_case__  :						Optional[Any]		=      pipeline(task="translation"	,					model=lowerCamelCase__	,					tokenizer=lowerCamelCase__	,					src_lang="en_XX"	,					tgt_lang="ar_AR")
								snake_case__  :						Union[str, Any]		=      translator("This is a test")
								self.assertEqual(lowerCamelCase__	,					[{"translation_text": "هذا إختبار"}])




							@require_torch
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      "patrickvonplaten/t5-tiny-random"
								snake_case__  :						Optional[Any]		=      pipeline(task="translation_cn_to_ar"	,					model=lowerCamelCase__)
								self.assertEqual(translator._preprocess_params["src_lang"]	,					"cn")
								self.assertEqual(translator._preprocess_params["tgt_lang"]	,					"ar")




							@require_torch
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      "patrickvonplaten/t5-tiny-random"
								with pytest.warns(lowerCamelCase__	,					match=R".*translation_en_to_de.*"):
									snake_case__  :						int		=      pipeline(task="translation"	,					model=lowerCamelCase__)
								self.assertEqual(translator.task	,					"translation_en_to_de")
								self.assertEqual(translator._preprocess_params["src_lang"]	,					"en")
								self.assertEqual(translator._preprocess_params["tgt_lang"]	,					"de")







							@require_torch
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								with self.assertRaises(lowerCamelCase__):
									pipeline(task="translation")






"""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'''  )  )






