
"""simple docstring"""






from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
				from transformers import (
				    TFAutoModel,
				    TFAutoModelForCausalLM,
				    TFAutoModelForMaskedLM,
				    TFAutoModelForPreTraining,
				    TFAutoModelForQuestionAnswering,
				    TFAutoModelForSeqaSeqLM,
				    TFAutoModelForSequenceClassification,
				    TFAutoModelForTableQuestionAnswering,
				    TFAutoModelForTokenClassification,
				    TFAutoModelWithLMHead,
				    TFBertForMaskedLM,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertModel,
				    TFFunnelBaseModel,
				    TFFunnelModel,
				    TFGPTaLMHeadModel,
				    TFRobertaForMaskedLM,
				    TFTaForConditionalGeneration,
				    TFTapasForQuestionAnswering,
				)
				from transformers.models.auto.modeling_tf_auto import (
				    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_MASKED_LM_MAPPING,
				    TF_MODEL_FOR_PRETRAINING_MAPPING,
				    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    TF_MODEL_MAPPING,
				)
				from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
				from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
				from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
				from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST




class 			_SCREAMING_SNAKE_CASE					(			UpperCamelCase_							):
			UpperCAmelCase_  :int				 =						"new-model"


if is_tf_available():




				class 			_SCREAMING_SNAKE_CASE					(			UpperCamelCase_							):
							UpperCAmelCase_  :Tuple				 =						NewModelConfig




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			@slow
			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :int			     =  '''bert-base-cased'''
						lowerCAmelCase_    :Optional[int]			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
						self.assertIsNotNone(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

						lowerCAmelCase_    :str			     =  TFAutoModel.from_pretrained(UpperCamelCase__       )
						self.assertIsNotNone(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :str			     =  '''bert-base-cased'''
						lowerCAmelCase_    :str			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
						self.assertIsNotNone(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

						lowerCAmelCase_    :Optional[Any]			     =  TFAutoModelForPreTraining.from_pretrained(UpperCamelCase__       )
						self.assertIsNotNone(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Tuple:





						for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_    :Any			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

									lowerCAmelCase_    :Union[str, Any]			     =  TFAutoModelForCausalLM.from_pretrained(UpperCamelCase__       )
									lowerCAmelCase_    :List[str]			     =  TFAutoModelForCausalLM.from_pretrained(UpperCamelCase__       ,   output_loading_info=UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Tuple:





						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_    :str			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

									lowerCAmelCase_    :Any			     =  TFAutoModelWithLMHead.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Optional[Any]:





						for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_    :List[Any]			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

									lowerCAmelCase_    :Dict			     =  TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase__       )
									lowerCAmelCase_    :List[Any]			     =  TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase__       ,   output_loading_info=UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			@slow
			def  __lowerCAmelCase (					self       )			->     List[Any]:





						for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_    :Any			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

									lowerCAmelCase_    :List[str]			     =  TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__       )
									lowerCAmelCase_    :Any			     =  TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__       ,   output_loading_info=UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






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





						for model_name in ["bert-base-uncased"]:
									lowerCAmelCase_    :List[str]			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

									lowerCAmelCase_    :List[str]			     =  TFAutoModelForSequenceClassification.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			@slow
			def  __lowerCAmelCase (					self       )			->     int:





						for model_name in ["bert-base-uncased"]:
									lowerCAmelCase_    :Optional[int]			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

									lowerCAmelCase_    :List[str]			     =  TFAutoModelForQuestionAnswering.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			@slow
			@require_tensorflow_probability
			def  __lowerCAmelCase (					self       )			->     Any:





						for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
									lowerCAmelCase_    :int			     =  AutoConfig.from_pretrained(UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

									lowerCAmelCase_    :Optional[int]			     =  TFAutoModelForTableQuestionAnswering.from_pretrained(UpperCamelCase__       )
									lowerCAmelCase_    :Union[str, Any]			     =  TFAutoModelForTableQuestionAnswering.from_pretrained(
									    UpperCamelCase__       ,   output_loading_info=UpperCamelCase__       )
									self.assertIsNotNone(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :int			     =  TFAutoModelWithLMHead.from_pretrained(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )
						self.assertEqual(model.num_parameters()       ,   1_4410       )
						self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase__       )       ,   1_4410       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Dict			     =  TFAutoModelWithLMHead.from_pretrained(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )
						self.assertEqual(model.num_parameters()       ,   1_4410       )
						self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase__       )       ,   1_4410       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Optional[int]			     =  TFAutoModel.from_pretrained("""sgugger/funnel-random-tiny"""       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

						lowerCAmelCase_    :List[str]			     =  copy.deepcopy(model.config       )
						lowerCAmelCase_    :Any			     =  ['''FunnelBaseModel''']
						lowerCAmelCase_    :Union[str, Any]			     =  TFAutoModel.from_config(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

						with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(UpperCamelCase__       )
									lowerCAmelCase_    :Tuple			     =  TFAutoModel.from_pretrained(UpperCamelCase__       )
									self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )






			def  __lowerCAmelCase (					self       )			->     Any:





						try:
									AutoConfig.register("""new-model"""       ,   UpperCamelCase__       )

									lowerCAmelCase_    :Optional[Any]			     =  [
									    TFAutoModel,
									    TFAutoModelForCausalLM,
									    TFAutoModelForMaskedLM,
									    TFAutoModelForPreTraining,
									    TFAutoModelForQuestionAnswering,
									    TFAutoModelForSequenceClassification,
									    TFAutoModelForTokenClassification,
									]

									for auto_class in auto_classes:
												with self.subTest(auto_class.__name__       ):
															# Wrong config class will raise an error
															with self.assertRaises(UpperCamelCase__       ):
																		auto_class.register(UpperCamelCase__       ,   UpperCamelCase__       )
															auto_class.register(UpperCamelCase__       ,   UpperCamelCase__       )
															# Trying to register something existing in the Transformers library will raise an error
															with self.assertRaises(UpperCamelCase__       ):
																		auto_class.register(UpperCamelCase__       ,   UpperCamelCase__       )

															# Now that the config is registered, it can be used as any other config with the auto-API
															lowerCAmelCase_    :List[str]			     =  BertModelTester(self       ).get_config()
															lowerCAmelCase_    :List[str]			     =  NewModelConfig(**tiny_config.to_dict()       )
															lowerCAmelCase_    :List[Any]			     =  auto_class.from_config(UpperCamelCase__       )
															self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

															with tempfile.TemporaryDirectory() as tmp_dir:
																		model.save_pretrained(UpperCamelCase__       )
																		lowerCAmelCase_    :str			     =  auto_class.from_pretrained(UpperCamelCase__       )
																		self.assertIsInstance(UpperCamelCase__       ,   UpperCamelCase__       )

						finally:
									if "new-model" in CONFIG_MAPPING._extra_content:
												del CONFIG_MAPPING._extra_content["new-model"]
									for mapping in (
									    TF_MODEL_MAPPING,
									    TF_MODEL_FOR_PRETRAINING_MAPPING,
									    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
									    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
									    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
									    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
									    TF_MODEL_FOR_MASKED_LM_MAPPING,
									):
												if NewModelConfig in mapping._extra_content:
															del mapping._extra_content[NewModelConfig]






			def  __lowerCAmelCase (					self       )			->     int:





						with self.assertRaisesRegex(
						    UpperCamelCase__       ,   """bert-base is not a local folder and is not a valid model identifier"""       ):
									lowerCAmelCase_    :Any			     =  TFAutoModel.from_pretrained("""bert-base"""       )






			def  __lowerCAmelCase (					self       )			->     List[str]:





						with self.assertRaisesRegex(
						    UpperCamelCase__       ,   r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""       ):
									lowerCAmelCase_    :Union[str, Any]			     =  TFAutoModel.from_pretrained(UpperCamelCase__       ,   revision="""aaaaaa"""       )






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





						with self.assertRaisesRegex(
						    UpperCamelCase__       ,   """hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"""       ,   ):
									lowerCAmelCase_    :Union[str, Any]			     =  TFAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""       )






			def  __lowerCAmelCase (					self       )			->     Tuple:





						with self.assertRaisesRegex(UpperCamelCase__       ,   """Use `from_pt=True` to load this model"""       ):
									lowerCAmelCase_    :int			     =  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""       )






			def  __lowerCAmelCase (					self       )			->     List[Any]:
						lowerCAmelCase_    :List[str]			     =  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""       )
						with RequestCounter() as counter:
									lowerCAmelCase_    :Optional[Any]			     =  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""       )
									self.assertEqual(counter.get_request_count       ,   0       )
									self.assertEqual(counter.head_request_count       ,   1       )
									self.assertEqual(counter.other_request_count       ,   0       )

						# With a sharded checkpoint
						lowerCAmelCase_    :List[str]			     =  TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""       )
						with RequestCounter() as counter:
									lowerCAmelCase_    :Optional[int]			     =  TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""       )
									self.assertEqual(counter.get_request_count       ,   0       )
									self.assertEqual(counter.head_request_count       ,   1       )
									self.assertEqual(counter.other_request_count       ,   0       )







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_keras_nlp_available():
					from transformers.models.gpta import TFGPTaTokenizer


__UpperCamelCase		:       Optional[Any]									=     ['gpt2']
__UpperCamelCase		:       str									=     'gpt2'

if is_tf_available():





					class 					lowercase__						(   tf.Module):


											def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





																	'''simple docstring'''





																	super().__init__()
																	SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer
																	SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Dict							=							TFGPTaLMHeadModel.from_config(UpperCamelCase__						)



											@tf.function(input_signature=(tf.TensorSpec((None,)    ,		tf.string    ,		name='''text'''						),)						)
											def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.tokenizer(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenized['''input_ids'''].to_tensor()

																	SCREAMING_SNAKE_CASE							:    Any							=							tf.cast(input_ids_dense > 0    ,		tf.intaa						)
																	# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																	SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model(input_ids=UpperCamelCase__    ,		attention_mask=UpperCamelCase__						)['''logits''']

																	return outputs





@require_tf
@require_keras_nlp
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				int						):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[GPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in (TOKENIZER_CHECKPOINTS)]
												SCREAMING_SNAKE_CASE							:    List[str]							=							[TFGPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in TOKENIZER_CHECKPOINTS]
												assert len(self.tokenizers						) == len(self.tf_tokenizers						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							[
												    '''This is a straightforward English test sentence.''',
												    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
												    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
												    '''And some much more rare Chinese: 齉 堃 齉堃''',
												    '''Je vais aussi écrire en français pour tester les accents''',
												    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
												]
												SCREAMING_SNAKE_CASE							:    Dict							=							list(zip(self.test_sentences    ,		self.test_sentences[::-1]						)						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												for tokenizer, tf_tokenizer in zip(self.tokenizers    ,		self.tf_tokenizers						):
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer([test_inputs]    ,		return_tensors='''tf'''						)
																								SCREAMING_SNAKE_CASE							:    Any							=							tf_tokenizer([test_inputs]						)

																								for key in python_outputs.keys():
																														# convert them to numpy to avoid messing with ragged tensors
																														SCREAMING_SNAKE_CASE							:    int							=							python_outputs[key].numpy()
																														SCREAMING_SNAKE_CASE							:    int							=							tf_outputs[key].numpy()

																														self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape						)						)
																														self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__    ,		tf.intaa						) == tf_outputs_values						)						)


						@slow
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.function(UpperCamelCase__						)
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.constant(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[str]							=							compiled_tokenizer(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							tf_tokenizer(UpperCamelCase__						)

																								for key in eager_outputs.keys():
																														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)


						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    str							=							ModelToSave(tokenizer=UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model.serving(UpperCamelCase__						)  # Build model with some sample inputs
																		with TemporaryDirectory() as tempdir:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							Path(UpperCamelCase__						) / '''saved.model'''
																								tf.saved_model.save(UpperCamelCase__    ,		UpperCamelCase__    ,		signatures={'''serving_default''': model.serving}						)
																								SCREAMING_SNAKE_CASE							:    str							=							tf.saved_model.load(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    int							=							loaded_model.signatures['''serving_default'''](UpperCamelCase__						)['''output_0''']
																		# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																		self.assertTrue(tf.reduce_all(out == loaded_output						)						)


						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__						)  # Build model with some sample inputs

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tf_tokenizer.get_config()
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TFGPTaTokenizer.from_config(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							model_from_config(UpperCamelCase__						)

																		for key in from_config_output.keys():
																								self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]						)						)



						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		SCREAMING_SNAKE_CASE							:    Tuple							=							12_3123

																		for max_length in [3, 5, 1024]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																								SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__    ,		max_length=UpperCamelCase__						)

																								SCREAMING_SNAKE_CASE							:    Tuple							=							out['''input_ids'''].numpy().shape[1]

																								assert out_length == max_length





