





'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TFMobileBertForMaskedLM,
					    TFMobileBertForMultipleChoice,
					    TFMobileBertForNextSentencePrediction,
					    TFMobileBertForPreTraining,
					    TFMobileBertForQuestionAnswering,
					    TFMobileBertForSequenceClassification,
					    TFMobileBertForTokenClassification,
					    TFMobileBertModel,
					)
@require_tf
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =(
				    (
				        TFMobileBertModel,
				        TFMobileBertForMaskedLM,
				        TFMobileBertForNextSentencePrediction,
				        TFMobileBertForPreTraining,
				        TFMobileBertForQuestionAnswering,
				        TFMobileBertForSequenceClassification,
				        TFMobileBertForTokenClassification,
				        TFMobileBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__a            =(
				    {
				        'feature-extraction': TFMobileBertModel,
				        'fill-mask': TFMobileBertForMaskedLM,
				        'question-answering': TFMobileBertForQuestionAnswering,
				        'text-classification': TFMobileBertForSequenceClassification,
				        'token-classification': TFMobileBertForTokenClassification,
				        'zero-shot': TFMobileBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					str					,				__a     :					Tuple					,				__a     :					Optional[Any]					,				__a     :					List[str]=False					):
								_a								= super()._prepare_for_class(__a					,				__a					,				return_labels=__a					)

								if return_labels:
												if model_class in get_values(__a					):
																_a								= tf.zeros(self.model_tester.batch_size					,				dtype=tf.intaa					)

								return inputs_dict
				class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






								"""simple docstring"""







								def __init__(  self     :					List[str]					,				__a     :					int					,				__a     :					Union[str, Any]=13					,				__a     :					int=7					,				__a     :					List[str]=True					,				__a     :					str=True					,				__a     :					Union[str, Any]=True					,				__a     :					Union[str, Any]=True					,				__a     :					Tuple=99					,				__a     :					Any=32					,				__a     :					Tuple=32					,				__a     :					Any=2					,				__a     :					int=4					,				__a     :					Dict=37					,				__a     :					Union[str, Any]="gelu"					,				__a     :					Optional[Any]=0.1					,				__a     :					Union[str, Any]=0.1					,				__a     :					Dict=5_12					,				__a     :					int=16					,				__a     :					str=2					,				__a     :					Tuple=0.02					,				__a     :					Any=3					,				__a     :					Tuple=4					,				__a     :					Any=None					,				):
												_a								= parent
												_a								= batch_size
												_a								= seq_length
												_a								= is_training
												_a								= use_input_mask
												_a								= use_token_type_ids
												_a								= use_labels
												_a								= vocab_size
												_a								= hidden_size
												_a								= num_hidden_layers
												_a								= num_attention_heads
												_a								= intermediate_size
												_a								= hidden_act
												_a								= hidden_dropout_prob
												_a								= attention_probs_dropout_prob
												_a								= max_position_embeddings
												_a								= type_vocab_size
												_a								= type_sequence_label_size
												_a								= initializer_range
												_a								= num_labels
												_a								= num_choices
												_a								= scope
												_a								= embedding_size




								def   UpperCamelCase__   (  self     :					Any					):
												_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size					)

												_a								= None
												if self.use_input_mask:
																_a								= random_attention_mask([self.batch_size, self.seq_length]					)

												_a								= None
												if self.use_token_type_ids:
																_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size					)

												_a								= None
												_a								= None
												_a								= None
												if self.use_labels:
																_a								= ids_tensor([self.batch_size]					,				self.type_sequence_label_size					)
																_a								= ids_tensor([self.batch_size, self.seq_length]					,				self.num_labels					)
																_a								= ids_tensor([self.batch_size]					,				self.num_choices					)

												_a								= MobileBertConfig(
												    vocab_size=self.vocab_size					,				hidden_size=self.hidden_size					,				num_hidden_layers=self.num_hidden_layers					,				num_attention_heads=self.num_attention_heads					,				intermediate_size=self.intermediate_size					,				hidden_act=self.hidden_act					,				hidden_dropout_prob=self.hidden_dropout_prob					,				attention_probs_dropout_prob=self.attention_probs_dropout_prob					,				max_position_embeddings=self.max_position_embeddings					,				type_vocab_size=self.type_vocab_size					,				initializer_range=self.initializer_range					,				embedding_size=self.embedding_size					,				)

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




								def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Optional[Any]					,				__a     :					int					,				__a     :					Tuple					,				__a     :					str					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					str					):
												_a								= TFMobileBertModel(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)

												_a								= [input_ids, input_mask]
												_a								= model(__a					)

												_a								= model(__a					)

												self.parent.assertEqual(
												    result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)					)
												self.parent.assertEqual(result.pooler_output.shape					,				(self.batch_size, self.hidden_size)					)




								def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[str]					,				__a     :					Dict					,				__a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Dict					,				__a     :					List[Any]					,				__a     :					List[str]					):
												_a								= TFMobileBertForMaskedLM(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)					)




								def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Dict					,				__a     :					Dict					,				__a     :					Any					,				__a     :					Any					,				__a     :					str					,				__a     :					Tuple					,				__a     :					str					):
												_a								= TFMobileBertForNextSentencePrediction(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, 2)					)




								def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					Tuple					,				__a     :					Optional[Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					List[str]					,				__a     :					Optional[Any]					,				__a     :					List[Any]					):
												_a								= TFMobileBertForPreTraining(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(
												    result.prediction_logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)					)
												self.parent.assertEqual(result.seq_relationship_logits.shape					,				(self.batch_size, 2)					)




								def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[Any]					,				__a     :					str					,				__a     :					str					,				__a     :					Union[str, Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					,				__a     :					List[Any]					):
												_a								= self.num_labels
												_a								= TFMobileBertForSequenceClassification(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)					)




								def   UpperCamelCase__   (  self     :					Any					,				__a     :					str					,				__a     :					Any					,				__a     :					Optional[int]					,				__a     :					int					,				__a     :					Optional[int]					,				__a     :					int					,				__a     :					List[Any]					):
												_a								= self.num_choices
												_a								= TFMobileBertForMultipleChoice(config=__a					)
												_a								= tf.tile(tf.expand_dims(__a					,				1					)					,				(1, self.num_choices, 1)					)
												_a								= tf.tile(tf.expand_dims(__a					,				1					)					,				(1, self.num_choices, 1)					)
												_a								= tf.tile(tf.expand_dims(__a					,				1					)					,				(1, self.num_choices, 1)					)
												_a								= {
												    "input_ids": multiple_choice_inputs_ids,
												    "attention_mask": multiple_choice_input_mask,
												    "token_type_ids": multiple_choice_token_type_ids,
												}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_choices)					)




								def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[str]					,				__a     :					List[Any]					,				__a     :					Dict					,				__a     :					Dict					,				__a     :					int					,				__a     :					str					,				__a     :					List[str]					):
												_a								= self.num_labels
												_a								= TFMobileBertForTokenClassification(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.num_labels)					)




								def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					Tuple					,				__a     :					List[Any]					,				__a     :					str					,				__a     :					int					,				__a     :					Any					,				__a     :					int					,				__a     :					List[str]					):
												_a								= TFMobileBertForQuestionAnswering(config=__a					)
												_a								= {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
												_a								= model(__a					)
												self.parent.assertEqual(result.start_logits.shape					,				(self.batch_size, self.seq_length)					)
												self.parent.assertEqual(result.end_logits.shape					,				(self.batch_size, self.seq_length)					)




								def   UpperCamelCase__   (  self     :					Any					):
												_a								= self.prepare_config_and_inputs()
												(
												    (
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					(
												    _a
												)	,					
												)								= config_and_inputs
												_a								= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
												return config, inputs_dict




				def   UpperCamelCase__   (  self     :					str					):
								_a								= TFMobileBertModelTest.TFMobileBertModelTester(self					)
								_a								= ConfigTester(self					,				config_class=__a					,				hidden_size=37					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								self.config_tester.run_common_tests()




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*__a					)




				@slow
				def   UpperCamelCase__   (  self     :					List[str]					):
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
												_a								= TFMobileBertModel.from_pretrained(__a					)
												self.assertIsNotNone(__a					)


@require_tf
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				@slow
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased"					)
								_a								= tf.constant([[0, 1, 2, 3, 4, 5]]					)
								_a								= model(__a					)[0]

								_a								= [1, 6, 3_05_22]
								self.assertEqual(output.shape					,				__a					)

								_a								= tf.constant(
								    [
								        [
								            [-4.5919547, -9.248295, -9.645256],
								            [-6.7306175, -6.440284, -6.6052837],
								            [-7.2743506, -6.7847915, -6.024673],
								        ]
								    ]					)
								tf.debugging.assert_near(output[:, :3, :3]					,				__a					,				atol=1e-4					)




"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def   UpperCAmelCase_      ( UpperCAmelCase__			:				ArgumentParser   )       ->	int:
						raise NotImplementedError()




	@abstractmethod
	def   UpperCAmelCase_      ( self			:				int   )       ->	Optional[int]:
						raise NotImplementedError()
