
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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			unittest.TestCase   ):







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





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=False		):
					A__  :	List[str]         =							super()._prepare_for_class(UpperCamelCase__				,       UpperCamelCase__				,       return_labels=UpperCamelCase__		)

					if return_labels:
									if model_class in get_values(UpperCamelCase__		):
													A__  :	Tuple         =							tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa		)

					return inputs_dict







	class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







		'''simple docstring'''





		def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__=13				,       UpperCamelCase__=7				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=True				,       UpperCamelCase__=99				,       UpperCamelCase__=32				,       UpperCamelCase__=32				,       UpperCamelCase__=2				,       UpperCamelCase__=4				,       UpperCamelCase__=37				,       UpperCamelCase__="gelu"				,       UpperCamelCase__=0.1				,       UpperCamelCase__=0.1				,       UpperCamelCase__=512				,       UpperCamelCase__=16				,       UpperCamelCase__=2				,       UpperCamelCase__=0.0_2				,       UpperCamelCase__=3				,       UpperCamelCase__=4				,       UpperCamelCase__=None				,       ):
						A__  :	Tuple         =							parent
						A__  :	Tuple         =							batch_size
						A__  :	Tuple         =							seq_length
						A__  :	Union[str, Any]         =							is_training
						A__  :	Any         =							use_input_mask
						A__  :	Union[str, Any]         =							use_token_type_ids
						A__  :	Optional[Any]         =							use_labels
						A__  :	Dict         =							vocab_size
						A__  :	Union[str, Any]         =							hidden_size
						A__  :	str         =							num_hidden_layers
						A__  :	List[str]         =							num_attention_heads
						A__  :	List[str]         =							intermediate_size
						A__  :	Union[str, Any]         =							hidden_act
						A__  :	int         =							hidden_dropout_prob
						A__  :	List[str]         =							attention_probs_dropout_prob
						A__  :	Tuple         =							max_position_embeddings
						A__  :	Union[str, Any]         =							type_vocab_size
						A__  :	List[str]         =							type_sequence_label_size
						A__  :	Union[str, Any]         =							initializer_range
						A__  :	List[Any]         =							num_labels
						A__  :	List[str]         =							num_choices
						A__  :	Dict         =							scope
						A__  :	List[Any]         =							embedding_size





		def   __snake_case				(			self		):
						A__  :	str         =							ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size		)

						A__  :	Optional[Any]         =							None
						if self.use_input_mask:
										A__  :	List[Any]         =							random_attention_mask([self.batch_size, self.seq_length]		)

						A__  :	Optional[int]         =							None
						if self.use_token_type_ids:
										A__  :	str         =							ids_tensor([self.batch_size, self.seq_length]				,       self.type_vocab_size		)

						A__  :	Optional[int]         =							None
						A__  :	Union[str, Any]         =							None
						A__  :	Optional[int]         =							None
						if self.use_labels:
										A__  :	Optional[int]         =							ids_tensor([self.batch_size]				,       self.type_sequence_label_size		)
										A__  :	List[Any]         =							ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels		)
										A__  :	Any         =							ids_tensor([self.batch_size]				,       self.num_choices		)

						A__  :	Dict         =							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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
						A__  :	str         =							TFMobileBertModel(config=UpperCamelCase__		)
						A__  :	Optional[int]         =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
						A__  :	List[str]         =							model(UpperCamelCase__		)

						A__  :	List[Any]         =							[input_ids, input_mask]
						A__  :	Optional[Any]         =							model(UpperCamelCase__		)

						A__  :	Tuple         =							model(UpperCamelCase__		)

						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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
						A__  :	int         =							TFMobileBertForMaskedLM(config=UpperCamelCase__		)
						A__  :	Any         =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
						A__  :	List[str]         =							model(UpperCamelCase__		)
						self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)		)





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





		def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
						A__  :	int         =							TFMobileBertForPreTraining(config=UpperCamelCase__		)
						A__  :	List[str]         =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
						A__  :	int         =							model(UpperCamelCase__		)
						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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
						A__  :	str         =							self.num_labels
						A__  :	int         =							TFMobileBertForSequenceClassification(config=UpperCamelCase__		)
						A__  :	List[str]         =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
						A__  :	Union[str, Any]         =							model(UpperCamelCase__		)
						self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)		)





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





		def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
						A__  :	Tuple         =							self.num_labels
						A__  :	Optional[int]         =							TFMobileBertForTokenClassification(config=UpperCamelCase__		)
						A__  :	int         =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
						A__  :	Optional[Any]         =							model(UpperCamelCase__		)
						self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)		)





		def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
						A__  :	Dict         =							TFMobileBertForQuestionAnswering(config=UpperCamelCase__		)
						A__  :	str         =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
						A__  :	Optional[Any]         =							model(UpperCamelCase__		)
						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   __snake_case				(			self		):
						A__  :	List[str]         =							self.prepare_config_and_inputs()
						(
						    (
						    A__
						)       ,							(
						    A__
						)       ,							(
						    A__
						)       ,							(
						    A__
						)       ,							(
						    A__
						)       ,							(
						    A__
						)       ,							(
						    A__
						)       ,							
						)  :	Optional[int]         =							config_and_inputs
						A__  :	str         =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
						return config, inputs_dict





	def   __snake_case				(			self		):
					A__  :	str         =							TFMobileBertModelTest.TFMobileBertModelTester(self		)
					A__  :	Tuple         =							ConfigTester(self				,       config_class=UpperCamelCase__				,       hidden_size=37		)





	def   __snake_case				(			self		):
					self.config_tester.run_common_tests()





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





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





	def   __snake_case				(			self		):
					A__  :	int         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_multiple_choice(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_pretraining(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_question_answering(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_sequence_classification(*UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mobilebert_for_token_classification(*UpperCamelCase__		)





	@slow
	def   __snake_case				(			self		):
					# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					for model_name in ["google/mobilebert-uncased"]:
									A__  :	int         =							TFMobileBertModel.from_pretrained(UpperCamelCase__		)
									self.assertIsNotNone(UpperCamelCase__		)







@require_tf
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	@slow
	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''		)
					A__  :	Union[str, Any]         =							tf.constant([[0, 1, 2, 3, 4, 5]]		)
					A__  :	List[str]         =							model(UpperCamelCase__		)[0]

					A__  :	int         =							[1, 6, 3_0522]
					self.assertEqual(output.shape				,       UpperCamelCase__		)

					A__  :	Union[str, Any]         =							tf.constant(
					    [
					        [
					            [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
					            [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
					            [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
					        ]
					    ]		)
					tf.debugging.assert_near(output[:, :3, :3]				,       UpperCamelCase__				,       atol=1e-4		)

from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')