





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a		:					Optional[Any]	      =     {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					str	      =     ['''OwlViTFeatureExtractor''']
		a		:					List[Any]	      =     ['''OwlViTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					List[Any]	      =     [
		    '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OwlViTModel''',
		    '''OwlViTPreTrainedModel''',
		    '''OwlViTTextModel''',
		    '''OwlViTVisionModel''',
		    '''OwlViTForObjectDetection''',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a		:					List[str]	      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









"""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 			__UpperCamelCase							(		a__						,			a__						,			unittest.TestCase		):
							lowerCamelCase  :   Optional[int]    =(
							    (
							        TFMobileBertModel,
							        TFMobileBertForMaskedLM,
							        TFMobileBertForNextSentencePrediction,
							        TFMobileBertForPreTraining,
							        TFMobileBertForQuestionAnswering,
							        TFMobileBertForSequenceClassification,
							        TFMobileBertForTokenClassification,
							        TFMobileBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							lowerCamelCase  :   Tuple    =(
							    {
							        """feature-extraction""": TFMobileBertModel,
							        """fill-mask""": TFMobileBertForMaskedLM,
							        """question-answering""": TFMobileBertForQuestionAnswering,
							        """text-classification""": TFMobileBertForSequenceClassification,
							        """token-classification""": TFMobileBertForTokenClassification,
							        """zero-shot""": TFMobileBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase  :   Any    =False
							lowerCamelCase  :   str    =False






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__=False )      ->					Any:
								a :						Dict      =							super()._prepare_for_class(lowerCAmelCase__ ,   lowerCAmelCase__ ,   return_labels=lowerCAmelCase__ )

								if return_labels:
									if model_class in get_values(lowerCAmelCase__ ):
										a :						str      =							tf.zeros(self.model_tester.batch_size ,   dtype=tf.intaa )

								return inputs_dict


							class 			__UpperCamelCase							(		a__		):






														def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=13 ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=99 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=37 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=None ,   )      ->					List[str]:
															a :						Union[str, Any]      =							parent
															a :						str      =							batch_size
															a :						Optional[int]      =							seq_length
															a :						Tuple      =							is_training
															a :						int      =							use_input_mask
															a :						Union[str, Any]      =							use_token_type_ids
															a :						List[str]      =							use_labels
															a :						str      =							vocab_size
															a :						List[Any]      =							hidden_size
															a :						Any      =							num_hidden_layers
															a :						List[Any]      =							num_attention_heads
															a :						Tuple      =							intermediate_size
															a :						str      =							hidden_act
															a :						List[Any]      =							hidden_dropout_prob
															a :						Optional[Any]      =							attention_probs_dropout_prob
															a :						Union[str, Any]      =							max_position_embeddings
															a :						Tuple      =							type_vocab_size
															a :						Union[str, Any]      =							type_sequence_label_size
															a :						Optional[int]      =							initializer_range
															a :						Any      =							num_labels
															a :						str      =							num_choices
															a :						List[Any]      =							scope
															a :						str      =							embedding_size






														def        __a  (  self )      ->					Dict:
															a :						Optional[int]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

															a :						Optional[int]      =							None
															if self.use_input_mask:
																a :						int      =							random_attention_mask([self.batch_size, self.seq_length] )

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

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

															a :						Tuple      =							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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					int:
															a :						List[Any]      =							TFMobileBertModel(config=lowerCAmelCase__ )
															a :						int      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						List[Any]      =							model(lowerCAmelCase__ )

															a :						List[str]      =							[input_ids, input_mask]
															a :						Dict      =							model(lowerCAmelCase__ )

															a :						List[str]      =							model(lowerCAmelCase__ )

															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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[Any]:
															a :						List[Any]      =							TFMobileBertForMaskedLM(config=lowerCAmelCase__ )
															a :						Union[str, Any]      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Optional[int]      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					int:
															a :						int      =							TFMobileBertForNextSentencePrediction(config=lowerCAmelCase__ )
															a :						Any      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						List[str]      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, 2) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					List[str]:
															a :						Dict      =							TFMobileBertForPreTraining(config=lowerCAmelCase__ )
															a :						Union[str, Any]      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						List[Any]      =							model(lowerCAmelCase__ )
															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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Tuple:
															a :						List[str]      =							self.num_labels
															a :						List[str]      =							TFMobileBertForSequenceClassification(config=lowerCAmelCase__ )
															a :						Optional[Any]      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Dict      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels) )






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






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Any:
															a :						Any      =							self.num_labels
															a :						Union[str, Any]      =							TFMobileBertForTokenClassification(config=lowerCAmelCase__ )
															a :						Any      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Any      =							model(lowerCAmelCase__ )
															self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels) )






														def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					List[str]:
															a :						Dict      =							TFMobileBertForQuestionAnswering(config=lowerCAmelCase__ )
															a :						int      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
															a :						Optional[Any]      =							model(lowerCAmelCase__ )
															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        __a  (  self )      ->					List[str]:
															a :						Dict      =							self.prepare_config_and_inputs()
															(
															    (
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			(
															    a
															),			
															) :						Dict      =							config_and_inputs
															a :						Optional[int]      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
															return config, inputs_dict






							def        __a  (  self )      ->					List[str]:
								a :						List[str]      =							TFMobileBertModelTest.TFMobileBertModelTester(self )
								a :						str      =							ConfigTester(self ,   config_class=lowerCAmelCase__ ,   hidden_size=37 )






							def        __a  (  self )      ->					Any:
								self.config_tester.run_common_tests()






							def        __a  (  self )      ->					List[str]:
								a :						List[str]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								a :						Any      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Any:
								a :						Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase__ )






							def        __a  (  self )      ->					List[str]:
								a :						Tuple      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								a :						List[str]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								a :						Dict      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[Any]:
								a :						Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase__ )






							@slow
							def        __a  (  self )      ->					Any:
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
									a :						Union[str, Any]      =							TFMobileBertModel.from_pretrained(lowerCAmelCase__ )
									self.assertIsNotNone(lowerCAmelCase__ )


@require_tf
class 			__UpperCamelCase							(		unittest.TestCase		):






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

								a :						Optional[int]      =							[1, 6, 3_0522]
								self.assertEqual(output.shape ,   lowerCAmelCase__ )

								a :						Optional[Any]      =							tf.constant(
								    [
								        [
								            [-4.5_919_547, -9.248_295, -9.645_256],
								            [-6.7_306_175, -6.440_284, -6.6_052_837],
								            [-7.2_743_506, -6.7_847_915, -6.024_673],
								        ]
								    ] )
								tf.debugging.assert_near(output[:, :3, :3] ,   lowerCAmelCase__ ,   atol=1E-4 )


