
from typing import TYPE_CHECKING

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


_UpperCAmelCase :			Tuple              =	{"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase :			int              =	["BeitFeatureExtractor"]
					_UpperCAmelCase :			Dict              =	["BeitImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase :			Tuple              =	[
					    "BEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BeitForImageClassification",
					    "BeitForMaskedImageModeling",
					    "BeitForSemanticSegmentation",
					    "BeitModel",
					    "BeitPreTrainedModel",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase :			Tuple              =	[
					    "FlaxBeitForImageClassification",
					    "FlaxBeitForMaskedImageModeling",
					    "FlaxBeitModel",
					    "FlaxBeitPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_beit import BeitFeatureExtractor
										from .image_processing_beit import BeitImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_beit import (
										    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BeitForImageClassification,
										    BeitForMaskedImageModeling,
										    BeitForSemanticSegmentation,
										    BeitModel,
										    BeitPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_beit import (
										    FlaxBeitForImageClassification,
										    FlaxBeitForMaskedImageModeling,
										    FlaxBeitModel,
										    FlaxBeitPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase :			Any              =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
					from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(A_    ,				'tf_padding'	)	)
				self.parent.assertTrue(hasattr(A_    ,				'depth_multiplier'	)	)






class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=3    ,				A_=32    ,				A_=0.25    ,				A_=8    ,				A_=8    ,				A_=6    ,				A_=32    ,				A_=True    ,				A_=True    ,				A_=True    ,				A_="relu6"    ,				A_=1_280    ,				A_=0.1    ,				A_=0.02    ,				A_=True    ,				A_=True    ,				A_=10    ,				A_=None    ,				)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					image_size
				UpperCamelCase          =					depth_multiplier
				UpperCamelCase          =					depth_divisible_by
				UpperCamelCase          =					min_depth
				UpperCamelCase          =					expand_ratio
				UpperCamelCase          =					tf_padding
				UpperCamelCase          =					output_stride
				UpperCamelCase          =					first_layer_is_expansion
				UpperCamelCase          =					finegrained_output
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier	)
				UpperCamelCase          =					classifier_dropout_prob
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					is_training
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					scope



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				UpperCamelCase          =					None
				UpperCamelCase          =					None
				if self.use_labels:
						UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.num_labels	)
						UpperCamelCase          =					ids_tensor([self.batch_size, self.image_size, self.image_size]    ,				self.num_labels	)

				UpperCamelCase          =					self.get_config()

				return config, pixel_values, labels, pixel_labels



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				return MobileNetVaConfig(
				    num_channels=self.num_channels    ,				image_size=self.image_size    ,				depth_multiplier=self.depth_multiplier    ,				depth_divisible_by=self.depth_divisible_by    ,				min_depth=self.min_depth    ,				expand_ratio=self.expand_ratio    ,				output_stride=self.output_stride    ,				first_layer_is_expansion=self.first_layer_is_expansion    ,				finegrained_output=self.finegrained_output    ,				hidden_act=self.hidden_act    ,				tf_padding=self.tf_padding    ,				classifier_dropout_prob=self.classifier_dropout_prob    ,				initializer_range=self.initializer_range    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(
				    result.last_hidden_state.shape    ,				(
				        self.batch_size,
				        self.last_hidden_size,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )    ,				)
				self.parent.assertEqual(
				    result.pooler_output.shape    ,				(self.batch_size, self.last_hidden_size)    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					MobileNetVaForImageClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					MobileNetVaForSemanticSegmentation(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(
				    result.logits.shape    ,				(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )    ,				)
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				self.parent.assertEqual(
				    result.logits.shape    ,				(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )    ,				)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					config_and_inputs
				UpperCamelCase          =					{'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    (
		    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		__lowercase	:	Optional[int]     					=    (
		    {
		        "feature-extraction": MobileNetVaModel,
		        "image-classification": MobileNetVaForImageClassification,
		        "image-segmentation": MobileNetVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		__lowercase	:	Optional[int]     					=    False
		__lowercase	:	List[str]     					=    False
		__lowercase	:	List[str]     					=    False
		__lowercase	:	Dict     					=    False



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaModelTester(self	)
				UpperCamelCase          =					MobileNetVaConfigTester(self    ,				config_class=A_    ,				has_text_modality=A_	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'	)
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				pass



		@unittest.skip(reason='MobileNetV2 does not output attentions'	)
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						UpperCamelCase          =					inspect.signature(model.forward	)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						UpperCamelCase          =					[*signature.parameters.keys()]

						UpperCamelCase          =					['pixel_values']
						self.assertListEqual(arg_names[:1]    ,				A_	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				def check_hidden_states_output(A_    ,				A_    ,				A_	):
						UpperCamelCase          =					model_class(A_	)
						model.to(A_	)
						model.eval()

						with torch.no_grad():
								UpperCamelCase          =					model(**self._prepare_for_class(A_    ,				A_	)	)

						UpperCamelCase          =					outputs.hidden_states

						UpperCamelCase          =					16
						self.assertEqual(len(A_	)    ,				A_	)

				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					True
						check_hidden_states_output(A_    ,				A_    ,				A_	)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase          =					True

						check_hidden_states_output(A_    ,				A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					MobileNetVaModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)






def 				A	( )       ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image


@require_torch
@require_vision
class     lowercase (		unittest.TestCase       ):



		@cached_property
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				return (
				    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'	) if is_vision_available() else None
				)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'	).to(A_	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	).to(A_	)

				# forward pass
				with torch.no_grad():
						UpperCamelCase          =					model(**A_	)

				# verify the logits
				UpperCamelCase          =					torch.Size((1, 1_001)	)
				self.assertEqual(outputs.logits.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor([0.2445, -1.1993, 0.1905]	).to(A_	)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,				A_    ,				atol=1e-4	)	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'	)
				UpperCamelCase          =					model.to(A_	)

				UpperCamelCase          =					MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'	)

				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='pt'	).to(A_	)

				# forward pass
				with torch.no_grad():
						UpperCamelCase          =					model(**A_	)
				UpperCamelCase          =					outputs.logits

				# verify the logits
				UpperCamelCase          =					torch.Size((1, 21, 65, 65)	)
				self.assertEqual(logits.shape    ,				A_	)

				UpperCamelCase          =					torch.tensor(
				    [
				        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
				        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
				        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
				    ]    ,				device=A_    ,				)

				self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,				A_    ,				atol=1e-4	)	)
