

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 SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.config_class(**self.inputs_dict				)
				self.parent.assertTrue(hasattr(__UpperCamelCase			,					'tf_padding'				)				)
				self.parent.assertTrue(hasattr(__UpperCamelCase			,					'depth_multiplier'				)				)







class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=13			,					__UpperCamelCase=3			,					__UpperCamelCase=32			,					__UpperCamelCase=0.25			,					__UpperCamelCase=8			,					__UpperCamelCase=8			,					__UpperCamelCase=6			,					__UpperCamelCase=32			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase="relu6"			,					__UpperCamelCase=12_80			,					__UpperCamelCase=0.1			,					__UpperCamelCase=0.02			,					__UpperCamelCase=True			,					__UpperCamelCase=True			,					__UpperCamelCase=10			,					__UpperCamelCase=None			,					):

				"""simple docstring"""


				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			num_channels
				snake_case_   		=			image_size
				snake_case_   		=			depth_multiplier
				snake_case_   		=			depth_divisible_by
				snake_case_   		=			min_depth
				snake_case_   		=			expand_ratio
				snake_case_   		=			tf_padding
				snake_case_   		=			output_stride
				snake_case_   		=			first_layer_is_expansion
				snake_case_   		=			finegrained_output
				snake_case_   		=			hidden_act
				snake_case_   		=			last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier				)
				snake_case_   		=			classifier_dropout_prob
				snake_case_   		=			use_labels
				snake_case_   		=			is_training
				snake_case_   		=			num_labels
				snake_case_   		=			initializer_range
				snake_case_   		=			scope


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

				snake_case_   		=			self.get_config()

				return config, pixel_values, labels, pixel_labels


			def       __lowerCAmelCase			(     self				):

				"""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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			MobileNetVaModel(config=__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase				)
				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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.num_labels
				snake_case_   		=			MobileNetVaForImageClassification(__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase			,					labels=__UpperCamelCase				)
				self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.num_labels
				snake_case_   		=			MobileNetVaForSemanticSegmentation(__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.eval()
				snake_case_   		=			model(__UpperCamelCase				)
				self.parent.assertEqual(
				    result.logits.shape			,					(
				        self.batch_size,
				        self.num_labels,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )			,					)
				snake_case_   		=			model(__UpperCamelCase			,					labels=__UpperCamelCase				)
				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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.prepare_config_and_inputs()
				snake_case_		,							snake_case_		,							snake_case_		,							snake_case_   		=			config_and_inputs
				snake_case_   		=			{'pixel_values': pixel_values}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	(
			    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__A    	=	(
			    {
			        """feature-extraction""": MobileNetVaModel,
			        """image-classification""": MobileNetVaForImageClassification,
			        """image-segmentation""": MobileNetVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__A    	=	False
			__A    	=	False
			__A    	=	False
			__A    	=	False


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			MobileNetVaModelTester(self				)
				snake_case_   		=			MobileNetVaConfigTester(self			,					config_class=__UpperCamelCase			,					has_text_modality=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'				)
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'				)
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			@unittest.skip(reason='MobileNetV2 does not output attentions'				)
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_   		=			['pixel_values']
					self.assertListEqual(arg_names[:1]			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				def check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			model_class(__UpperCamelCase				)
					model.to(__UpperCamelCase				)
					model.eval()

					with torch.no_grad():
						snake_case_   		=			model(**self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase				)				)

					snake_case_   		=			outputs.hidden_states

					snake_case_   		=			16
					self.assertEqual(len(__UpperCamelCase				)			,					__UpperCamelCase				)

				snake_case_		,							snake_case_   		=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_   		=			True
					check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)

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

					check_hidden_states_output(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_   		=			MobileNetVaModel.from_pretrained(__UpperCamelCase				)
					self.assertIsNotNone(__UpperCamelCase				)







def a():





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






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






			"""simple docstring"""




			@cached_property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return (
				    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'				) if is_vision_available() else None
				)


			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'				).to(__UpperCamelCase				)

				snake_case_   		=			self.default_image_processor
				snake_case_   		=			prepare_img()
				snake_case_   		=			image_processor(images=__UpperCamelCase			,					return_tensors='pt'				).to(__UpperCamelCase				)

				# forward pass
				with torch.no_grad():
					snake_case_   		=			model(**__UpperCamelCase				)

				# verify the logits
				snake_case_   		=			torch.Size((1, 10_01)				)
				self.assertEqual(outputs.logits.shape			,					__UpperCamelCase				)

				snake_case_   		=			torch.tensor([0.2445, -1.1993, 0.1905]				).to(__UpperCamelCase				)

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







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'				)
				snake_case_   		=			model.to(__UpperCamelCase				)

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

				snake_case_   		=			prepare_img()
				snake_case_   		=			image_processor(images=__UpperCamelCase			,					return_tensors='pt'				).to(__UpperCamelCase				)

				# forward pass
				with torch.no_grad():
					snake_case_   		=			model(**__UpperCamelCase				)
				snake_case_   		=			outputs.logits

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

				snake_case_   		=			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=__UpperCamelCase			,					)

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



def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			len(lowercase__						)
	for _ in range(lowercase__						):
		for i in range(_ % 2      ,			arr_size - 1      ,			2						):
			if arr[i + 1] < arr[i]:
				snake_case_		,							snake_case_   		=			arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
		A									=  list(range(10, 0, -1))
		print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
