import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
					from transformers.models.mobilevitva.modeling_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
					    make_divisible,
					)


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor


class     lowercase (		UpperCamelCase__       ):



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.config_class(**self.inputs_dict	)
				self.parent.assertTrue(hasattr(__a    ,				'width_multiplier'	)	)






class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=64    ,				A_=2    ,				A_=3    ,				A_="swish"    ,				A_=3    ,				A_=32    ,				A_=0.1    ,				A_=0.02    ,				A_=True    ,				A_=True    ,				A_=10    ,				A_=None    ,				A_=0.25    ,				A_=0.0    ,				A_=0.0    ,				)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					image_size
				UpperCamelCase          =					patch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					make_divisible(512 * width_multiplier    ,				divisor=8	)
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					conv_kernel_size
				UpperCamelCase          =					output_stride
				UpperCamelCase          =					classifier_dropout_prob
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					is_training
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					scope
				UpperCamelCase          =					width_multiplier
				UpperCamelCase          =					ffn_dropout
				UpperCamelCase          =					attn_dropout



		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	)  -> List[Any]:
				"""simple docstring"""
				return MobileViTVaConfig(
				    image_size=self.image_size    ,				patch_size=self.patch_size    ,				num_channels=self.num_channels    ,				hidden_act=self.hidden_act    ,				conv_kernel_size=self.conv_kernel_size    ,				output_stride=self.output_stride    ,				classifier_dropout_prob=self.classifier_dropout_prob    ,				initializer_range=self.initializer_range    ,				width_multiplier=self.width_multiplier    ,				ffn_dropout=self.ffn_dropout_prob    ,				attn_dropout=self.attn_dropout_prob    ,				)



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



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					MobileViTVaForImageClassification(__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[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					MobileViTVaForSemanticSegmentation(__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	)  -> int:
				"""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 (		UpperCamelCase__  ,       UpperCamelCase__  ,       unittest.TestCase       ):
		__lowercase	:	str     					=    (
		    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)

		__lowercase	:	Any     					=    (
		    {
		        """feature-extraction""": MobileViTVaModel,
		        """image-classification""": MobileViTVaForImageClassification,
		        """image-segmentation""": MobileViTVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		__lowercase	:	List[Any]     					=    False
		__lowercase	:	str     					=    False
		__lowercase	:	Union[str, Any]     					=    False
		__lowercase	:	int     					=    False



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					MobileViTVaModelTester(self	)
				UpperCamelCase          =					MobileViTVaConfigTester(self    ,				config_class=__a    ,				has_text_modality=__a	)



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



		@unittest.skip(reason='MobileViTV2 does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				pass



		@unittest.skip(reason='MobileViTV2 does not support input and output embeddings'	)
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='MobileViTV2 does not output attentions'	)
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				pass



		@require_torch_multi_gpu
		@unittest.skip(reason='Got `CUDA error: misaligned address` for tests after this one being run.'	)
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				pass



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""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	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__a	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""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          =					5
						self.assertEqual(len(__a	)    ,				__a	)

						# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
						# with the width and height being successively divided by 2.
						UpperCamelCase          =					2
						for i in range(len(__a	)	):
								self.assertListEqual(
								    list(hidden_states[i].shape[-2:]	)    ,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,				)
								divisor *= 2

						self.assertEqual(self.model_tester.output_stride    ,				divisor // 2	)

				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	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__a	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""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[str]:
				"""simple docstring"""
				for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					MobileViTVaModel.from_pretrained(__a	)
						self.assertIsNotNone(__a	)






def 				A	( )       ->  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	)  -> List[Any]:
				"""simple docstring"""
				return (
				    MobileViTImageProcessor.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'	)
				    if is_vision_available()
				    else None
				)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'	).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_000)	)
				self.assertEqual(outputs.logits.shape    ,				__a	)

				UpperCamelCase          =					torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]	).to(__a	)

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



		@slow
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'	)
				UpperCamelCase          =					model.to(__a	)

				UpperCamelCase          =					MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'	)

				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, 32, 32)	)
				self.assertEqual(logits.shape    ,				__a	)

				UpperCamelCase          =					torch.tensor(
				    [
				        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
				        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
				        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
				    ]    ,				device=__a    ,				)

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



		@slow
		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					MobileViTVaForSemanticSegmentation.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'	)
				UpperCamelCase          =					model.to(__a	)

				UpperCamelCase          =					MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'	)

				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.detach().cpu()

				UpperCamelCase          =					image_processor.post_process_semantic_segmentation(outputs=__a    ,				target_sizes=[(50, 60)]	)
				UpperCamelCase          =					torch.Size((50, 60)	)
				self.assertEqual(segmentation[0].shape    ,				__a	)

				UpperCamelCase          =					image_processor.post_process_semantic_segmentation(outputs=__a	)
				UpperCamelCase          =					torch.Size((32, 32)	)
				self.assertEqual(segmentation[0].shape    ,				__a	)


from __future__ import annotations


def 				A	( lowercase   ,	lowercase			)       ->  tuple[int, int]:
		'''simple docstring'''

		if b == 0:
				return (1, 0)
		((UpperCamelCase)     ,  (UpperCamelCase))          =					extended_euclid(lowercase   ,	a % b			)
		UpperCamelCase          =					a // b
		return (y, x - k * y)


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		((UpperCamelCase)     ,  (UpperCamelCase))          =					extended_euclid(lowercase   ,	lowercase			)
		UpperCamelCase          =					na * na
		UpperCamelCase          =					ra * x * na + ra * y * na
		return (n % m + m) % m


def 				A	( lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		((UpperCamelCase)     ,  (UpperCamelCase))          =					extended_euclid(lowercase   ,	lowercase			)
		if b < 0:
				UpperCamelCase          =					(b % n + n) % n
		return b


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

		UpperCamelCase     ,  UpperCamelCase          =					invert_modulo(lowercase   ,	lowercase			), invert_modulo(lowercase   ,	lowercase			)
		UpperCamelCase          =					na * na
		UpperCamelCase          =					ra * x * na + ra * y * na
		return (n % m + m) % m


if __name__ == "__main__":
					from doctest import testmod

					testmod(name="chinese_remainder_theorem", verbose=True)
					testmod(name="chinese_remainder_theorem2", verbose=True)
					testmod(name="invert_modulo", verbose=True)
					testmod(name="extended_euclid", verbose=True)
