
import inspect
import unittest

from transformers import MobileViTConfig
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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
		from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Optional[Any]:
								__UpperCamelCase			:     Tuple         =      self.config_class(**self.inputs_dict		)
								self.parent.assertTrue(hasattr(a  ,     "hidden_sizes"		)		)
								self.parent.assertTrue(hasattr(a  ,     "neck_hidden_sizes"		)		)
								self.parent.assertTrue(hasattr(a  ,     "num_attention_heads"		)		)




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[Any]  ,     a	:Optional[int]  ,     a	:Tuple=1_3  ,     a	:Union[str, Any]=3_2  ,     a	:int=2  ,     a	:Tuple=3  ,     a	:Optional[Any]=6_4_0  ,     a	:Dict=4  ,     a	:Optional[int]="silu"  ,     a	:Dict=3  ,     a	:List[str]=3_2  ,     a	:Union[str, Any]=0.1  ,     a	:List[str]=0.1  ,     a	:List[str]=0.1  ,     a	:Any=0.02  ,     a	:Union[str, Any]=True  ,     a	:Dict=True  ,     a	:Any=1_0  ,     a	:List[str]=None  ,     )	->				Optional[int]:
								__UpperCamelCase			:     int         =      parent
								__UpperCamelCase			:     int         =      batch_size
								__UpperCamelCase			:     Any         =      image_size
								__UpperCamelCase			:     Optional[int]         =      patch_size
								__UpperCamelCase			:     List[Any]         =      num_channels
								__UpperCamelCase			:     Optional[Any]         =      last_hidden_size
								__UpperCamelCase			:     Optional[int]         =      num_attention_heads
								__UpperCamelCase			:     Any         =      hidden_act
								__UpperCamelCase			:     Any         =      conv_kernel_size
								__UpperCamelCase			:     str         =      output_stride
								__UpperCamelCase			:     Dict         =      hidden_dropout_prob
								__UpperCamelCase			:     Union[str, Any]         =      attention_probs_dropout_prob
								__UpperCamelCase			:     List[str]         =      classifier_dropout_prob
								__UpperCamelCase			:     str         =      use_labels
								__UpperCamelCase			:     Any         =      is_training
								__UpperCamelCase			:     Dict         =      num_labels
								__UpperCamelCase			:     Union[str, Any]         =      initializer_range
								__UpperCamelCase			:     Dict         =      scope







	def      _lowerCamelCase       (      self	:Tuple		)	->				Any:
								__UpperCamelCase			:     Tuple         =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								__UpperCamelCase			:     Optional[int]         =      None
								__UpperCamelCase			:     Union[str, Any]         =      None
								if self.use_labels:
															__UpperCamelCase			:     int         =      ids_tensor([self.batch_size]  ,     self.num_labels		)
															__UpperCamelCase			:     List[str]         =      ids_tensor([self.batch_size, self.image_size, self.image_size]  ,     self.num_labels		)

								__UpperCamelCase			:     List[str]         =      self.get_config()

								return config, pixel_values, labels, pixel_labels







	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								return MobileViTConfig(
								    image_size=self.image_size  ,     patch_size=self.patch_size  ,     num_channels=self.num_channels  ,     num_attention_heads=self.num_attention_heads  ,     hidden_act=self.hidden_act  ,     conv_kernel_size=self.conv_kernel_size  ,     output_stride=self.output_stride  ,     hidden_dropout_prob=self.hidden_dropout_prob  ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,     classifier_dropout_prob=self.classifier_dropout_prob  ,     initializer_range=self.initializer_range  ,     )







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Tuple  ,     a	:List[str]  ,     a	:Optional[int]  ,     a	:List[str]		)	->				int:
								__UpperCamelCase			:     List[Any]         =      MobileViTModel(config=a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     List[Any]         =      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      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Tuple  ,     a	:Any  ,     a	:int  ,     a	:Any		)	->				Any:
								__UpperCamelCase			:     int         =      self.num_labels
								__UpperCamelCase			:     int         =      MobileViTForImageClassification(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     Union[str, Any]         =      model(a  ,     labels=a		)
								self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)		)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:Optional[int]  ,     a	:Optional[int]  ,     a	:int  ,     a	:List[str]		)	->				List[Any]:
								__UpperCamelCase			:     Union[str, Any]         =      self.num_labels
								__UpperCamelCase			:     Any         =      MobileViTForSemanticSegmentation(a		)
								model.to(a		)
								model.eval()
								__UpperCamelCase			:     str         =      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			:     Tuple         =      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      _lowerCamelCase       (      self	:str		)	->				int:
								__UpperCamelCase			:     List[Any]         =      self.prepare_config_and_inputs()
								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      config_and_inputs
								__UpperCamelCase			:     Tuple         =      {"pixel_values": pixel_values}
								return config, inputs_dict




@require_torch
class 				lowerCamelCase__							(       __lowercase  ,		__lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  (
	    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	_A			=  (
	    {
	        'feature-extraction': MobileViTModel,
	        'image-classification': MobileViTForImageClassification,
	        'image-segmentation': MobileViTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	_A			=  False
	_A			=  False
	_A			=  False
	_A			=  False







	def      _lowerCamelCase       (      self	:int		)	->				Dict:
								__UpperCamelCase			:     str         =      MobileViTModelTester(self		)
								__UpperCamelCase			:     int         =      MobileViTConfigTester(self  ,     config_class=a  ,     has_text_modality=a		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Dict:
								self.config_tester.run_common_tests()







	@unittest.skip(reason="MobileViT does not use inputs_embeds"		)
	def      _lowerCamelCase       (      self	:List[str]		)	->				Any:
								pass







	@unittest.skip(reason="MobileViT does not support input and output embeddings"		)
	def      _lowerCamelCase       (      self	:List[str]		)	->				int:
								pass







	@unittest.skip(reason="MobileViT does not output attentions"		)
	def      _lowerCamelCase       (      self	:List[str]		)	->				Optional[int]:
								pass







	def      _lowerCamelCase       (      self	:Any		)	->				Optional[int]:
								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      self.model_tester.prepare_config_and_inputs_for_common()

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

															__UpperCamelCase			:     Dict         =      ["pixel_values"]
															self.assertListEqual(arg_names[:1]  ,     a		)







	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				str:
								pass







	def      _lowerCamelCase       (      self	:int		)	->				Tuple:
								__UpperCamelCase			:     Union[str, Any]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a		)







	def      _lowerCamelCase       (      self	:int		)	->				int:
								def check_hidden_states_output(a	:List[Any]  ,     a	:Optional[Any]  ,     a	:Any		):
															__UpperCamelCase			:     str         =      model_class(a		)
															model.to(a		)
															model.eval()

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

															__UpperCamelCase			:     Tuple         =      outputs.hidden_states

															__UpperCamelCase			:     Tuple         =      5
															self.assertEqual(len(a		)  ,     a		)

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															__UpperCamelCase			:     Any         =      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			:     Any         =      self.model_tester.prepare_config_and_inputs_for_common()

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

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__UpperCamelCase			:     Any         =      True

															check_hidden_states_output(a  ,     a  ,     a		)







	def      _lowerCamelCase       (      self	:Any		)	->				List[Any]:
								__UpperCamelCase			:     Optional[Any]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a		)







	def      _lowerCamelCase       (      self	:Dict		)	->				int:
								__UpperCamelCase			:     List[str]         =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*a		)







	@slow
	def      _lowerCamelCase       (      self	:List[Any]		)	->				Optional[int]:
								for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase			:     List[str]         =      MobileViTModel.from_pretrained(a		)
															self.assertIsNotNone(a		)




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
							return image




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




	'''simple docstring'''




	@cached_property
	def      _lowerCamelCase       (      self	:Tuple		)	->				Any:
								return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"		) if is_vision_available() else None







	@slow
	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								__UpperCamelCase			:     List[Any]         =      MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"		).to(a		)

								__UpperCamelCase			:     Dict         =      self.default_image_processor
								__UpperCamelCase			:     Optional[Any]         =      prepare_img()
								__UpperCamelCase			:     Union[str, Any]         =      image_processor(images=a  ,     return_tensors="pt"		).to(a		)

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

								# verify the logits
								__UpperCamelCase			:     Optional[int]         =      torch.Size((1, 1_0_0_0)		)
								self.assertEqual(outputs.logits.shape  ,     a		)

								__UpperCamelCase			:     List[str]         =      torch.tensor([-1.9364, -1.2327, -0.4653]		).to(a		)

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







	@slow
	def      _lowerCamelCase       (      self	:str		)	->				List[str]:
								__UpperCamelCase			:     Optional[Any]         =      MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)
								__UpperCamelCase			:     Optional[int]         =      model.to(a		)

								__UpperCamelCase			:     str         =      MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)

								__UpperCamelCase			:     List[Any]         =      prepare_img()
								__UpperCamelCase			:     Tuple         =      image_processor(images=a  ,     return_tensors="pt"		).to(a		)

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

								# verify the logits
								__UpperCamelCase			:     List[Any]         =      torch.Size((1, 2_1, 3_2, 3_2)		)
								self.assertEqual(logits.shape  ,     a		)

								__UpperCamelCase			:     Optional[Any]         =      torch.tensor(
								    [
								        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
								        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
								        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
								    ]  ,     device=a  ,     )

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







	@slow
	def      _lowerCamelCase       (      self	:List[Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     int         =      MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)
								__UpperCamelCase			:     str         =      model.to(a		)

								__UpperCamelCase			:     str         =      MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)

								__UpperCamelCase			:     List[str]         =      prepare_img()
								__UpperCamelCase			:     List[Any]         =      image_processor(images=a  ,     return_tensors="pt"		).to(a		)

								# forward pass
								with torch.no_grad():
															__UpperCamelCase			:     List[Any]         =      model(**a		)

								__UpperCamelCase			:     List[str]         =      outputs.logits.detach().cpu()

								__UpperCamelCase			:     Any         =      image_processor.post_process_semantic_segmentation(outputs=a  ,     target_sizes=[(5_0, 6_0)]		)
								__UpperCamelCase			:     Optional[Any]         =      torch.Size((5_0, 6_0)		)
								self.assertEqual(segmentation[0].shape  ,     a		)

								__UpperCamelCase			:     Optional[Any]         =      image_processor.post_process_semantic_segmentation(outputs=a		)
								__UpperCamelCase			:     Union[str, Any]         =      torch.Size((3_2, 3_2)		)
								self.assertEqual(segmentation[0].shape  ,     a		)

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:




		class 				lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:Optional[Any]  ,     a	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      DepthEstimationPipeline(model=a  ,     image_processor=a		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      depth_estimator(
								    [
								        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
								        "http://images.cocodataset.org/val2017/000000039769.jpg",
								        # RGBA
								        dataset[0]["file"],
								        # LA
								        dataset[1]["file"],
								        # L
								        dataset[2]["file"],
								    ]		)
								self.assertEqual(
								    [
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								    ]  ,     a  ,     )







	@require_tf
	@unittest.skip("Depth estimation is not implemented in TF"		)
	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								pass







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      hashimage(outputs["depth"]		)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)  ,     29.304		)
								self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)  ,     2.662		)







	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)