
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor





class       __A(				unittest.TestCase       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Any=7   ,					__UpperCamelCase					:     Optional[int]=3   ,					__UpperCamelCase					:     List[Any]=3_0   ,					__UpperCamelCase					:     Optional[Any]=4_0_0   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     str=0.9   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     Optional[int]=True   ,					__UpperCamelCase					:     str=[0.5, 0.5, 0.5]   ,					__UpperCamelCase					:     Optional[int]=[0.5, 0.5, 0.5]   ,					):
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_0}
												lowerCamelCase_							  =					crop_size if crop_size is not None else {"""height""": 3_0, """width""": 3_0}
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					num_channels
												lowerCamelCase_							  =					min_resolution
												lowerCamelCase_							  =					max_resolution
												lowerCamelCase_							  =					do_resize_and_center_crop
												lowerCamelCase_							  =					size
												lowerCamelCase_							  =					crop_pct
												lowerCamelCase_							  =					crop_size
												lowerCamelCase_							  =					do_normalize
												lowerCamelCase_							  =					image_mean
												lowerCamelCase_							  =					image_std



					def    lowercase__      (	self					:     Optional[int]   ):
												return {
												    "size": self.size,
												    "do_resize_and_center_crop": self.do_resize_and_center_crop,
												    "crop_pct": self.crop_pct,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												}





@require_torch
@require_vision
class       __A(				UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    PoolFormerImageProcessor if is_vision_available() else None

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					PoolFormerImageProcessingTester(self   )

					@property
					def    lowercase__      (	self					:     str   ):
												return self.image_processor_tester.prepare_image_processor_dict()

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.image_processing_class(**self.image_processor_dict   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""do_resize_and_center_crop"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""size"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""crop_pct"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""do_normalize"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""image_mean"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""image_std"""   )   )

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.image_processing_class.from_dict(self.image_processor_dict   )
												self.assertEqual(image_processor.size   ,					{"""shortest_edge""": 3_0}   )
												self.assertEqual(image_processor.crop_size   ,					{"""height""": 3_0, """width""": 3_0}   )

												lowerCamelCase_							  =					self.image_processing_class.from_dict(self.image_processor_dict   ,					size=4_2   ,					crop_size=8_4   )
												self.assertEqual(image_processor.size   ,					{"""shortest_edge""": 4_2}   )
												self.assertEqual(image_processor.crop_size   ,					{"""height""": 8_4, """width""": 8_4}   )

					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					def    lowercase__      (	self					:     int   ):
												# Initialize image_processing
												lowerCamelCase_							  =					self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												lowerCamelCase_							  =					prepare_image_inputs(self.image_processor_tester   ,					equal_resolution=__UpperCamelCase   )
												for image in image_inputs:
																			self.assertIsInstance(__UpperCamelCase   ,					Image.Image   )

												# Test not batched input
												lowerCamelCase_							  =					image_processing(image_inputs[0]   ,					return_tensors="""pt"""   ).pixel_values

												self.assertEqual(
												    encoded_images.shape   ,					(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,					)

												# Test batched
												lowerCamelCase_							  =					image_processing(__UpperCamelCase   ,					return_tensors="""pt"""   ).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,					(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,					)

					def    lowercase__      (	self					:     int   ):
												# Initialize image_processing
												lowerCamelCase_							  =					self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												lowerCamelCase_							  =					prepare_image_inputs(self.image_processor_tester   ,					equal_resolution=__UpperCamelCase   ,					numpify=__UpperCamelCase   )
												for image in image_inputs:
																			self.assertIsInstance(__UpperCamelCase   ,					np.ndarray   )

												# Test not batched input
												lowerCamelCase_							  =					image_processing(image_inputs[0]   ,					return_tensors="""pt"""   ).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,					(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,					)

												# Test batched
												lowerCamelCase_							  =					image_processing(__UpperCamelCase   ,					return_tensors="""pt"""   ).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,					(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,					)



					def    lowercase__      (	self					:     Optional[Any]   ):
												# Initialize image_processing
												lowerCamelCase_							  =					self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												lowerCamelCase_							  =					prepare_image_inputs(self.image_processor_tester   ,					equal_resolution=__UpperCamelCase   ,					torchify=__UpperCamelCase   )
												for image in image_inputs:
																			self.assertIsInstance(__UpperCamelCase   ,					torch.Tensor   )

												# Test not batched input
												lowerCamelCase_							  =					image_processing(image_inputs[0]   ,					return_tensors="""pt"""   ).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,					(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,					)

												# Test batched
												lowerCamelCase_							  =					image_processing(__UpperCamelCase   ,					return_tensors="""pt"""   ).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,					(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,					)







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       __A:

							@staticmethod
							def    lowercase__      (	*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
														pass



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Image       )  ->    str:
							lowerCamelCase_							  =					hashlib.mda(image.tobytes()       )
							return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING

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

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

												lowerCamelCase_							  =					datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,					"""image"""   ,					split="""test"""   )
												lowerCamelCase_							  =					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   )},
												    ]   ,					__UpperCamelCase   ,					)

					@require_tf
					@unittest.skip("""Depth estimation is not implemented in TF"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@slow
					@require_torch
					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""Intel/dpt-large"""
												lowerCamelCase_							  =					pipeline("""depth-estimation"""   ,					model=__UpperCamelCase   )
												lowerCamelCase_							  =					depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Dict   ):
												# This is highly irregular to have no small tests.
												self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""   )





