



'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_lowercase						  =		logging.get_logger(__name__)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       Union[str, Any]	)->  Dict:
    A__		 =					b.T
    A__		 =					np.sum(np.square(UpperCamelCase_	) ,       axis=1	)
    A__		 =					np.sum(np.square(UpperCamelCase_	) ,       axis=0	)
    A__		 =					np.matmul(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					aa[:, None] - 2 * ab + ba[None, :]
    return d



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       str	)->  Any:
    A__		 =					x.reshape(-1 ,       3	)
    A__		 =					squared_euclidean_distance(UpperCamelCase_ ,       UpperCamelCase_	)
    return np.argmin(UpperCamelCase_ ,       axis=1	)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				['''pixel_values''']




     def __init__(				self ,						a__ = None ,						a__ = True ,						a__ = None ,						a__ = PILImageResampling.BILINEAR ,						a__ = True ,						a__ = True ,						**a__ ,						):
         super().__init__(**a__)
         A__		 =					size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
         A__		 =					get_size_dict(a__)
         A__		 =					np.array(a__) if clusters is not None else None
         A__		 =					do_resize
         A__		 =					size
         A__		 =					resample
         A__		 =					do_normalize
         A__		 =					do_color_quantize




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = PILImageResampling.BILINEAR ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(a__)
         if "height" not in size or "width" not in size:
             raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}")
         return resize(
             a__ ,						size=(size['''height'''], size['''width''']) ,						resample=a__ ,						data_format=a__ ,						**a__)




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						):
         A__		 =					rescale(image=a__ ,						scale=1 / 1_2_7.5 ,						data_format=a__)
         A__		 =					image - 1
         return image




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = ChannelDimension.FIRST ,						**a__ ,						):
         A__		 =					do_resize if do_resize is not None else self.do_resize
         A__		 =					size if size is not None else self.size
         A__		 =					get_size_dict(a__)
         A__		 =					resample if resample is not None else self.resample
         A__		 =					do_normalize if do_normalize is not None else self.do_normalize
         A__		 =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         A__		 =					clusters if clusters is not None else self.clusters
         A__		 =					np.array(a__)

         A__		 =					make_list_of_images(a__)

         if not valid_images(a__):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.''')

         if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.''')

         if do_color_quantize and clusters is None:
             raise ValueError('''Clusters must be specified if do_color_quantize is True.''')

         # All transformations expect numpy arrays.
         A__		 =					[to_numpy_array(a__) for image in images]

         if do_resize:
             A__		 =					[self.resize(image=a__ ,						size=a__ ,						resample=a__) for image in images]

         if do_normalize:
             A__		 =					[self.normalize(image=a__) for image in images]

         if do_color_quantize:
             A__		 =					[to_channel_dimension_format(a__ ,						ChannelDimension.LAST) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             A__		 =					np.array(a__)
             A__		 =					color_quantize(a__ ,						a__).reshape(images.shape[:-1])

             # flatten to (batch_size, height*width)
             A__		 =					images.shape[0]
             A__		 =					images.reshape(a__ ,						-1)

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             A__		 =					list(a__)
         else:
             A__		 =					[to_channel_dimension_format(a__ ,						a__) for image in images]

         A__		 =					{'''input_ids''': images}
         return BatchFeature(data=a__ ,						tensor_type=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     _UpperCAmelCase			:




        @staticmethod
        def 		snake_case_		(				*a__ ,						**a__):
            pass



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Image	)->  str:
    A__		 =					hashlib.mda(image.tobytes()	)
    return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _UpperCAmelCase			(       unittest.TestCase     ):
     UpperCamelCase__		    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					DepthEstimationPipeline(model=a__ ,						image_processor=a__)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} ,						a__)
         import datasets

         A__		 =					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' ,						'''image''' ,						split='''test''')
         A__		 =					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 		snake_case_		(				self):
         pass




     @slow
     @require_torch
     def 		snake_case_		(				self):
         A__		 =					'''Intel/dpt-large'''
         A__		 =					pipeline('''depth-estimation''' ,						model=a__)
         A__		 =					depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
         A__		 =					hashimage(outputs['''depth'''])

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) ,						2_9.3_0_4)
         self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) ,						2.6_6_2)




     @require_torch
     def 		snake_case_		(				self):
         # This is highly irregular to have no small tests.
         self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')
