


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  snake_case_     :
											@staticmethod
											def 				lowerCamelCase__(   *__snake_case     :Tuple    ,**__snake_case     :Tuple   )      ->					List[str]:
																		pass






def 							__lowercase     (				__lowerCAmelCase							:       Image     ):
							a__    =  hashlib.mda(image.tobytes()     )
							return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  snake_case_     (unittest.TestCase   ):
					UpperCAmelCase__      :					List[Any]					   =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :List[str]    ,__snake_case     :str    ,__snake_case     :int   )      ->					List[str]:
												a__    =  DepthEstimationPipeline(model=__snake_case    ,image_processor=__snake_case   )
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Union[str, Any]    ,__snake_case     :int   )      ->					str:
												a__    =  depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
												self.assertEqual({'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )}    ,__snake_case   )
												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   )},
												    ]    ,__snake_case    ,)
					@require_tf
					@unittest.skip('Depth estimation is not implemented in TF'   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					List[Any]:
												pass
					@slow
					@require_torch
					def 				lowerCamelCase__(   self     :Dict   )      ->					Dict:
												a__    =  'Intel/dpt-large'
												a__    =  pipeline('depth-estimation'    ,model=__snake_case   )
												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()   )    ,29.3_04   )
												self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()   )    ,2.6_62   )



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








from typing import List, Optional, Union

import numpy as np
import PIL.Image

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


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

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

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

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




