
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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


lowercase						:      Dict									= logging.get_logger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					List[List[ImageInput]]:






       '''simple docstring'''





       if isinstance(_lowerCamelCase		,						(list, tuple)) and isinstance(videos[0]		,						(list, tuple)) and is_valid_image(videos[0][0]):
              return videos

       elif isinstance(_lowerCamelCase		,						(list, tuple)) and is_valid_image(videos[0]):
              return [videos]

       elif is_valid_image(_lowerCamelCase):
              return [[videos]]

       raise ValueError(F'Could not make batched video from {videos}')




class 				lowerCamelCase__							(       __lowercase):




 '''simple docstring'''




 _A			=  ['pixel_values']







 def __init__(      self	:Any  ,     a	:bool = True  ,     a	:Dict[str, int] = None  ,     a	:PILImageResampling = PILImageResampling.BILINEAR  ,     a	:bool = True  ,     a	:Dict[str, int] = None  ,     a	:bool = True  ,     a	:Union[int, float] = 1 / 2_5_5  ,     a	:bool = True  ,     a	:Optional[Union[float, List[float]]] = None  ,     a	:Optional[Union[float, List[float]]] = None  ,     **a	:Union[str, Any]  ,     )	->				None:
        super().__init__(**a		)
        __UpperCamelCase			:     Union[str, Any]         =      size if size is not None else {"shortest_edge": 2_2_4}
        __UpperCamelCase			:     Optional[int]         =      get_size_dict(a  ,     default_to_square=a		)
        __UpperCamelCase			:     List[str]         =      crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
        __UpperCamelCase			:     Optional[int]         =      get_size_dict(a  ,     param_name="crop_size"		)

        __UpperCamelCase			:     int         =      do_resize
        __UpperCamelCase			:     List[str]         =      size
        __UpperCamelCase			:     str         =      do_center_crop
        __UpperCamelCase			:     Tuple         =      crop_size
        __UpperCamelCase			:     Optional[int]         =      resample
        __UpperCamelCase			:     str         =      do_rescale
        __UpperCamelCase			:     str         =      rescale_factor
        __UpperCamelCase			:     Tuple         =      do_normalize
        __UpperCamelCase			:     Dict         =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __UpperCamelCase			:     int         =      image_std if image_std is not None else IMAGENET_STANDARD_STD







 def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:np.ndarray  ,     a	:Dict[str, int]  ,     a	:PILImageResampling = PILImageResampling.BILINEAR  ,     a	:Optional[Union[str, ChannelDimension]] = None  ,     **a	:Tuple  ,     )	->				np.ndarray:
        __UpperCamelCase			:     Any         =      get_size_dict(a  ,     default_to_square=a		)
        if "shortest_edge" in size:
               __UpperCamelCase			:     Dict         =      get_resize_output_image_size(a  ,     size["shortest_edge"]  ,     default_to_square=a		)
        elif "height" in size and "width" in size:
               __UpperCamelCase			:     int         =      (size["height"], size["width"])
        else:
               raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
        return resize(a  ,     size=a  ,     resample=a  ,     data_format=a  ,     **a		)







 def      _lowerCamelCase       (      self	:Any  ,     a	:np.ndarray  ,     a	:Dict[str, int]  ,     a	:Optional[Union[str, ChannelDimension]] = None  ,     **a	:str  ,     )	->				np.ndarray:
        __UpperCamelCase			:     List[str]         =      get_size_dict(a		)
        if "height" not in size or "width" not in size:
               raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'		)
        return center_crop(a  ,     size=(size["height"], size["width"])  ,     data_format=a  ,     **a		)







 def      _lowerCamelCase       (      self	:Any  ,     a	:np.ndarray  ,     a	:Union[int, float]  ,     a	:Optional[Union[str, ChannelDimension]] = None  ,     **a	:Tuple  ,     )	->				Optional[Any]:
        return rescale(a  ,     scale=a  ,     data_format=a  ,     **a		)







 def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:np.ndarray  ,     a	:Union[float, List[float]]  ,     a	:Union[float, List[float]]  ,     a	:Optional[Union[str, ChannelDimension]] = None  ,     **a	:List[str]  ,     )	->				np.ndarray:
        return normalize(a  ,     mean=a  ,     std=a  ,     data_format=a  ,     **a		)







 def      _lowerCamelCase       (      self	:List[str]  ,     a	:ImageInput  ,     a	:bool = None  ,     a	:Dict[str, int] = None  ,     a	:PILImageResampling = None  ,     a	:bool = None  ,     a	:Dict[str, int] = None  ,     a	:bool = None  ,     a	:float = None  ,     a	:bool = None  ,     a	:Optional[Union[float, List[float]]] = None  ,     a	:Optional[Union[float, List[float]]] = None  ,     a	:Optional[ChannelDimension] = ChannelDimension.FIRST  ,     )	->				np.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_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."		)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."		)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        __UpperCamelCase			:     Optional[Any]         =      to_numpy_array(a		)

        if do_resize:
               __UpperCamelCase			:     List[str]         =      self.resize(image=a  ,     size=a  ,     resample=a		)

        if do_center_crop:
               __UpperCamelCase			:     Optional[int]         =      self.center_crop(a  ,     size=a		)

        if do_rescale:
               __UpperCamelCase			:     Optional[Any]         =      self.rescale(image=a  ,     scale=a		)

        if do_normalize:
               __UpperCamelCase			:     Dict         =      self.normalize(image=a  ,     mean=a  ,     std=a		)

        __UpperCamelCase			:     Tuple         =      to_channel_dimension_format(a  ,     a		)
        return image







 def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:ImageInput  ,     a	:bool = None  ,     a	:Dict[str, int] = None  ,     a	:PILImageResampling = None  ,     a	:bool = None  ,     a	:Dict[str, int] = None  ,     a	:bool = None  ,     a	:float = None  ,     a	:bool = None  ,     a	:Optional[Union[float, List[float]]] = None  ,     a	:Optional[Union[float, List[float]]] = None  ,     a	:Optional[Union[str, TensorType]] = None  ,     a	:ChannelDimension = ChannelDimension.FIRST  ,     **a	:Optional[Any]  ,     )	->				PIL.Image.Image:
        __UpperCamelCase			:     int         =      do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase			:     str         =      resample if resample is not None else self.resample
        __UpperCamelCase			:     Any         =      do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCamelCase			:     int         =      do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase			:     str         =      rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase			:     Optional[Any]         =      do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase			:     Any         =      image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase			:     Optional[int]         =      image_std if image_std is not None else self.image_std

        __UpperCamelCase			:     List[str]         =      size if size is not None else self.size
        __UpperCamelCase			:     List[Any]         =      get_size_dict(a  ,     default_to_square=a		)
        __UpperCamelCase			:     str         =      crop_size if crop_size is not None else self.crop_size
        __UpperCamelCase			:     str         =      get_size_dict(a  ,     param_name="crop_size"		)

        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."		)

        __UpperCamelCase			:     Optional[Any]         =      make_batched(a		)

        __UpperCamelCase			:     List[str]         =      [
            [
                self._preprocess_image(
                    image=a  ,     do_resize=a  ,     size=a  ,     resample=a  ,     do_center_crop=a  ,     crop_size=a  ,     do_rescale=a  ,     rescale_factor=a  ,     do_normalize=a  ,     image_mean=a  ,     image_std=a  ,     data_format=a  ,     )
                for img in video
            ]
            for video in videos
        ]

        __UpperCamelCase			:     Union[str, Any]         =      {"pixel_values": videos}
        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 				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"		)