

'''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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__      		=			logging.get_logger(__name__)


if is_vision_available():
 import PIL






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[int]           =    ['pixel_values']






  def __init__(   self     :  List[str]      ,lowercase__     :  bool = True      ,lowercase__     :  Dict[str, int] = None      ,lowercase__     :  PILImageResampling = PILImageResampling.BICUBIC      ,lowercase__     :  bool = True      ,lowercase__     :  Dict[str, int] = None      ,lowercase__     :  bool = True      ,lowercase__     :  Union[int, float] = 1 / 2_5_5      ,lowercase__     :  bool = True      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  bool = True      ,**lowercase__     :  str      ,):
   super().__init__(**lowercase__    )
   __lowercase													=				size if size is not None else {'''shortest_edge''': 2_2_4}
   __lowercase													=				get_size_dict(lowercase__      ,default_to_square=lowercase__    )
   __lowercase													=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   __lowercase													=				get_size_dict(lowercase__      ,default_to_square=lowercase__      ,param_name='''crop_size'''    )

   __lowercase													=				do_resize
   __lowercase													=				size
   __lowercase													=				resample
   __lowercase													=				do_center_crop
   __lowercase													=				crop_size
   __lowercase													=				do_rescale
   __lowercase													=				rescale_factor
   __lowercase													=				do_normalize
   __lowercase													=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __lowercase													=				image_std if image_std is not None else OPENAI_CLIP_STD
   __lowercase													=				do_convert_rgb






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  np.ndarray      ,lowercase__     :  Dict[str, int]      ,lowercase__     :  PILImageResampling = PILImageResampling.BICUBIC      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				get_size_dict(lowercase__      ,default_to_square=lowercase__    )
   if "shortest_edge" not in size:
    raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"    )
   __lowercase													=				get_resize_output_image_size(lowercase__      ,size=size['''shortest_edge''']      ,default_to_square=lowercase__    )
   return resize(lowercase__      ,size=lowercase__      ,resample=lowercase__      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  np.ndarray      ,lowercase__     :  Dict[str, int]      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  List[str]      ,):
   __lowercase													=				get_size_dict(lowercase__    )
   if "height" not in size or "width" not in size:
    raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"    )
   return center_crop(lowercase__      ,size=(size['''height'''], size['''width'''])      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  np.ndarray      ,lowercase__     :  Union[int, float]      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  int      ,):
   return rescale(lowercase__      ,scale=lowercase__      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  np.ndarray      ,lowercase__     :  Union[float, List[float]]      ,lowercase__     :  Union[float, List[float]]      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  str      ,):
   return normalize(lowercase__      ,mean=lowercase__      ,std=lowercase__      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  ImageInput      ,lowercase__     :  bool = None      ,lowercase__     :  Dict[str, int] = None      ,lowercase__     :  PILImageResampling = None      ,lowercase__     :  bool = None      ,lowercase__     :  int = None      ,lowercase__     :  bool = None      ,lowercase__     :  float = None      ,lowercase__     :  bool = None      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  bool = None      ,lowercase__     :  Optional[Union[str, TensorType]] = None      ,lowercase__     :  Optional[ChannelDimension] = ChannelDimension.FIRST      ,**lowercase__     :  str      ,):
   __lowercase													=				do_resize if do_resize is not None else self.do_resize
   __lowercase													=				size if size is not None else self.size
   __lowercase													=				get_size_dict(lowercase__      ,param_name='''size'''      ,default_to_square=lowercase__    )
   __lowercase													=				resample if resample is not None else self.resample
   __lowercase													=				do_center_crop if do_center_crop is not None else self.do_center_crop
   __lowercase													=				crop_size if crop_size is not None else self.crop_size
   __lowercase													=				get_size_dict(lowercase__      ,param_name='''crop_size'''      ,default_to_square=lowercase__    )
   __lowercase													=				do_rescale if do_rescale is not None else self.do_rescale
   __lowercase													=				rescale_factor if rescale_factor is not None else self.rescale_factor
   __lowercase													=				do_normalize if do_normalize is not None else self.do_normalize
   __lowercase													=				image_mean if image_mean is not None else self.image_mean
   __lowercase													=				image_std if image_std is not None else self.image_std
   __lowercase													=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __lowercase													=				make_list_of_images(lowercase__    )

   if not valid_images(lowercase__    ):
    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:
    raise ValueError('''Size 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.'''    )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __lowercase													=				[convert_to_rgb(lowercase__    ) for image in images]

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

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

   if do_center_crop:
    __lowercase													=				[self.center_crop(image=lowercase__      ,size=lowercase__    ) for image in images]

   if do_rescale:
    __lowercase													=				[self.rescale(image=lowercase__      ,scale=lowercase__    ) for image in images]

   if do_normalize:
    __lowercase													=				[self.normalize(image=lowercase__      ,mean=lowercase__      ,std=lowercase__    ) for image in images]

   __lowercase													=				[to_channel_dimension_format(lowercase__      ,lowercase__    ) for image in images]

   __lowercase													=				{'''pixel_values''': images}
   return BatchFeature(data=lowercase__      ,tensor_type=lowercase__    )




'''simple docstring'''






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     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				hashlib.mda(image.tobytes()   )
 return m.hexdigest()






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






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

   __lowercase													=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,'''image'''      ,split='''test'''    )
   __lowercase													=				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    )},
       ]      ,lowercase__      ,)






  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






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

