




'''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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						['''pixel_values''']



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Optional[Dict[str, int]] = None  ,				__lowerCamelCase :     PILImageResampling = PILImageResampling.BILINEAR  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Union[int, float] = 1 / 2_5_5  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				**__lowerCamelCase :     List[Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				size if size is not None else {"shortest_edge": 2_5_6}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				do_resize
      _SCREAMING_SNAKE_CASE	=				size
      _SCREAMING_SNAKE_CASE	=				resample
      _SCREAMING_SNAKE_CASE	=				do_center_crop
      _SCREAMING_SNAKE_CASE	=				crop_size
      _SCREAMING_SNAKE_CASE	=				do_rescale
      _SCREAMING_SNAKE_CASE	=				rescale_factor
      _SCREAMING_SNAKE_CASE	=				do_normalize
      _SCREAMING_SNAKE_CASE	=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _SCREAMING_SNAKE_CASE	=				image_std if image_std is not None else IMAGENET_STANDARD_STD



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Dict[str, int]  ,				__lowerCamelCase :     PILImageResampling = PILImageResampling.BICUBIC  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     Dict  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
      _SCREAMING_SNAKE_CASE	=				get_resize_output_image_size(__lowerCamelCase  ,				size=size["shortest_edge"]  ,				default_to_square=__lowerCamelCase   )
      return resize(__lowerCamelCase  ,				size=__lowerCamelCase  ,				resample=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Dict[str, int]  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     List[Any]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      return center_crop(__lowerCamelCase  ,				size=(size["height"], size["width"])  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     float  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return rescale(__lowerCamelCase  ,				scale=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Union[float, List[float]]  ,				__lowerCamelCase :     Union[float, List[float]]  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     Optional[Any]  ,				):

      """simple docstring"""
      return normalize(__lowerCamelCase  ,				mean=__lowerCamelCase  ,				std=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     ImageInput  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     PILImageResampling = None  ,				__lowerCamelCase :     bool = None  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Optional[float] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[str, TensorType]] = None  ,				__lowerCamelCase :     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				do_resize if do_resize is not None else self.do_resize
      _SCREAMING_SNAKE_CASE	=				size if size is not None else self.size
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				resample if resample is not None else self.resample
      _SCREAMING_SNAKE_CASE	=				do_center_crop if do_center_crop is not None else self.do_center_crop
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else self.crop_size
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				do_rescale if do_rescale is not None else self.do_rescale
      _SCREAMING_SNAKE_CASE	=				rescale_factor if rescale_factor is not None else self.rescale_factor
      _SCREAMING_SNAKE_CASE	=				do_normalize if do_normalize is not None else self.do_normalize
      _SCREAMING_SNAKE_CASE	=				image_mean if image_mean is not None else self.image_mean
      _SCREAMING_SNAKE_CASE	=				image_std if image_std is not None else self.image_std

      _SCREAMING_SNAKE_CASE	=				make_list_of_images(__lowerCamelCase   )

      if not valid_images(__lowerCamelCase   ):
         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."   )

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

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

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

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

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

      _SCREAMING_SNAKE_CASE	=				[to_channel_dimension_format(__lowerCamelCase  ,				__lowerCamelCase   ) for image in images]

      _SCREAMING_SNAKE_CASE	=				{"pixel_values": images}
      return BatchFeature(data=__lowerCamelCase  ,				tensor_type=__lowerCamelCase   )







'''simple docstring'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase_			=						abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Dict:
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(__A  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			str:
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   _SCREAMING_SNAKE_CASE	=				terminalreporter.config.getoption("--make-reports"  )
   if make_reports:
      pytest_terminal_summary_main(__A	,					id=__A  )

