


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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    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


_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   logging.get_logger(__name__)

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		['pixel_values']
    def __init__(	self      , __snake_case = True      , __snake_case = None      , __snake_case = 0.9      , __snake_case = PILImageResampling.BICUBIC      , __snake_case = True      , __snake_case = None      , __snake_case = 1 / 2_5_5      , __snake_case = True      , __snake_case = True      , __snake_case = None      , __snake_case = None      , **__snake_case      , ):
         super().__init__(**__snake_case   )
         snake_case					=    size if size is not None else {'''shortest_edge''': 2_2_4}
         snake_case					=    get_size_dict(__snake_case      , default_to_square=__snake_case   )
         snake_case					=    crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         snake_case					=    get_size_dict(__snake_case      , param_name='''crop_size'''   )

         snake_case					=    do_resize
         snake_case					=    size
         snake_case					=    crop_pct
         snake_case					=    resample
         snake_case					=    do_center_crop
         snake_case					=    crop_size
         snake_case					=    do_rescale
         snake_case					=    rescale_factor
         snake_case					=    do_normalize
         snake_case					=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         snake_case					=    image_std if image_std is not None else IMAGENET_DEFAULT_STD
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = None      , __snake_case = PILImageResampling.BICUBIC      , __snake_case = None      , **__snake_case      , ):
         snake_case					=    get_size_dict(__snake_case      , default_to_square=__snake_case   )
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
              raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
         if crop_pct is not None:
              if "shortest_edge" in size:
                   snake_case					=    int(size['''shortest_edge'''] / crop_pct   )
              elif "height" in size and "width" in size:
                   if size["height"] == size["width"]:
                        snake_case					=    int(size['''height'''] / crop_pct   )
                   else:
                        snake_case					=    (int(size['''height'''] / crop_pct   ), int(size['''width'''] / crop_pct   ))
              else:
                   raise ValueError('''Invalid size for resize: {}'''.format(__snake_case   )   )

              snake_case					=    get_resize_output_image_size(__snake_case      , size=__snake_case      , default_to_square=__snake_case   )
         else:
              if "shortest_edge" in size:
                   snake_case					=    get_resize_output_image_size(__snake_case      , size=size['''shortest_edge''']      , default_to_square=__snake_case   )
              elif "height" in size and "width" in size:
                   snake_case					=    (size['''height'''], size['''width'''])
              else:
                   raise ValueError('''Invalid size for resize: {}'''.format(__snake_case   )   )

         return resize(__snake_case      , size=__snake_case      , resample=__snake_case      , data_format=__snake_case      , **__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         snake_case					=    get_size_dict(__snake_case   )
         if "height" not in size or "width" not in size:
              raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''   )
         return center_crop(__snake_case      , size=(size['''height'''], size['''width'''])      , data_format=__snake_case      , **__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         return rescale(__snake_case      , scale=__snake_case      , data_format=__snake_case      , **__snake_case   )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case = None      , **__snake_case      , ):
         return normalize(__snake_case      , mean=__snake_case      , std=__snake_case      , data_format=__snake_case      , **__snake_case   )




    def 		a_      (	self      , __snake_case      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = ChannelDimension.FIRST      , **__snake_case      , ):
         snake_case					=    do_resize if do_resize is not None else self.do_resize
         snake_case					=    crop_pct if crop_pct is not None else self.crop_pct
         snake_case					=    resample if resample is not None else self.resample
         snake_case					=    do_center_crop if do_center_crop is not None else self.do_center_crop
         snake_case					=    do_rescale if do_rescale is not None else self.do_rescale
         snake_case					=    rescale_factor if rescale_factor is not None else self.rescale_factor
         snake_case					=    do_normalize if do_normalize is not None else self.do_normalize
         snake_case					=    image_mean if image_mean is not None else self.image_mean
         snake_case					=    image_std if image_std is not None else self.image_std

         snake_case					=    size if size is not None else self.size
         snake_case					=    get_size_dict(__snake_case      , default_to_square=__snake_case   )
         snake_case					=    crop_size if crop_size is not None else self.crop_size
         snake_case					=    get_size_dict(__snake_case      , param_name='''crop_size'''   )

         snake_case					=    make_list_of_images(__snake_case   )

         if not valid_images(__snake_case   ):
              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_center_crop and crop_pct is None:
              raise ValueError('''Crop_pct 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.
         snake_case					=    [to_numpy_array(__snake_case   ) for image in images]

         if do_resize:
              snake_case					=    [self.resize(image=__snake_case      , size=__snake_case      , crop_pct=__snake_case      , resample=__snake_case   ) for image in images]

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

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

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

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

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








# 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.
_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   abspath(join(dirname(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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(UpperCamelCase_    )

def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     from transformers.testing_utils import pytest_terminal_summary_main

     snake_case					=    terminalreporter.config.getoption('''--make-reports'''    )
     if make_reports:
          pytest_terminal_summary_main(UpperCamelCase_							,id=UpperCamelCase_    )




