





import pytest

import datasets


# Import fixture modules as plugins
SCREAMING_SNAKE_CASE__							:      Dict         =    ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
   for item in items:
      if any(marker in item.keywords for marker in ["integration", "unit"] ):
         continue
      item.add_marker(pytest.mark.unit )






def    _A	(   lowerCamelCase ):
   config.addinivalue_line("markers"				,			"torchaudio_latest: mark test to run with torchaudio>=0.12" )






@pytest.fixture(autouse=lowerCamelCase )
def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
   a__						:    Tuple   		=	tmp_path_factory.getbasetemp() / "cache"
   a__						:    Optional[int]   		=	test_hf_cache_home / "datasets"
   a__						:    List[str]   		=	test_hf_cache_home / "metrics"
   a__						:    Union[str, Any]   		=	test_hf_cache_home / "modules"
   monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE"				,			str(lowerCamelCase ) )
   monkeypatch.setattr("datasets.config.HF_METRICS_CACHE"				,			str(lowerCamelCase ) )
   monkeypatch.setattr("datasets.config.HF_MODULES_CACHE"				,			str(lowerCamelCase ) )
   a__						:    int   		=	test_hf_datasets_cache / "downloads"
   monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"				,			str(lowerCamelCase ) )
   a__						:    Tuple   		=	test_hf_datasets_cache / "downloads" / "extracted"
   monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"				,			str(lowerCamelCase ) )






@pytest.fixture(autouse=lowerCamelCase				,			scope="session" )
def    _A	(   ):
   datasets.disable_progress_bar()






@pytest.fixture(autouse=lowerCamelCase )
def    _A	(   lowerCamelCase ):
   # don't take tests into account when counting downloads
   monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"				,			lowerCamelCase )






@pytest.fixture
def    _A	(   lowerCamelCase ):
   # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
   # To be removed once SQLAlchemy 2.0 supported
   monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"				,			lowerCamelCase )








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 convert_to_rgb, 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


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE__							:      List[str]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					["""pixel_values"""]





   def __init__(			self       ,      snake_case = True       ,      snake_case = None       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = True       ,      snake_case = 1 / 255       ,      snake_case = True       ,      snake_case = None       ,      snake_case = None       ,      snake_case = True       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      super().__init__(**snake_case   )
      a__						:    List[str]   		=	size if size is not None else {"height": 384, "width": 384}
      a__						:    List[str]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    Any   		=	do_resize
      a__						:    Optional[int]   		=	size
      a__						:    int   		=	resample
      a__						:    Optional[Any]   		=	do_rescale
      a__						:    Dict   		=	rescale_factor
      a__						:    Dict   		=	do_normalize
      a__						:    Any   		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a__						:    List[str]   		=	image_std if image_std is not None else OPENAI_CLIP_STD
      a__						:    str   		=	do_convert_rgb





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    Optional[int]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )
      if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""   )
      a__						:    Optional[int]   		=	(size["height"], size["width"])
      return resize(snake_case       ,      size=snake_case       ,      resample=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	List[str]:
      """simple docstring"""





      return rescale(snake_case       ,      scale=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      return normalize(snake_case       ,      mean=snake_case       ,      std=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			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 = ChannelDimension.FIRST       ,      **snake_case       ,      )					->	PIL.Image.Image:
      """simple docstring"""





      a__						:    List[str]   		=	do_resize if do_resize is not None else self.do_resize
      a__						:    Optional[Any]   		=	resample if resample is not None else self.resample
      a__						:    int   		=	do_rescale if do_rescale is not None else self.do_rescale
      a__						:    int   		=	rescale_factor if rescale_factor is not None else self.rescale_factor
      a__						:    Union[str, Any]   		=	do_normalize if do_normalize is not None else self.do_normalize
      a__						:    List[Any]   		=	image_mean if image_mean is not None else self.image_mean
      a__						:    int   		=	image_std if image_std is not None else self.image_std
      a__						:    Tuple   		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a__						:    Tuple   		=	size if size is not None else self.size
      a__						:    Any   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    str   		=	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_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:
         a__						:    List[str]   		=	[convert_to_rgb(snake_case   ) for image in images]

      # All transformations expect numpy arrays.
      a__						:    Dict   		=	[to_numpy_array(snake_case   ) for image in images]

      if do_resize:
         a__						:    Union[str, Any]   		=	[self.resize(image=snake_case       ,      size=snake_case       ,      resample=snake_case   ) for image in images]

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

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

      a__						:    Union[str, Any]   		=	[to_channel_dimension_format(snake_case       ,      snake_case   ) for image in images]

      a__						:    List[Any]   		=	BatchFeature(data={"pixel_values": images}       ,      tensor_type=snake_case   )

      return encoded_outputs

