


'''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


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


if is_vision_available():
  import PIL
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	['pixel_values']


    def __init__(     self  ,					a = True  ,					a = None  ,					a = PILImageResampling.BICUBIC  ,					a = True  ,					a = None  ,					a = True  ,					a = 1 / 2_55  ,					a = True  ,					a = None  ,					a = None  ,					a = True  ,					**a  ,					):
          super().__init__(**a    )
          UpperCamelCase__										=				size if size is not None else {"shortest_edge": 2_24}
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a    )
          UpperCamelCase__										=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a  ,					param_name="crop_size"    )

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


    def    __a							(     self  ,					a  ,					a  ,					a = PILImageResampling.BICUBIC  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a    )
          if "shortest_edge" not in size:
                raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
          UpperCamelCase__										=				get_resize_output_image_size(a  ,					size=size["shortest_edge"]  ,					default_to_square=a    )
          return resize(a  ,					size=a  ,					resample=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(a    )
          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(a  ,					size=(size["height"], size["width"])  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          return rescale(a  ,					scale=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          return normalize(a  ,					mean=a  ,					std=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = ChannelDimension.FIRST  ,					**a  ,					):
          UpperCamelCase__										=				do_resize if do_resize is not None else self.do_resize
          UpperCamelCase__										=				size if size is not None else self.size
          UpperCamelCase__										=				get_size_dict(a  ,					param_name="size"  ,					default_to_square=a    )
          UpperCamelCase__										=				resample if resample is not None else self.resample
          UpperCamelCase__										=				do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase__										=				crop_size if crop_size is not None else self.crop_size
          UpperCamelCase__										=				get_size_dict(a  ,					param_name="crop_size"  ,					default_to_square=a    )
          UpperCamelCase__										=				do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase__										=				rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase__										=				do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase__										=				image_mean if image_mean is not None else self.image_mean
          UpperCamelCase__										=				image_std if image_std is not None else self.image_std
          UpperCamelCase__										=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          UpperCamelCase__										=				make_list_of_images(a    )

          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."    )

          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:
                UpperCamelCase__										=				[convert_to_rgb(a    ) for image in images]

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

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

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

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

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

          UpperCamelCase__										=				[to_channel_dimension_format(a  ,					a    ) for image in images]

          UpperCamelCase__										=				{"pixel_values": images}
          return BatchFeature(data=a  ,					tensor_type=a    )












'''simple docstring'''




import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    "kwargs, expected"			,     [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10       )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(__lowercase			,     i + 1       ) for i in range(10       )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1       )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0			,     4       ), range(4			,     7       ), range(7			,     10       )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0			,     1       ), range(1			,     2       ), range(2			,     3       )]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   _distribute_shards(**__lowercase       )
     assert out == expected
@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"			,     [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      Optional[Any]			,     __lowercase							:      int       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   _split_gen_kwargs(__lowercase			,     __lowercase       )
     assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"			,     [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      List[Any]       )		->       List[Any]:


     '''simple docstring'''



     if expected is RuntimeError:
          with pytest.raises(__lowercase       ):
               _number_of_shards_in_gen_kwargs(__lowercase       )
     else:
          _UpperCAmelCase     							=   _number_of_shards_in_gen_kwargs(__lowercase       )
          assert out == expected




