





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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_UpperCAmelCase				:       Any	   =				logging.get_logger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[List[ImageInput]]:
    if isinstance(__lowerCamelCase						,	(list, tuple) ) and isinstance(videos[0]						,	(list, tuple) ) and is_valid_image(videos[0][0] ):
        return videos

    elif isinstance(__lowerCamelCase						,	(list, tuple) ) and is_valid_image(videos[0] ):
        return [videos]

    elif is_valid_image(__lowerCamelCase ):
        return [[videos]]

    raise ValueError(F"""Could not make batched video from {videos}""" )


class    lowerCAmelCase							(					snake_case_			):
 UpperCAmelCase__													=		["""pixel_values"""]
 def __init__(      self  :      Dict			,     UpperCAmelCase  :      Tuple = True			,     UpperCAmelCase  :      List[Any] = None			,     UpperCAmelCase  :      Optional[int] = PILImageResampling.BILINEAR			,     UpperCAmelCase  :      Any = True			,     UpperCAmelCase  :      List[Any] = None			,     UpperCAmelCase  :      Optional[Any] = True			,     UpperCAmelCase  :      Union[str, Any] = 1 / 255			,     UpperCAmelCase  :      Optional[int] = True			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      Union[str, Any] = None			,     **UpperCAmelCase  :      Optional[int]			,     )			->	None:
     super().__init__(**UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		size if size is not None else {'shortest_edge': 224}
     lowerCamelCase__    :      int					 =		get_size_dict(UpperCAmelCase			,     default_to_square=UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__    :      Tuple					 =		get_size_dict(UpperCAmelCase			,     param_name='crop_size'	)

     lowerCamelCase__    :      Dict					 =		do_resize
     lowerCamelCase__    :      List[str]					 =		size
     lowerCamelCase__    :      List[str]					 =		do_center_crop
     lowerCamelCase__    :      Optional[int]					 =		crop_size
     lowerCamelCase__    :      List[str]					 =		resample
     lowerCamelCase__    :      int					 =		do_rescale
     lowerCamelCase__    :      List[str]					 =		rescale_factor
     lowerCamelCase__    :      List[Any]					 =		do_normalize
     lowerCamelCase__    :      List[Any]					 =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__    :      Any					 =		image_std if image_std is not None else IMAGENET_STANDARD_STD
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Optional[Any] = PILImageResampling.BILINEAR			,     UpperCAmelCase  :      Tuple = None			,     **UpperCAmelCase  :      int			,     )			->	np.ndarray:
     lowerCamelCase__    :      Optional[int]					 =		get_size_dict(UpperCAmelCase			,     default_to_square=UpperCAmelCase	)
     if "shortest_edge" in size:
         lowerCamelCase__    :      Any					 =		get_resize_output_image_size(UpperCAmelCase			,     size['shortest_edge']			,     default_to_square=UpperCAmelCase	)
     elif "height" in size and "width" in size:
         lowerCamelCase__    :      Any					 =		(size['height'], size['width'])
     else:
         raise ValueError(F"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""	)
     return resize(UpperCAmelCase			,     size=UpperCAmelCase			,     resample=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      List[Any] = None			,     **UpperCAmelCase  :      Optional[int]			,     )			->	np.ndarray:
     lowerCamelCase__    :      Optional[int]					 =		get_size_dict(UpperCAmelCase	)
     if "height" not in size or "width" not in size:
         raise ValueError(F"""Size must have \'height\' and \'width\' as keys. Got {size.keys()}"""	)
     return center_crop(UpperCAmelCase			,     size=(size['height'], size['width'])			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Optional[int] = None			,     **UpperCAmelCase  :      Any			,     )			->	List[str]:
     return rescale(UpperCAmelCase			,     scale=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Optional[int] = None			,     **UpperCAmelCase  :      Optional[int]			,     )			->	np.ndarray:
     return normalize(UpperCAmelCase			,     mean=UpperCAmelCase			,     std=UpperCAmelCase			,     data_format=UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      List[Any]			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      Optional[Any] = None			,     UpperCAmelCase  :      Any = None			,     UpperCAmelCase  :      List[Any] = None			,     UpperCAmelCase  :      Tuple = None			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      Any = None			,     UpperCAmelCase  :      Tuple = None			,     UpperCAmelCase  :      List[str] = ChannelDimension.FIRST			,     )			->	np.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_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.
     lowerCamelCase__    :      int					 =		to_numpy_array(UpperCAmelCase	)

     if do_resize:
         lowerCamelCase__    :      Tuple					 =		self.resize(image=UpperCAmelCase			,     size=UpperCAmelCase			,     resample=UpperCAmelCase	)

     if do_center_crop:
         lowerCamelCase__    :      Union[str, Any]					 =		self.center_crop(UpperCAmelCase			,     size=UpperCAmelCase	)

     if do_rescale:
         lowerCamelCase__    :      Union[str, Any]					 =		self.rescale(image=UpperCAmelCase			,     scale=UpperCAmelCase	)

     if do_normalize:
         lowerCamelCase__    :      Union[str, Any]					 =		self.normalize(image=UpperCAmelCase			,     mean=UpperCAmelCase			,     std=UpperCAmelCase	)

     lowerCamelCase__    :      List[str]					 =		to_channel_dimension_format(UpperCAmelCase			,     UpperCAmelCase	)
     return image







 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any] = None			,     UpperCAmelCase  :      List[Any] = None			,     UpperCAmelCase  :      str = None			,     UpperCAmelCase  :      List[Any] = None			,     UpperCAmelCase  :      int = None			,     UpperCAmelCase  :      Dict = None			,     UpperCAmelCase  :      int = None			,     UpperCAmelCase  :      Tuple = None			,     UpperCAmelCase  :      Tuple = None			,     UpperCAmelCase  :      Tuple = None			,     UpperCAmelCase  :      int = None			,     UpperCAmelCase  :      Optional[Any] = ChannelDimension.FIRST			,     **UpperCAmelCase  :      int			,     )			->	PIL.Image.Image:
     lowerCamelCase__    :      List[Any]					 =		do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__    :      Dict					 =		resample if resample is not None else self.resample
     lowerCamelCase__    :      Tuple					 =		do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__    :      Dict					 =		do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__    :      Optional[int]					 =		rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__    :      Optional[Any]					 =		do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__    :      Union[str, Any]					 =		image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__    :      List[str]					 =		image_std if image_std is not None else self.image_std

     lowerCamelCase__    :      List[Any]					 =		size if size is not None else self.size
     lowerCamelCase__    :      Union[str, Any]					 =		get_size_dict(UpperCAmelCase			,     default_to_square=UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__    :      Dict					 =		get_size_dict(UpperCAmelCase			,     param_name='crop_size'	)

     if not valid_images(UpperCAmelCase	):
         raise ValueError(
             'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
             'torch.Tensor, tf.Tensor or jax.ndarray.'	)

     lowerCamelCase__    :      Optional[Any]					 =		make_batched(UpperCAmelCase	)

     lowerCamelCase__    :      str					 =		[
         [
             self._preprocess_image(
                 image=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=UpperCAmelCase			,     image_std=UpperCAmelCase			,     data_format=UpperCAmelCase			,     )
             for img in video
         ]
         for video in videos
     ]

     lowerCamelCase__    :      Union[str, Any]					 =		{'pixel_values': videos}
     return BatchFeature(data=UpperCAmelCase			,     tensor_type=UpperCAmelCase	)












'''simple docstring'''

import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowercase_					= {
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Dict					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]    )						->Tuple:
    if got_ver is None or want_ver is None:
        raise ValueError(
            F'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'
            F' reinstalling {pkg}.'    )
    if not ops[op](version.parse(__lowerCamelCase    )					,       version.parse(__lowerCamelCase    )    ):
        raise ImportError(
            F'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'    )





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[str] = None    )						->None:
    _SCREAMING_SNAKE_CASE									=			F'\n{hint}' if hint is not None else """"""

    # non-versioned check
    if re.match(R"""^[\w_\-\d]+$"""					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			requirement, None, None
    else:
        _SCREAMING_SNAKE_CASE									=			re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""					,       __lowerCamelCase    )
        if not match:
            raise ValueError(
                """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
                F' got {requirement}'    )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			match[0]
        _SCREAMING_SNAKE_CASE									=			want_full.split(""","""    )  # there could be multiple requirements
        _SCREAMING_SNAKE_CASE									=			{}
        for w in want_range:
            _SCREAMING_SNAKE_CASE									=			re.findall(R"""^([\s!=<>]{1,2})(.+)"""					,       __lowerCamelCase    )
            if not match:
                raise ValueError(
                    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
                    F' but got {requirement}'    )
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			match[0]
            _SCREAMING_SNAKE_CASE									=			want_ver
            if op not in ops:
                raise ValueError(F'{requirement}: need one of {list(ops.keys()    )}, but got {op}'    )

    # special case
    if pkg == "python":
        _SCREAMING_SNAKE_CASE									=			""".""".join([str(__lowerCamelCase    ) for x in sys.version_info[:3]]    )
        for op, want_ver in wanted.items():
            _compare_versions(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
        return

    # check if any version is installed
    try:
        _SCREAMING_SNAKE_CASE									=			importlib.metadata.version(__lowerCamelCase    )
    except importlib.metadata.PackageNotFoundError:
        raise importlib.metadata.PackageNotFoundError(
            F'The \'{requirement}\' distribution was not found and is required by this application. {hint}'    )

    # check that the right version is installed if version number or a range was provided
    if want_ver is not None:
        for op, want_ver in wanted.items():
            _compare_versions(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )






def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]    )						->str:
    _SCREAMING_SNAKE_CASE									=			"""Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
    return require_version(__lowerCamelCase					,       __lowerCamelCase    )



