






"""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, 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, is_vision_available, logging


if is_vision_available():
      import PIL


UpperCamelCase_	:				Optional[Any]										= logging.get_logger(__name__)


class      _lowercase (     lowerCAmelCase						):
      _a   :     str								=   ['''pixel_values''']


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





            """simple docstring"""







            super().__init__(**a						)
            __snake_case      :    Optional[int]  			=size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
            __snake_case      :    Dict  			=get_size_dict(a						)
            __snake_case      :    List[str]  			=crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
            __snake_case      :    Optional[Any]  			=get_size_dict(a							,   param_name='''crop_size'''						)

            __snake_case      :    Any  			=do_resize
            __snake_case      :    List[Any]  			=size
            __snake_case      :    Tuple  			=resample
            __snake_case      :    str  			=do_center_crop
            __snake_case      :    str  			=crop_size
            __snake_case      :    Dict  			=do_rescale
            __snake_case      :    int  			=rescale_factor
            __snake_case      :    List[Any]  			=do_normalize
            __snake_case      :    List[Any]  			=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __snake_case      :    List[Any]  			=image_std if image_std is not None else IMAGENET_STANDARD_STD


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	np.ndarray							,   a				:	Dict[str, int]							,   a				:	PILImageResampling = PIL.Image.BICUBIC							,   a				:	Optional[Union[str, ChannelDimension]] = None							,   **a				:	List[Any]							,   ):





            """simple docstring"""







            __snake_case      :    str  			=get_size_dict(a						)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
            return resize(
                a							,   size=(size['''height'''], size['''width'''])							,   resample=a							,   data_format=a							,   **a						)


      def _UpperCamelCase						(    self				:	Dict							,   a				:	np.ndarray							,   a				:	Dict[str, int]							,   a				:	Optional[Union[str, ChannelDimension]] = None							,   **a				:	Union[str, Any]							,   ):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=get_size_dict(a						)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
            return center_crop(a							,   size=(size['''height'''], size['''width'''])							,   data_format=a							,   **a						)


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





            """simple docstring"""







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


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





            """simple docstring"""







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







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





            """simple docstring"""







            __snake_case      :    Tuple  			=do_resize if do_resize is not None else self.do_resize
            __snake_case      :    List[Any]  			=resample if resample is not None else self.resample
            __snake_case      :    List[Any]  			=do_center_crop if do_center_crop is not None else self.do_center_crop
            __snake_case      :    str  			=do_rescale if do_rescale is not None else self.do_rescale
            __snake_case      :    Any  			=rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case      :    List[Any]  			=do_normalize if do_normalize is not None else self.do_normalize
            __snake_case      :    int  			=image_mean if image_mean is not None else self.image_mean
            __snake_case      :    str  			=image_std if image_std is not None else self.image_std

            __snake_case      :    List[str]  			=size if size is not None else self.size
            __snake_case      :    Dict  			=get_size_dict(a						)
            __snake_case      :    Tuple  			=crop_size if crop_size is not None else self.crop_size
            __snake_case      :    Tuple  			=get_size_dict(a							,   param_name='''crop_size'''						)

            __snake_case      :    Optional[Any]  			=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 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.
            __snake_case      :    List[str]  			=[to_numpy_array(a						) for image in images]

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

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

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

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

            __snake_case      :    Optional[Any]  			=[to_channel_dimension_format(a							,   a						) for image in images]

            __snake_case      :    Optional[Any]  			={'''pixel_values''': images}
            return BatchFeature(data=a							,   tensor_type=a						)










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_	:				Union[str, Any]										= {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


