
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


lowercase__          =logging.get_logger(__name__)


if is_vision_available():
						import PIL



class 		UpperCamelCase__  (	__lowercase   ):
			_SCREAMING_SNAKE_CASE				:							str     	=				["pixel_values"]
			def __init__(self			: List[Any] ,    snake_case_			: bool = True ,    snake_case_			: Dict[str, int] = None ,    snake_case_			: PILImageResampling = PILImageResampling.BICUBIC ,    snake_case_			: bool = True ,    snake_case_			: Dict[str, int] = None ,    snake_case_			: bool = True ,    snake_case_			: Union[int, float] = 1 / 2_5_5 ,    snake_case_			: bool = True ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: bool = True ,    **snake_case_			: Any ,    ):
				super().__init__(**snake_case_       )
				__a			:  List[str]		    =  size if size is not None else {'''shortest_edge''': 2_2_4}
				__a			:  int		    =  get_size_dict(snake_case_ ,    default_to_square=snake_case_       )
				__a			:  List[Any]		    =  crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
				__a			:  Union[str, Any]		    =  get_size_dict(snake_case_ ,    default_to_square=snake_case_ ,    param_name='''crop_size'''       )

				__a			:  int		    =  do_resize
				__a			:  List[Any]		    =  size
				__a			:  Optional[int]		    =  resample
				__a			:  Dict		    =  do_center_crop
				__a			:  Optional[Any]		    =  crop_size
				__a			:  List[str]		    =  do_rescale
				__a			:  int		    =  rescale_factor
				__a			:  Dict		    =  do_normalize
				__a			:  Any		    =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				__a			:  int		    =  image_std if image_std is not None else OPENAI_CLIP_STD
				__a			:  Dict		    =  do_convert_rgb
			def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: np.ndarray ,    snake_case_			: Dict[str, int] ,    snake_case_			: PILImageResampling = PILImageResampling.BICUBIC ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: Optional[Any] ,    ):
				__a			:  int		    =  get_size_dict(snake_case_ ,    default_to_square=snake_case_       )
				if "shortest_edge" not in size:
					raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"       )
				__a			:  Optional[int]		    =  get_resize_output_image_size(snake_case_ ,    size=size['''shortest_edge'''] ,    default_to_square=snake_case_       )
				return resize(snake_case_ ,    size=snake_case_ ,    resample=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
			def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: np.ndarray ,    snake_case_			: Dict[str, int] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: List[Any] ,    ):
				__a			:  Optional[Any]		    =  get_size_dict(snake_case_       )
				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(snake_case_ ,    size=(size['''height'''], size['''width''']) ,    data_format=snake_case_ ,    **snake_case_       )
			def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: np.ndarray ,    snake_case_			: Union[int, float] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: int ,    ):
				return rescale(snake_case_ ,    scale=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
			def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: np.ndarray ,    snake_case_			: Union[float, List[float]] ,    snake_case_			: Union[float, List[float]] ,    snake_case_			: Optional[Union[str, ChannelDimension]] = None ,    **snake_case_			: Any ,    ):
				return normalize(snake_case_ ,    mean=snake_case_ ,    std=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )







			def 				lowerCAmelCase      (self			: str ,    snake_case_			: ImageInput ,    snake_case_			: bool = None ,    snake_case_			: Dict[str, int] = None ,    snake_case_			: PILImageResampling = None ,    snake_case_			: bool = None ,    snake_case_			: int = None ,    snake_case_			: bool = None ,    snake_case_			: float = None ,    snake_case_			: bool = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: Optional[Union[float, List[float]]] = None ,    snake_case_			: bool = None ,    snake_case_			: Optional[Union[str, TensorType]] = None ,    snake_case_			: Optional[ChannelDimension] = ChannelDimension.FIRST ,    **snake_case_			: List[str] ,    ):
				__a			:  List[Any]		    =  do_resize if do_resize is not None else self.do_resize
				__a			:  int		    =  size if size is not None else self.size
				__a			:  Optional[int]		    =  get_size_dict(snake_case_ ,    param_name='''size''' ,    default_to_square=snake_case_       )
				__a			:  List[Any]		    =  resample if resample is not None else self.resample
				__a			:  str		    =  do_center_crop if do_center_crop is not None else self.do_center_crop
				__a			:  str		    =  crop_size if crop_size is not None else self.crop_size
				__a			:  int		    =  get_size_dict(snake_case_ ,    param_name='''crop_size''' ,    default_to_square=snake_case_       )
				__a			:  Dict		    =  do_rescale if do_rescale is not None else self.do_rescale
				__a			:  Union[str, Any]		    =  rescale_factor if rescale_factor is not None else self.rescale_factor
				__a			:  List[Any]		    =  do_normalize if do_normalize is not None else self.do_normalize
				__a			:  str		    =  image_mean if image_mean is not None else self.image_mean
				__a			:  List[str]		    =  image_std if image_std is not None else self.image_std
				__a			:  Optional[int]		    =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				__a			:  Union[str, Any]		    =  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:
					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:
					__a			:  Optional[Any]		    =  [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			:  Any		    =  [self.resize(image=snake_case_ ,    size=snake_case_ ,    resample=snake_case_       ) for image in images]

				if do_center_crop:
					__a			:  Tuple		    =  [self.center_crop(image=snake_case_ ,    size=snake_case_       ) for image in images]

				if do_rescale:
					__a			:  Optional[int]		    =  [self.rescale(image=snake_case_ ,    scale=snake_case_       ) for image in images]

				if do_normalize:
					__a			:  str		    =  [self.normalize(image=snake_case_ ,    mean=snake_case_ ,    std=snake_case_       ) for image in images]

				__a			:  Dict		    =  [to_channel_dimension_format(snake_case_ ,    snake_case_       ) for image in images]

				__a			:  int		    =  {'''pixel_values''': images}
				return BatchFeature(data=snake_case_ ,    tensor_type=snake_case_       )


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				CustomTokenizer
   pass
