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__    							= logging.get_logger(__name__)




def        lowerCAmelCase_			(					__A							)					->    List[List[ImageInput]]:







      '''simple docstring'''


      if isinstance(__A,  (list, tuple)							) and isinstance(videos[0],  (list, tuple)							) and is_valid_image(videos[0][0]							):
            return videos

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

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

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







class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[str]              =      ['pixel_values']


   def __init__(self					:						Dict	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						PILImageResampling = PILImageResampling.BILINEAR	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Union[int, float] = 1 / 2_5_5	,  __UpperCAmelCase					:						bool = True	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    None:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase		)
         UpperCAmelCase__								=  size if size is not None else {"shortest_edge": 2_2_4}
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  default_to_square=__UpperCAmelCase		)
         UpperCAmelCase__								=  crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  param_name="crop_size"		)

         UpperCAmelCase__								=  do_resize
         UpperCAmelCase__								=  size
         UpperCAmelCase__								=  do_center_crop
         UpperCAmelCase__								=  crop_size
         UpperCAmelCase__								=  resample
         UpperCAmelCase__								=  do_rescale
         UpperCAmelCase__								=  rescale_factor
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase__								=  image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Dict[str, int]	,  __UpperCAmelCase					:						PILImageResampling = PILImageResampling.BILINEAR	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  default_to_square=__UpperCAmelCase		)
         if "shortest_edge" in size:
               UpperCAmelCase__								=  get_resize_output_image_size(__UpperCAmelCase	,  size["shortest_edge"]	,  default_to_square=__UpperCAmelCase		)
         elif "height" in size and "width" in size:
               UpperCAmelCase__								=  (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       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Dict[str, int]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Optional[int]	,  )						->    np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  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       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Union[int, float]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						Union[str, Any]	,  )						->    List[Any]:
         """simple docstring"""



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


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						np.ndarray	,  __UpperCAmelCase					:						Union[float, List[float]]	,  __UpperCAmelCase					:						Union[float, List[float]]	,  __UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None	,  **__UpperCAmelCase					:						List[str]	,  )						->    np.ndarray:
         """simple docstring"""



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


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						ImageInput	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						PILImageResampling = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[ChannelDimension] = ChannelDimension.FIRST	,  )						->    np.ndarray:
         """simple docstring"""



         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.
         UpperCAmelCase__								=  to_numpy_array(__UpperCAmelCase		)

         if do_resize:
               UpperCAmelCase__								=  self.resize(image=__UpperCAmelCase	,  size=__UpperCAmelCase	,  resample=__UpperCAmelCase		)

         if do_center_crop:
               UpperCAmelCase__								=  self.center_crop(__UpperCAmelCase	,  size=__UpperCAmelCase		)

         if do_rescale:
               UpperCAmelCase__								=  self.rescale(image=__UpperCAmelCase	,  scale=__UpperCAmelCase		)

         if do_normalize:
               UpperCAmelCase__								=  self.normalize(image=__UpperCAmelCase	,  mean=__UpperCAmelCase	,  std=__UpperCAmelCase		)

         UpperCAmelCase__								=  to_channel_dimension_format(__UpperCAmelCase	,  __UpperCAmelCase		)
         return image





   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						ImageInput	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						PILImageResampling = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Dict[str, int] = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						float = None	,  __UpperCAmelCase					:						bool = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[float, List[float]]] = None	,  __UpperCAmelCase					:						Optional[Union[str, TensorType]] = None	,  __UpperCAmelCase					:						ChannelDimension = ChannelDimension.FIRST	,  **__UpperCAmelCase					:						int	,  )						->    PIL.Image.Image:
         """simple docstring"""



         UpperCAmelCase__								=  do_resize if do_resize is not None else self.do_resize
         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__								=  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__								=  size if size is not None else self.size
         UpperCAmelCase__								=  get_size_dict(__UpperCAmelCase	,  default_to_square=__UpperCAmelCase		)
         UpperCAmelCase__								=  crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase__								=  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."		)

         UpperCAmelCase__								=  make_batched(__UpperCAmelCase		)

         UpperCAmelCase__								=  [
             [
                 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
         ]

         UpperCAmelCase__								=  {"pixel_values": videos}
         return BatchFeature(data=__UpperCAmelCase	,  tensor_type=__UpperCAmelCase		)




from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    							= {'tokenization_bertweet': ['BertweetTokenizer']}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

      UpperCamelCase__    							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



