from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
   import PIL

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



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> 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 __snake_case    (						lowerCamelCase_		):






   """simple docstring"""




   lowerCAmelCase_														=							['''pixel_values''']




   def __init__(   self      :    List[Any]	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = True	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    bool = True	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     super().__init__(**_snake_case				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_snake_case	,     default_to_square=_snake_case				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_snake_case	,     param_name="""crop_size"""				)

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




   def      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_snake_case	,     default_to_square=_snake_case				)
     if "shortest_edge" in size:
       SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(_snake_case	,     size["""shortest_edge"""]	,     default_to_square=_snake_case				)
     elif "height" in size and "width" in size:
       SCREAMING_SNAKE_CASE__      							=				(size["""height"""], size["""width"""])
     else:
       raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
     return resize(_snake_case	,     size=_snake_case	,     resample=_snake_case	,     data_format=_snake_case	,     **_snake_case				)




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_snake_case				)
     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(_snake_case	,     size=(size["""height"""], size["""width"""])	,     data_format=_snake_case	,     **_snake_case				)




   def      __a						(   self      :    Any	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    bool = True	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				image.astype(np.floataa				)
     if offset:
       SCREAMING_SNAKE_CASE__      							=				image - (scale / 2)
     return rescale(_snake_case	,     scale=_snake_case	,     data_format=_snake_case	,     **_snake_case				)




   def      __a						(   self      :    Dict	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[float, List[float]]	,     _lowercase      :    Union[float, List[float]]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






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




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[ChannelDimension] = ChannelDimension.FIRST	,     ):



     """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."""				)

     if offset and not do_rescale:
       raise ValueError("""For offset, do_rescale must also be set to True."""				)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__      							=				to_numpy_array(_snake_case				)

     if do_resize:
       SCREAMING_SNAKE_CASE__      							=				self.resize(image=_snake_case	,     size=_snake_case	,     resample=_snake_case				)

     if do_center_crop:
       SCREAMING_SNAKE_CASE__      							=				self.center_crop(_snake_case	,     size=_snake_case				)

     if do_rescale:
       SCREAMING_SNAKE_CASE__      							=				self.rescale(image=_snake_case	,     scale=_snake_case	,     offset=_snake_case				)

     if do_normalize:
       SCREAMING_SNAKE_CASE__      							=				self.normalize(image=_snake_case	,     mean=_snake_case	,     std=_snake_case				)

     SCREAMING_SNAKE_CASE__      							=				to_channel_dimension_format(_snake_case	,     _snake_case				)
     return image




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				offset if offset is not None else self.offset
     SCREAMING_SNAKE_CASE__      							=				do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_snake_case	,     default_to_square=_snake_case				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(_snake_case	,     param_name="""crop_size"""				)

     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."""				)

     SCREAMING_SNAKE_CASE__      							=				make_batched(_snake_case				)

     SCREAMING_SNAKE_CASE__      							=				[
         [
             self._preprocess_image(
                 image=_snake_case	,     do_resize=_snake_case	,     size=_snake_case	,     resample=_snake_case	,     do_center_crop=_snake_case	,     crop_size=_snake_case	,     do_rescale=_snake_case	,     rescale_factor=_snake_case	,     offset=_snake_case	,     do_normalize=_snake_case	,     image_mean=_snake_case	,     image_std=_snake_case	,     data_format=_snake_case	,     )
             for img in video
         ]
         for video in videos
     ]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": videos}
     return BatchFeature(data=_snake_case	,     tensor_type=_snake_case				)

from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
