



"""simple docstring"""




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							:   Dict				=				logging.get_logger(__name__)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  List[List[ImageInput]]:





     '''simple docstring'''

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

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

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

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






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					['pixel_values']



      def __init__(	self	:      List[Any]	,      A	:      bool = True	,      A	:      Dict[str, int] = None	,      A	:      PILImageResampling = PILImageResampling.BILINEAR	,      A	:      bool = True	,      A	:      Dict[str, int] = None	,      A	:      bool = True	,      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	:      Dict	,      ):
           super().__init__(**A		)
           _UpperCAmelCase     :      Any														=       size if size is not None else {"shortest_edge": 2_5_6}
           _UpperCAmelCase     :      List[str]														=       get_size_dict(A	,      default_to_square=A		)
           _UpperCAmelCase     :      Dict														=       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase     :      Optional[int]														=       get_size_dict(A	,      param_name="crop_size"		)

           _UpperCAmelCase     :      Optional[int]														=       do_resize
           _UpperCAmelCase     :      int														=       size
           _UpperCAmelCase     :      Optional[int]														=       do_center_crop
           _UpperCAmelCase     :      List[str]														=       crop_size
           _UpperCAmelCase     :      int														=       resample
           _UpperCAmelCase     :      Union[str, Any]														=       do_rescale
           _UpperCAmelCase     :      Dict														=       rescale_factor
           _UpperCAmelCase     :      Dict														=       offset
           _UpperCAmelCase     :      str														=       do_normalize
           _UpperCAmelCase     :      Union[str, Any]														=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _UpperCAmelCase     :      Optional[int]														=       image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 		snake_case_   (	self	:      Any	,      A	:      np.ndarray	,      A	:      Dict[str, int]	,      A	:      PILImageResampling = PILImageResampling.BILINEAR	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      List[str]	,      ):
           _UpperCAmelCase     :      int														=       get_size_dict(A	,      default_to_square=A		)
           if "shortest_edge" in size:
                _UpperCAmelCase     :      Tuple														=       get_resize_output_image_size(A	,      size["shortest_edge"]	,      default_to_square=A		)
           elif "height" in size and "width" in size:
                _UpperCAmelCase     :      Dict														=       (size["height"], size["width"])
           else:
                raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
           return resize(A	,      size=A	,      resample=A	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      Any	,      A	:      np.ndarray	,      A	:      Dict[str, int]	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      Optional[int]	,      ):
           _UpperCAmelCase     :      Optional[Any]														=       get_size_dict(A		)
           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(A	,      size=(size["height"], size["width"])	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      int	,      A	:      np.ndarray	,      A	:      Union[int, float]	,      A	:      bool = True	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      List[Any]	,      ):
           _UpperCAmelCase     :      Union[str, Any]														=       image.astype(np.floataa		)
           if offset:
                _UpperCAmelCase     :      Dict														=       image - (scale / 2)
           return rescale(A	,      scale=A	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      List[str]	,      A	:      np.ndarray	,      A	:      Union[float, List[float]]	,      A	:      Union[float, List[float]]	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      Union[str, Any]	,      ):
           return normalize(A	,      mean=A	,      std=A	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      str	,      A	:      ImageInput	,      A	:      bool = None	,      A	:      Dict[str, int] = None	,      A	:      PILImageResampling = None	,      A	:      bool = None	,      A	:      Dict[str, int] = None	,      A	:      bool = None	,      A	:      float = None	,      A	:      bool = None	,      A	:      bool = None	,      A	:      Optional[Union[float, List[float]]] = None	,      A	:      Optional[Union[float, List[float]]] = None	,      A	:      Optional[ChannelDimension] = ChannelDimension.FIRST	,      ):
           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.
           _UpperCAmelCase     :      str														=       to_numpy_array(A		)

           if do_resize:
                _UpperCAmelCase     :      Any														=       self.resize(image=A	,      size=A	,      resample=A		)

           if do_center_crop:
                _UpperCAmelCase     :      Tuple														=       self.center_crop(A	,      size=A		)

           if do_rescale:
                _UpperCAmelCase     :      Dict														=       self.rescale(image=A	,      scale=A	,      offset=A		)

           if do_normalize:
                _UpperCAmelCase     :      List[str]														=       self.normalize(image=A	,      mean=A	,      std=A		)

           _UpperCAmelCase     :      Tuple														=       to_channel_dimension_format(A	,      A		)
           return image



      def 		snake_case_   (	self	:      List[Any]	,      A	:      ImageInput	,      A	:      bool = None	,      A	:      Dict[str, int] = None	,      A	:      PILImageResampling = None	,      A	:      bool = None	,      A	:      Dict[str, int] = None	,      A	:      bool = None	,      A	:      float = None	,      A	:      bool = 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	:      List[str]	,      ):
           _UpperCAmelCase     :      int														=       do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase     :      Optional[Any]														=       resample if resample is not None else self.resample
           _UpperCAmelCase     :      List[str]														=       do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase     :      List[str]														=       do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase     :      str														=       rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase     :      List[Any]														=       offset if offset is not None else self.offset
           _UpperCAmelCase     :      Union[str, Any]														=       do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase     :      List[str]														=       image_mean if image_mean is not None else self.image_mean
           _UpperCAmelCase     :      List[Any]														=       image_std if image_std is not None else self.image_std

           _UpperCAmelCase     :      Dict														=       size if size is not None else self.size
           _UpperCAmelCase     :      List[str]														=       get_size_dict(A	,      default_to_square=A		)
           _UpperCAmelCase     :      int														=       crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase     :      Dict														=       get_size_dict(A	,      param_name="crop_size"		)

           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."		)

           _UpperCAmelCase     :      Optional[int]														=       make_batched(A		)

           _UpperCAmelCase     :      List[str]														=       [
               [
                   self._preprocess_image(
                       image=A	,      do_resize=A	,      size=A	,      resample=A	,      do_center_crop=A	,      crop_size=A	,      do_rescale=A	,      rescale_factor=A	,      offset=A	,      do_normalize=A	,      image_mean=A	,      image_std=A	,      data_format=A	,      )
                   for img in video
               ]
               for video in videos
           ]

           _UpperCAmelCase     :      Tuple														=       {"pixel_values": videos}
           return BatchFeature(data=A	,      tensor_type=A		)





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase							:   Optional[int]				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					'roberta'



      def __init__(	self	:      str	,      A	:      Optional[int]=5_0_2_6_5	,      A	:      Optional[int]=7_6_8	,      A	:      Tuple=1_2	,      A	:      Optional[Any]=1_2	,      A	:      Optional[int]=3_0_7_2	,      A	:      str="gelu"	,      A	:      List[Any]=0.1	,      A	:      Optional[Any]=0.1	,      A	:      Any=5_1_2	,      A	:      Dict=2	,      A	:      Union[str, Any]=0.02	,      A	:      str=1e-12	,      A	:      Optional[int]=1	,      A	:      List[str]=0	,      A	:      int=2	,      A	:      Any="absolute"	,      A	:      Optional[int]=True	,      A	:      int=None	,      **A	:      str	,      ):
           super().__init__(pad_token_id=A	,      bos_token_id=A	,      eos_token_id=A	,      **A		)

           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      Dict														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       hidden_act
           _UpperCAmelCase     :      Any														=       intermediate_size
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Tuple														=       max_position_embeddings
           _UpperCAmelCase     :      Union[str, Any]														=       type_vocab_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      int														=       layer_norm_eps
           _UpperCAmelCase     :      Tuple														=       position_embedding_type
           _UpperCAmelCase     :      int														=       use_cache
           _UpperCAmelCase     :      Dict														=       classifier_dropout







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Tuple		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      Optional[Any]														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      List[Any]														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("input_ids", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)
