





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


__lowerCamelCase       :     Tuple   =     logging.get_logger(__name__)



def 		A_				(     _lowerCAmelCase     )						-> List[List[ImageInput]]:
 if isinstance(_lowerCAmelCase							,   (list, tuple)     ) and isinstance(videos[0]							,   (list, tuple)     ) and is_valid_image(videos[0][0]     ):
  return videos

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

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

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



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   ['pixel_values']





      def __init__( self       ,							A_ = True       ,							A_ = None       ,							A_ = PILImageResampling.BILINEAR       ,							A_ = True       ,							A_ = None       ,							A_ = True       ,							A_ = 1 / 255       ,							A_ = True       ,							A_ = None       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(**A_   )
       UpperCamelCase	:					List[Any]       	=   size if size is not None else {"shortest_edge": 224}
       UpperCamelCase	:					int       	=   get_size_dict(A_       ,							default_to_square=A_   )
       UpperCamelCase	:					List[str]       	=   crop_size if crop_size is not None else {"height": 224, "width": 224}
       UpperCamelCase	:					Optional[Any]       	=   get_size_dict(A_       ,							param_name="crop_size"   )

       UpperCamelCase	:					Any       	=   do_resize
       UpperCamelCase	:					int       	=   size
       UpperCamelCase	:					Tuple       	=   do_center_crop
       UpperCamelCase	:					str       	=   crop_size
       UpperCamelCase	:					Tuple       	=   resample
       UpperCamelCase	:					str       	=   do_rescale
       UpperCamelCase	:					Tuple       	=   rescale_factor
       UpperCamelCase	:					int       	=   do_normalize
       UpperCamelCase	:					str       	=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase	:					Tuple       	=   image_std if image_std is not None else IMAGENET_STANDARD_STD





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = PILImageResampling.BILINEAR       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   get_size_dict(A_       ,							default_to_square=A_   )
       if "shortest_edge" in size:
        UpperCamelCase	:					str       	=   get_resize_output_image_size(A_       ,							size["shortest_edge"]       ,							default_to_square=A_   )
       elif "height" in size and "width" in size:
        UpperCamelCase	:					Any       	=   (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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




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





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = 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."   )

       # All transformations expect numpy arrays.
       UpperCamelCase	:					int       	=   to_numpy_array(A_   )

       if do_resize:
        UpperCamelCase	:					str       	=   self.resize(image=A_       ,							size=A_       ,							resample=A_   )

       if do_center_crop:
        UpperCamelCase	:					Dict       	=   self.center_crop(A_       ,							size=A_   )

       if do_rescale:
        UpperCamelCase	:					int       	=   self.rescale(image=A_       ,							scale=A_   )

       if do_normalize:
        UpperCamelCase	:					Union[str, Any]       	=   self.normalize(image=A_       ,							mean=A_       ,							std=A_   )

       UpperCamelCase	:					List[Any]       	=   to_channel_dimension_format(A_       ,							A_   )
       return image





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = ChannelDimension.FIRST       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   do_resize if do_resize is not None else self.do_resize
       UpperCamelCase	:					int       	=   resample if resample is not None else self.resample
       UpperCamelCase	:					Optional[int]       	=   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	:					List[Any]       	=   rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase	:					int       	=   do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase	:					int       	=   image_mean if image_mean is not None else self.image_mean
       UpperCamelCase	:					Any       	=   image_std if image_std is not None else self.image_std

       UpperCamelCase	:					List[Any]       	=   size if size is not None else self.size
       UpperCamelCase	:					Optional[Any]       	=   get_size_dict(A_       ,							default_to_square=A_   )
       UpperCamelCase	:					List[Any]       	=   crop_size if crop_size is not None else self.crop_size
       UpperCamelCase	:					List[Any]       	=   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	:					List[str]       	=   make_batched(A_   )

       UpperCamelCase	:					Tuple       	=   [
           [
               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_       ,							do_normalize=A_       ,							image_mean=A_       ,							image_std=A_       ,							data_format=A_       ,							)
               for img in video
           ]
           for video in videos
       ]

       UpperCamelCase	:					str       	=   {"pixel_values": videos}
       return BatchFeature(data=A_       ,							tensor_type=A_   )







from collections import OrderedDict
from typing import Mapping

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


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

__lowerCamelCase       :     str   =     {
    """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 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   'roberta'





      def __init__( self       ,							A_=5_0265       ,							A_=768       ,							A_=12       ,							A_=12       ,							A_=3072       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=2       ,							A_=0.02       ,							A_=1e-12       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_="absolute"       ,							A_=True       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							**A_   )

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



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task == "multiple-choice":
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase	:					Optional[int]       	=   {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]   )
