





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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


A   : int			=				logging.get_logger(__name__)


class   A      (SCREAMING_SNAKE_CASE   ):
   '''simple docstring'''




   __lowerCamelCase     :	Optional[Any]          =   ['''pixel_values''']






   def __init__(      self       :   Dict							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = 0.9							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   Union[int, float] = 1 / 2_55							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     **__lowerCAmelCase       :   Optional[int]							,     )     ->     None:







     """simple docstring"""






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

     A__  				=       do_resize
     A__  				=       size
     A__  				=       crop_pct
     A__  				=       resample
     A__  				=       do_center_crop
     A__  				=       crop_size
     A__  				=       do_rescale
     A__  				=       rescale_factor
     A__  				=       do_normalize
     A__  				=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     A__  				=       image_std if image_std is not None else IMAGENET_DEFAULT_STD






   def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[float] = None							,     __lowerCAmelCase       :   PILImageResampling = PILImageResampling.BICUBIC							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







     """simple docstring"""






     A__  				=       get_size_dict(__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
     if "shortest_edge" not in size and ("height" not in size or "width" not in size):
       raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
     if crop_pct is not None:
       if "shortest_edge" in size:
         A__  				=       int(size["""shortest_edge"""] / crop_pct       )
       elif "height" in size and "width" in size:
         if size["height"] == size["width"]:
           A__  				=       int(size["""height"""] / crop_pct       )
         else:
           A__  				=       (int(size["""height"""] / crop_pct       ), int(size["""width"""] / crop_pct       ))
       else:
         raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

       A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=__lowerCAmelCase							,     default_to_square=__lowerCAmelCase       )
     else:
       if "shortest_edge" in size:
         A__  				=       get_resize_output_image_size(__lowerCAmelCase							,     size=size["""shortest_edge"""]							,     default_to_square=__lowerCAmelCase       )
       elif "height" in size and "width" in size:
         A__  				=       (size["""height"""], size["""width"""])
       else:
         raise ValueError("""Invalid size for resize: {}""".format(__lowerCAmelCase       )       )

     return resize(__lowerCAmelCase							,     size=__lowerCAmelCase							,     resample=__lowerCAmelCase							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






   def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Dict[str, int]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Optional[Any]							,     )     ->     np.ndarray:







     """simple docstring"""






     A__  				=       get_size_dict(__lowerCAmelCase       )
     if "height" not in size or "width" not in size:
       raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
     return center_crop(__lowerCAmelCase							,     size=(size["""height"""], size["""width"""])							,     data_format=__lowerCAmelCase							,     **__lowerCAmelCase       )






   def        a_	(      self       :   str							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[int, float]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   Dict							,     )     ->     List[str]:







     """simple docstring"""






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






   def        a_	(      self       :   int							,     __lowerCAmelCase       :   np.ndarray							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Union[float, List[float]]							,     __lowerCAmelCase       :   Optional[Union[str, ChannelDimension]] = None							,     **__lowerCAmelCase       :   int							,     )     ->     np.ndarray:







     """simple docstring"""






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







   def        a_	(      self       :   Optional[Any]							,     __lowerCAmelCase       :   ImageInput							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   int = None							,     __lowerCAmelCase       :   PILImageResampling = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Dict[str, int] = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   float = None							,     __lowerCAmelCase       :   bool = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[float, List[float]]] = None							,     __lowerCAmelCase       :   Optional[Union[str, TensorType]] = None							,     __lowerCAmelCase       :   ChannelDimension = ChannelDimension.FIRST							,     **__lowerCAmelCase       :   Tuple							,     )     ->     PIL.Image.Image:







     """simple docstring"""






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

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

     A__  				=       make_list_of_images(__lowerCAmelCase       )

     if not valid_images(__lowerCAmelCase       ):
       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 or resample is None:
       raise ValueError("""Size and resample must be specified if do_resize is True."""       )

     if do_center_crop and crop_pct is None:
       raise ValueError("""Crop_pct 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.
     A__  				=       [to_numpy_array(__lowerCAmelCase       ) for image in images]

     if do_resize:
       A__  				=       [self.resize(image=__lowerCAmelCase							,     size=__lowerCAmelCase							,     crop_pct=__lowerCAmelCase							,     resample=__lowerCAmelCase       ) for image in images]

     if do_center_crop:
       A__  				=       [self.center_crop(image=__lowerCAmelCase							,     size=__lowerCAmelCase       ) for image in images]

     if do_rescale:
       A__  				=       [self.rescale(image=__lowerCAmelCase							,     scale=__lowerCAmelCase       ) for image in images]

     if do_normalize:
       A__  				=       [self.normalize(image=__lowerCAmelCase							,     mean=__lowerCAmelCase							,     std=__lowerCAmelCase       ) for image in images]

     A__  				=       [to_channel_dimension_format(__lowerCAmelCase							,     __lowerCAmelCase       ) for image in images]

     A__  				=       {"""pixel_values""": images}
     return BatchFeature(data=__lowerCAmelCase							,     tensor_type=__lowerCAmelCase       )










from collections import OrderedDict
from typing import Mapping

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


A   : Tuple			=				logging.get_logger(__name__)

A   : Optional[int]			=				{
    '''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      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Union[str, Any]          =   '''roberta'''






			def __init__(      self       :   Any							,     __lowerCAmelCase       :   Tuple=5_02_65							,     __lowerCAmelCase       :   Optional[int]=7_68							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[Any]=30_72							,     __lowerCAmelCase       :   Any="gelu"							,     __lowerCAmelCase       :   Optional[Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Optional[Any]=5_12							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   List[str]=0.0_2							,     __lowerCAmelCase       :   Dict=1e-12							,     __lowerCAmelCase       :   Optional[int]=1							,     __lowerCAmelCase       :   Tuple=0							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   Dict="absolute"							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Union[str, Any]=None							,     **__lowerCAmelCase       :   str							,     )     ->     str:







					"""simple docstring"""






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

					A__  				=       vocab_size
					A__  				=       hidden_size
					A__  				=       num_hidden_layers
					A__  				=       num_attention_heads
					A__  				=       hidden_act
					A__  				=       intermediate_size
					A__  				=       hidden_dropout_prob
					A__  				=       attention_probs_dropout_prob
					A__  				=       max_position_embeddings
					A__  				=       type_vocab_size
					A__  				=       initializer_range
					A__  				=       layer_norm_eps
					A__  				=       position_embedding_type
					A__  				=       use_cache
					A__  				=       classifier_dropout


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@property
			def        a_	(      self       :   int       )     ->     Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






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



