


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


__snake_case      = logging.get_logger(__name__)




class  UpperCAmelCase       (							__snake_case     ):
			lowercase						       =			["pixel_values"]




			def __init__(    self				:		Tuple							,							__magic_name__				:		bool = True							,							__magic_name__				:		Dict[str, int] = None							,							__magic_name__				:		int = 0.9							,							__magic_name__				:		PILImageResampling = PILImageResampling.BICUBIC							,							__magic_name__				:		bool = True							,							__magic_name__				:		Dict[str, int] = None							,							__magic_name__				:		Union[int, float] = 1 / 2_5_5							,							__magic_name__				:		bool = True							,							__magic_name__				:		bool = True							,							__magic_name__				:		Optional[Union[float, List[float]]] = None							,							__magic_name__				:		Optional[Union[float, List[float]]] = None							,							**__magic_name__				:		Any							,							):







						"""simple docstring"""







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

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




			def 	lowerCamelCase_       (    self				:		Optional[int]							,							__magic_name__				:		np.ndarray							,							__magic_name__				:		Dict[str, int]							,							__magic_name__				:		Optional[float] = None							,							__magic_name__				:		PILImageResampling = PILImageResampling.BICUBIC							,							__magic_name__				:		Optional[Union[str, ChannelDimension]] = None							,							**__magic_name__				:		Any							,							):







						"""simple docstring"""







						UpperCamelCase								=       get_size_dict(SCREAMING_SNAKE_CASE__							,							default_to_square=SCREAMING_SNAKE_CASE__    )
						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:
												UpperCamelCase								=       int(size["""shortest_edge"""] / crop_pct    )
									elif "height" in size and "width" in size:
												if size["height"] == size["width"]:
															UpperCamelCase								=       int(size["""height"""] / crop_pct    )
												else:
															UpperCamelCase								=       (int(size["""height"""] / crop_pct    ), int(size["""width"""] / crop_pct    ))
									else:
												raise ValueError("""Invalid size for resize: {}""".format(SCREAMING_SNAKE_CASE__    )    )

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

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




			def 	lowerCamelCase_       (    self				:		Tuple							,							__magic_name__				:		np.ndarray							,							__magic_name__				:		Dict[str, int]							,							__magic_name__				:		Optional[Union[str, ChannelDimension]] = None							,							**__magic_name__				:		List[Any]							,							):







						"""simple docstring"""







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




			def 	lowerCamelCase_       (    self				:		Union[str, Any]							,							__magic_name__				:		np.ndarray							,							__magic_name__				:		Union[int, float]							,							__magic_name__				:		Optional[Union[str, ChannelDimension]] = None							,							**__magic_name__				:		List[str]							,							):







						"""simple docstring"""







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




			def 	lowerCamelCase_       (    self				:		List[Any]							,							__magic_name__				:		np.ndarray							,							__magic_name__				:		Union[float, List[float]]							,							__magic_name__				:		Union[float, List[float]]							,							__magic_name__				:		Optional[Union[str, ChannelDimension]] = None							,							**__magic_name__				:		int							,							):







						"""simple docstring"""







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





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







						"""simple docstring"""







						UpperCamelCase								=       do_resize if do_resize is not None else self.do_resize
						UpperCamelCase								=       crop_pct if crop_pct is not None else self.crop_pct
						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(SCREAMING_SNAKE_CASE__							,							default_to_square=SCREAMING_SNAKE_CASE__    )
						UpperCamelCase								=       crop_size if crop_size is not None else self.crop_size
						UpperCamelCase								=       get_size_dict(SCREAMING_SNAKE_CASE__							,							param_name="""crop_size"""    )

						UpperCamelCase								=       make_list_of_images(SCREAMING_SNAKE_CASE__    )

						if not valid_images(SCREAMING_SNAKE_CASE__    ):
									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.
						UpperCamelCase								=       [to_numpy_array(SCREAMING_SNAKE_CASE__    ) for image in images]

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

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

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

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

						UpperCamelCase								=       [to_channel_dimension_format(SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__    ) for image in images]

						UpperCamelCase								=       {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE__							,							tensor_type=SCREAMING_SNAKE_CASE__    )






from collections import OrderedDict
from typing import Mapping

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


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''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       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						="roberta"

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple=5_02_65					, SCREAMING_SNAKE_CASE__			:			List[str]=7_68					, SCREAMING_SNAKE_CASE__			:			List[str]=12					, SCREAMING_SNAKE_CASE__			:			Any=12					, SCREAMING_SNAKE_CASE__			:			Tuple=30_72					, SCREAMING_SNAKE_CASE__			:			str="gelu"					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.1					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			int=5_12					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=2					, SCREAMING_SNAKE_CASE__			:			Dict=0.02					, SCREAMING_SNAKE_CASE__			:			List[str]=1e-12					, SCREAMING_SNAKE_CASE__			:			Any=1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=0					, SCREAMING_SNAKE_CASE__			:			str=2					, SCREAMING_SNAKE_CASE__			:			Any="absolute"					, SCREAMING_SNAKE_CASE__			:			Any=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



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

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




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



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


