from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__		:       int       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Tuple       						=       {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'big_bird'




  def __init__(   self  :						Any  ,			a_  :						List[str]=5_0358  ,			a_  :						Union[str, Any]=768  ,			a_  :						Optional[Any]=12  ,			a_  :						Any=12  ,			a_  :						List[str]=3072  ,			a_  :						str="gelu_new"  ,			a_  :						str=0.1  ,			a_  :						str=0.1  ,			a_  :						Optional[int]=4096  ,			a_  :						Tuple=2  ,			a_  :						str=0.02  ,			a_  :						str=1e-1_2  ,			a_  :						Any=True  ,			a_  :						List[Any]=0  ,			a_  :						Union[str, Any]=1  ,			a_  :						List[Any]=2  ,			a_  :						List[str]=66  ,			a_  :						Optional[int]="block_sparse"  ,			a_  :						int=True  ,			a_  :						Dict=False  ,			a_  :						Any=64  ,			a_  :						Tuple=3  ,			a_  :						Tuple=None  ,			**a_  :						List[str]  ,			)->							List[str]:




       """simple docstring"""
       super().__init__(
           pad_token_id=a_  ,			bos_token_id=a_  ,			eos_token_id=a_  ,			sep_token_id=a_  ,			**a_  ,			)

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

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		rescale_embeddings
       SCREAMING_SNAKE_CASE__					:					Any      =		attention_type
       SCREAMING_SNAKE_CASE__					:					Dict      =		use_bias
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		block_size
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_random_blocks
       SCREAMING_SNAKE_CASE__					:					Any      =		classifier_dropout




class    snake_case       (   UpperCamelCase_    ):




  @property
  def 						__lowercase(   self  :						List[Any]					)->							Mapping[str, Mapping[int, str]]:




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







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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case__				:	Tuple					       =					logging.get_logger(__name__)


if is_vision_available():
      import PIL





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["pixel_values"]



       def __init__(						self				:				List[str]		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				bool = True		,				__a				:				Dict[str, int] = None		,				__a				:				bool = True		,				__a				:				Union[int, float] = 1 / 255		,				__a				:				bool = True		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = True		,				**__a				:				str		,				)					->None:
          super().__init__(**__a							)
          lowerCamelCase_						:   Any		= size if size is not None else {"""shortest_edge""": 224}
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          lowerCamelCase_						:   Any		= get_size_dict(__a		,				default_to_square=__a		,				param_name="""crop_size"""							)

          lowerCamelCase_						:   List[Any]		= do_resize
          lowerCamelCase_						:   str		= size
          lowerCamelCase_						:   Any		= resample
          lowerCamelCase_						:   List[Any]		= do_center_crop
          lowerCamelCase_						:   Any		= crop_size
          lowerCamelCase_						:   List[Any]		= do_rescale
          lowerCamelCase_						:   Tuple		= rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize
          lowerCamelCase_						:   Any		= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          lowerCamelCase_						:   int		= image_std if image_std is not None else OPENAI_CLIP_STD
          lowerCamelCase_						:   Tuple		= do_convert_rgb



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				PILImageResampling = PILImageResampling.BICUBIC		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->np.ndarray:
          lowerCamelCase_						:   Optional[Any]		= get_size_dict(__a		,				default_to_square=__a							)
          if "shortest_edge" not in size:
             raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
          lowerCamelCase_						:   Dict		= get_resize_output_image_size(__a		,				size=size["""shortest_edge"""]		,				default_to_square=__a							)
          return resize(__a		,				size=__a		,				resample=__a		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				np.ndarray		,				__a				:				Dict[str, int]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				List[Any]		,				)					->np.ndarray:
          lowerCamelCase_						:   List[Any]		= get_size_dict(__a							)
          if "height" not in size or "width" not in size:
             raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
          return center_crop(__a		,				size=(size["""height"""], size["""width"""])		,				data_format=__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				np.ndarray		,				__a				:				Union[int, float]		,				__a				:				Optional[Union[str, ChannelDimension]] = None		,				**__a				:				Dict		,				)					->Dict:
          return rescale(__a		,				scale=__a		,				data_format=__a		,				**__a							)



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




       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				ImageInput		,				__a				:				bool = None		,				__a				:				Dict[str, int] = None		,				__a				:				PILImageResampling = None		,				__a				:				bool = None		,				__a				:				int = None		,				__a				:				bool = None		,				__a				:				float = None		,				__a				:				bool = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				Optional[Union[float, List[float]]] = None		,				__a				:				bool = None		,				__a				:				Optional[Union[str, TensorType]] = None		,				__a				:				Optional[ChannelDimension] = ChannelDimension.FIRST		,				**__a				:				List[str]		,				)					->PIL.Image.Image:
          lowerCamelCase_						:   List[Any]		= do_resize if do_resize is not None else self.do_resize
          lowerCamelCase_						:   Any		= size if size is not None else self.size
          lowerCamelCase_						:   str		= get_size_dict(__a		,				param_name="""size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[str]		= resample if resample is not None else self.resample
          lowerCamelCase_						:   List[str]		= do_center_crop if do_center_crop is not None else self.do_center_crop
          lowerCamelCase_						:   List[Any]		= crop_size if crop_size is not None else self.crop_size
          lowerCamelCase_						:   Optional[int]		= get_size_dict(__a		,				param_name="""crop_size"""		,				default_to_square=__a							)
          lowerCamelCase_						:   List[Any]		= do_rescale if do_rescale is not None else self.do_rescale
          lowerCamelCase_						:   List[str]		= rescale_factor if rescale_factor is not None else self.rescale_factor
          lowerCamelCase_						:   Dict		= do_normalize if do_normalize is not None else self.do_normalize
          lowerCamelCase_						:   Dict		= image_mean if image_mean is not None else self.image_mean
          lowerCamelCase_						:   Tuple		= image_std if image_std is not None else self.image_std
          lowerCamelCase_						:   Union[str, Any]		= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          lowerCamelCase_						:   Union[str, Any]		= make_list_of_images(__a							)

          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."""							)

          if do_resize and size is None:
             raise ValueError("""Size 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."""							)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
             lowerCamelCase_						:   str		= [convert_to_rgb(__a							) for image in images]

          # All transformations expect numpy arrays.
          lowerCamelCase_						:   str		= [to_numpy_array(__a							) for image in images]

          if do_resize:
             lowerCamelCase_						:   int		= [self.resize(image=__a		,				size=__a		,				resample=__a							) for image in images]

          if do_center_crop:
             lowerCamelCase_						:   Dict		= [self.center_crop(image=__a		,				size=__a							) for image in images]

          if do_rescale:
             lowerCamelCase_						:   List[str]		= [self.rescale(image=__a		,				scale=__a							) for image in images]

          if do_normalize:
             lowerCamelCase_						:   Tuple		= [self.normalize(image=__a		,				mean=__a		,				std=__a							) for image in images]

          lowerCamelCase_						:   Tuple		= [to_channel_dimension_format(__a		,				__a							) for image in images]

          lowerCamelCase_						:   Tuple		= {"""pixel_values""": images}
          return BatchFeature(data=__a		,				tensor_type=__a							)





