



'''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

lowercase_   :					int        =     logging.get_logger(__name__)







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   List[str]    ):
						if isinstance(__a			,	(list, tuple)    ) and isinstance(videos[0]			,	(list, tuple)    ) and is_valid_image(videos[0][0]    ):
												return videos

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

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

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




class   __UpperCamelCase							(lowercase__      ):
	__A      	=       ['''pixel_values''']







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






							super().__init__(**lowercase_    )
							lowercase   =				size if size is not None else {"""shortest_edge""": 256}
							lowercase   =				get_size_dict(lowercase_			,					default_to_square=lowercase_    )
							lowercase   =				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowercase   =				get_size_dict(lowercase_			,					param_name="""crop_size"""    )

							lowercase   =				do_resize
							lowercase   =				size
							lowercase   =				do_center_crop
							lowercase   =				crop_size
							lowercase   =				resample
							lowercase   =				do_rescale
							lowercase   =				rescale_factor
							lowercase   =				offset
							lowercase   =				do_normalize
							lowercase   =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase   =				image_std if image_std is not None else IMAGENET_STANDARD_STD







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase = PILImageResampling.BILINEAR			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     np.ndarray:
							'''simple docstring'''






							lowercase   =				get_size_dict(lowercase_			,					default_to_square=lowercase_    )
							if "shortest_edge" in size:
													lowercase   =				get_resize_output_image_size(lowercase_			,					size["""shortest_edge"""]			,					default_to_square=lowercase_    )
							elif "height" in size and "width" in size:
													lowercase   =				(size["""height"""], size["""width"""])
							else:
													raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""    )
							return resize(lowercase_			,					size=lowercase_			,					resample=lowercase_			,					data_format=lowercase_			,					**lowercase_    )







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     np.ndarray:
							'''simple docstring'''






							lowercase   =				get_size_dict(lowercase_    )
							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(lowercase_			,					size=(size["""height"""], size["""width"""])			,					data_format=lowercase_			,					**lowercase_    )







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase = True			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     List[str]:
							'''simple docstring'''






							lowercase   =				image.astype(np.floataa    )
							if offset:
													lowercase   =				image - (scale / 2)
							return rescale(lowercase_			,					scale=lowercase_			,					data_format=lowercase_			,					**lowercase_    )







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     np.ndarray:
							'''simple docstring'''






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







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = ChannelDimension.FIRST			,					)     ->     np.ndarray:
							'''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."""    )

							if offset and not do_rescale:
													raise ValueError("""For offset, do_rescale must also be set to True."""    )

							# All transformations expect numpy arrays.
							lowercase   =				to_numpy_array(lowercase_    )

							if do_resize:
													lowercase   =				self.resize(image=lowercase_			,					size=lowercase_			,					resample=lowercase_    )

							if do_center_crop:
													lowercase   =				self.center_crop(lowercase_			,					size=lowercase_    )

							if do_rescale:
													lowercase   =				self.rescale(image=lowercase_			,					scale=lowercase_			,					offset=lowercase_    )

							if do_normalize:
													lowercase   =				self.normalize(image=lowercase_			,					mean=lowercase_			,					std=lowercase_    )

							lowercase   =				to_channel_dimension_format(lowercase_			,					lowercase_    )
							return image







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = ChannelDimension.FIRST			,					**_lowerCAmelCase			,					)     ->     PIL.Image.Image:
							'''simple docstring'''






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

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

							if not valid_images(lowercase_    ):
													raise ValueError(
													    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
													    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

							lowercase   =				make_batched(lowercase_    )

							lowercase   =				[
							    [
							        self._preprocess_image(
							            image=lowercase_			,					do_resize=lowercase_			,					size=lowercase_			,					resample=lowercase_			,					do_center_crop=lowercase_			,					crop_size=lowercase_			,					do_rescale=lowercase_			,					rescale_factor=lowercase_			,					offset=lowercase_			,					do_normalize=lowercase_			,					image_mean=lowercase_			,					image_std=lowercase_			,					data_format=lowercase_			,					)
							        for img in video
							    ]
							    for video in videos
							]

							lowercase   =				{"""pixel_values""": videos}
							return BatchFeature(data=lowercase_			,					tensor_type=lowercase_    )






from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase:						str   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    '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       lowercase_					(lowercase__   ):
				snake_case	    ='big_bird'





				def __init__(				self       ,  lowercase_=50358       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu_new"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=4096       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-12       ,  lowercase_=True       ,  lowercase_=0       ,  lowercase_=1       ,  lowercase_=2       ,  lowercase_=66       ,  lowercase_="block_sparse"       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_=64       ,  lowercase_=3       ,  lowercase_=None       ,  **lowercase_       ,  )  -> Any:
											super().__init__(
											    pad_token_id=lowercase_       ,  bos_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  sep_token_id=lowercase_       ,  **lowercase_       ,  )

											a__      =vocab_size
											a__      =max_position_embeddings
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =initializer_range
											a__      =type_vocab_size
											a__      =layer_norm_eps
											a__      =use_cache

											a__      =rescale_embeddings
											a__      =attention_type
											a__      =use_bias
											a__      =block_size
											a__      =num_random_blocks
											a__      =classifier_dropout







class       lowercase_					(lowercase__   ):





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

