

'''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

_lowerCamelCase      					=     logging.get_logger(__name__)


def  a__       (    _SCREAMING_SNAKE_CASE   :       Tuple				)     ->					List[List[ImageInput]]:


	"""simple docstring"""





	if isinstance(_SCREAMING_SNAKE_CASE	,				(list, tuple)				) and isinstance(videos[0]	,				(list, tuple)				) and is_valid_image(videos[0][0]				):
		return videos

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

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

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





class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	Tuple        =["pixel_values"]






			def __init__(  self			,_snake_case = True			,_snake_case = None			,_snake_case = PILImageResampling.BILINEAR			,_snake_case = True			,_snake_case = None			,_snake_case = True			,_snake_case = 1 / 2_55			,_snake_case = True			,_snake_case = True			,_snake_case = None			,_snake_case = None			,**_snake_case			,):
				super().__init__(**_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  size if size is not None else {"shortest_edge": 2_56}
				UpperCAmelCase_     :      List[str]						=  get_size_dict(_snake_case			,default_to_square=_snake_case     )
				UpperCAmelCase_     :      str						=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
				UpperCAmelCase_     :      Optional[Any]						=  get_size_dict(_snake_case			,param_name="crop_size"     )

				UpperCAmelCase_     :      int						=  do_resize
				UpperCAmelCase_     :      List[str]						=  size
				UpperCAmelCase_     :      Dict						=  do_center_crop
				UpperCAmelCase_     :      Optional[Any]						=  crop_size
				UpperCAmelCase_     :      Optional[Any]						=  resample
				UpperCAmelCase_     :      int						=  do_rescale
				UpperCAmelCase_     :      Optional[int]						=  rescale_factor
				UpperCAmelCase_     :      Dict						=  offset
				UpperCAmelCase_     :      Optional[Any]						=  do_normalize
				UpperCAmelCase_     :      Union[str, Any]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				UpperCAmelCase_     :      Dict						=  image_std if image_std is not None else IMAGENET_STANDARD_STD






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = PILImageResampling.BILINEAR			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      Any						=  get_size_dict(_snake_case			,default_to_square=_snake_case     )
				if "shortest_edge" in size:
					UpperCAmelCase_     :      Optional[Any]						=  get_resize_output_image_size(_snake_case			,size["shortest_edge"]			,default_to_square=_snake_case     )
				elif "height" in size and "width" in size:
					UpperCAmelCase_     :      Optional[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(_snake_case			,size=_snake_case			,resample=_snake_case			,data_format=_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      Dict						=  get_size_dict(_snake_case     )
				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(_snake_case			,size=(size["height"], size["width"])			,data_format=_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = True			,_snake_case = None			,**_snake_case			,):
				UpperCAmelCase_     :      int						=  image.astype(np.floataa     )
				if offset:
					UpperCAmelCase_     :      Any						=  image - (scale / 2)
				return rescale(_snake_case			,scale=_snake_case			,data_format=_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case = None			,**_snake_case			,):
				return normalize(_snake_case			,mean=_snake_case			,std=_snake_case			,data_format=_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = ChannelDimension.FIRST			,):
				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.
				UpperCAmelCase_     :      Optional[int]						=  to_numpy_array(_snake_case     )

				if do_resize:
					UpperCAmelCase_     :      Dict						=  self.resize(image=_snake_case			,size=_snake_case			,resample=_snake_case     )

				if do_center_crop:
					UpperCAmelCase_     :      Optional[Any]						=  self.center_crop(_snake_case			,size=_snake_case     )

				if do_rescale:
					UpperCAmelCase_     :      Union[str, Any]						=  self.rescale(image=_snake_case			,scale=_snake_case			,offset=_snake_case     )

				if do_normalize:
					UpperCAmelCase_     :      Any						=  self.normalize(image=_snake_case			,mean=_snake_case			,std=_snake_case     )

				UpperCAmelCase_     :      Any						=  to_channel_dimension_format(_snake_case			,_snake_case     )
				return image






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = None			,_snake_case = ChannelDimension.FIRST			,**_snake_case			,):
				UpperCAmelCase_     :      Tuple						=  do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_     :      str						=  resample if resample is not None else self.resample
				UpperCAmelCase_     :      List[Any]						=  do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCAmelCase_     :      Tuple						=  do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase_     :      List[str]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase_     :      List[Any]						=  offset if offset is not None else self.offset
				UpperCAmelCase_     :      Optional[Any]						=  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_     :      int						=  image_std if image_std is not None else self.image_std

				UpperCAmelCase_     :      Dict						=  size if size is not None else self.size
				UpperCAmelCase_     :      int						=  get_size_dict(_snake_case			,default_to_square=_snake_case     )
				UpperCAmelCase_     :      List[Any]						=  crop_size if crop_size is not None else self.crop_size
				UpperCAmelCase_     :      int						=  get_size_dict(_snake_case			,param_name="crop_size"     )

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

				UpperCAmelCase_     :      Any						=  make_batched(_snake_case     )

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

				UpperCAmelCase_     :      List[str]						=  {"pixel_values": videos}
				return BatchFeature(data=_snake_case			,tensor_type=_snake_case     )






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


_A     :			str  						=			logging.get_logger(__name__)

_A     :			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 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

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

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


