




"""simple docstring"""

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


a					=			logging.get_logger(__name__)






def 						_snake_case     (					_snake_case							:					str							)    ->					List[List[ImageInput]]:






      '''simple docstring'''


      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 				lowercase_       (     __lowercase							):



 '''simple docstring'''
 UpperCAmelCase    :     int        =	["pixel_values"]

 def __init__( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       Optional[int] = True    ,							_UpperCAmelCase       :       Optional[int] = None    ,							_UpperCAmelCase       :       Optional[Any] = PILImageResampling.BILINEAR    ,							_UpperCAmelCase       :       Optional[Any] = True    ,							_UpperCAmelCase       :       Tuple = None    ,							_UpperCAmelCase       :       str = True    ,							_UpperCAmelCase       :       Any = 1 / 255    ,							_UpperCAmelCase       :       List[Any] = True    ,							_UpperCAmelCase       :       int = None    ,							_UpperCAmelCase       :       Union[str, Any] = None    ,							**_UpperCAmelCase       :       List[Any]    ,							):
       super().__init__(**_a		)
       _A					   =   size if size is not None else {'''shortest_edge''': 224}
       _A					   =   get_size_dict(_a    ,							default_to_square=_a		)
       _A					   =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       _A					   =   get_size_dict(_a    ,							param_name='crop_size'		)

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

 def 			lowerCAmelCase_							( self       :       int    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       List[str] = PILImageResampling.BILINEAR    ,							_UpperCAmelCase       :       List[Any] = None    ,							**_UpperCAmelCase       :       Union[str, Any]    ,							):
       _A					   =   get_size_dict(_a    ,							default_to_square=_a		)
       if "shortest_edge" in size:
             _A					   =   get_resize_output_image_size(_a    ,							size['shortest_edge']    ,							default_to_square=_a		)
       elif "height" in size and "width" in size:
             _A					   =   (size['''height'''], size['''width'''])
       else:
             raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''		)
       return resize(_a    ,							size=_a    ,							resample=_a    ,							data_format=_a    ,							**_a		)

 def 			lowerCAmelCase_							( self       :       int    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Union[str, Any] = None    ,							**_UpperCAmelCase       :       Tuple    ,							):
       _A					   =   get_size_dict(_a		)
       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(_a    ,							size=(size['height'], size['width'])    ,							data_format=_a    ,							**_a		)

 def 			lowerCAmelCase_							( self       :       Any    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       str    ,							_UpperCAmelCase       :       int = None    ,							**_UpperCAmelCase       :       Optional[Any]    ,							):
       return rescale(_a    ,							scale=_a    ,							data_format=_a    ,							**_a		)

 def 			lowerCAmelCase_							( self       :       Optional[int]    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       List[Any] = None    ,							**_UpperCAmelCase       :       str    ,							):
       return normalize(_a    ,							mean=_a    ,							std=_a    ,							data_format=_a    ,							**_a		)

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       List[str] = None    ,							_UpperCAmelCase       :       Union[str, Any] = None    ,							_UpperCAmelCase       :       str = None    ,							_UpperCAmelCase       :       Tuple = None    ,							_UpperCAmelCase       :       Optional[Any] = None    ,							_UpperCAmelCase       :       Optional[Any] = None    ,							_UpperCAmelCase       :       str = None    ,							_UpperCAmelCase       :       Optional[int] = None    ,							_UpperCAmelCase       :       int = None    ,							_UpperCAmelCase       :       List[Any] = None    ,							_UpperCAmelCase       :       Tuple = 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.'		)

       # All transformations expect numpy arrays.
       _A					   =   to_numpy_array(_a		)

       if do_resize:
             _A					   =   self.resize(image=_a    ,							size=_a    ,							resample=_a		)

       if do_center_crop:
             _A					   =   self.center_crop(_a    ,							size=_a		)

       if do_rescale:
             _A					   =   self.rescale(image=_a    ,							scale=_a		)

       if do_normalize:
             _A					   =   self.normalize(image=_a    ,							mean=_a    ,							std=_a		)

       _A					   =   to_channel_dimension_format(_a    ,							_a		)
       return image



 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Dict = None    ,							_UpperCAmelCase       :       Optional[Any] = None    ,							_UpperCAmelCase       :       Dict = None    ,							_UpperCAmelCase       :       Union[str, Any] = None    ,							_UpperCAmelCase       :       List[Any] = None    ,							_UpperCAmelCase       :       Dict = None    ,							_UpperCAmelCase       :       Optional[Any] = None    ,							_UpperCAmelCase       :       Optional[int] = None    ,							_UpperCAmelCase       :       List[str] = None    ,							_UpperCAmelCase       :       Optional[int] = None    ,							_UpperCAmelCase       :       Tuple = None    ,							_UpperCAmelCase       :       Tuple = ChannelDimension.FIRST    ,							**_UpperCAmelCase       :       int    ,							):
       _A					   =   do_resize if do_resize is not None else self.do_resize
       _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(_a    ,							default_to_square=_a		)
       _A					   =   crop_size if crop_size is not None else self.crop_size
       _A					   =   get_size_dict(_a    ,							param_name='crop_size'		)

       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.'		)

       _A					   =   make_batched(_a		)

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

       _A					   =   {'''pixel_values''': videos}
       return BatchFeature(data=_a    ,							tensor_type=_a		)




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__				=		logging.get_logger(__name__)

a__				=		{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


							def __init__(			self       ,							_a=3       ,							_a=6_4       ,							_a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,							_a=[3, 4, 6, 3]       ,							_a="preactivation"       ,							_a="relu"       ,							_a=None       ,							_a=3_2       ,							_a=0.0       ,							_a=False       ,							_a=3_2       ,							_a=1       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    Union[str, Any]:
												super().__init__(**_a       )
												if layer_type not in self.layer_types:
																	raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types       )}"""       )
												if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																						_a	:    Any         =		global_padding.upper()
																	else:
																						raise ValueError(F"""Padding strategy {global_padding} not supported"""       )
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		embedding_size
												_a	:    Any         =		hidden_sizes
												_a	:    int         =		depths
												_a	:    Dict         =		layer_type
												_a	:    int         =		hidden_act
												_a	:    Optional[Any]         =		global_padding
												_a	:    Optional[Any]         =		num_groups
												_a	:    Union[str, Any]         =		drop_path_rate
												_a	:    Tuple         =		embedding_dynamic_padding
												_a	:    Union[str, Any]         =		output_stride
												_a	:    Any         =		width_factor

												_a	:    Any         =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       ,							len(_a       ) + 1       )]
												_a      ,					_a	:    List[str]         =		get_aligned_output_features_output_indices(
												    out_features=_a       ,							out_indices=_a       ,							stage_names=self.stage_names       )



