






'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ['''pixel_values''']






      def __init__(			self      :		List[str]  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		int = 32  ,   UpperCAmelCase      :		Any=PILImageResampling.BILINEAR  ,   UpperCAmelCase      :		bool = True  ,   **UpperCAmelCase      :		Dict  ,   )	->	None:


       '''simple docstring'''

       lowercase       : Dict						=do_resize
       lowercase       : Optional[int]						=do_rescale
       lowercase       : Tuple						=size_divisor
       lowercase       : List[str]						=resample
       super().__init__(**UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		Optional[ChannelDimension] = None  ,   **UpperCAmelCase      :		Any		)	->	np.ndarray:


       '''simple docstring'''

       lowercase	,		lowercase       : Any						=get_image_size(UpperCAmelCase		)
       # Rounds the height and width down to the closest multiple of size_divisor
       lowercase       : Optional[Any]						=height // size_divisor * size_divisor
       lowercase       : Union[str, Any]						=width // size_divisor * size_divisor
       lowercase       : List[str]						=resize(UpperCAmelCase  ,   (new_h, new_w)  ,   resample=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)
       return image






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		np.ndarray  ,   UpperCAmelCase      :		float  ,   UpperCAmelCase      :		Optional[ChannelDimension] = None  ,   **UpperCAmelCase      :		List[str]		)	->	np.ndarray:


       '''simple docstring'''

       return rescale(image=UpperCAmelCase  ,   scale=UpperCAmelCase  ,   data_format=UpperCAmelCase  ,   **UpperCAmelCase		)






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]  ,   UpperCAmelCase      :		Optional[bool] = None  ,   UpperCAmelCase      :		Optional[int] = None  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		Optional[bool] = None  ,   UpperCAmelCase      :		Optional[Union[TensorType, str]] = None  ,   UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST  ,   **UpperCAmelCase      :		Dict  ,   )	->	BatchFeature:


       '''simple docstring'''

       lowercase       : List[Any]						=do_resize if do_resize is not None else self.do_resize
       lowercase       : Union[str, Any]						=do_rescale if do_rescale is not None else self.do_rescale
       lowercase       : Optional[int]						=size_divisor if size_divisor is not None else self.size_divisor
       lowercase       : Optional[Any]						=resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
        raise ValueError('''size_divisor is required for resizing'''		)

       lowercase       : List[str]						=make_list_of_images(UpperCAmelCase		)

       if not valid_images(UpperCAmelCase		):
        raise ValueError('''Invalid image(s)'''		)

       # All transformations expect numpy arrays.
       lowercase       : Union[str, Any]						=[to_numpy_array(UpperCAmelCase		) for img in images]

       if do_resize:
        lowercase       : int						=[self.resize(UpperCAmelCase  ,   size_divisor=UpperCAmelCase  ,   resample=UpperCAmelCase		) for image in images]

       if do_rescale:
        lowercase       : Any						=[self.rescale(UpperCAmelCase  ,   scale=1 / 255		) for image in images]

       lowercase       : Optional[Any]						=[to_channel_dimension_format(UpperCAmelCase  ,   UpperCAmelCase		) for image in images]

       lowercase       : List[str]						={'''pixel_values''': images}
       return BatchFeature(data=UpperCAmelCase  ,   tensor_type=UpperCAmelCase		)











'''simple docstring'''



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


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class UpperCAmelCase_       (     __A					,							__A					):




      """simple docstring"""




      UpperCamelCase_             =      '''bit'''
      UpperCamelCase_             =      ['''preactivation''', '''bottleneck''']
      UpperCamelCase_             =      ['''SAME''', '''VALID''']






      def __init__(			self      :		Any  ,   UpperCAmelCase      :		Optional[int]=3  ,   UpperCAmelCase      :		Any=64  ,   UpperCAmelCase      :		List[str]=[256, 512, 1024, 2048]  ,   UpperCAmelCase      :		Dict=[3, 4, 6, 3]  ,   UpperCAmelCase      :		List[Any]="preactivation"  ,   UpperCAmelCase      :		Tuple="relu"  ,   UpperCAmelCase      :		Tuple=None  ,   UpperCAmelCase      :		Tuple=32  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Dict=32  ,   UpperCAmelCase      :		Any=1  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   **UpperCAmelCase      :		str  ,   )	->	Dict:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       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:
         lowercase       : Any						=global_padding.upper()
        else:
         raise ValueError(f'Padding strategy {global_padding} not supported'		)
       lowercase       : List[Any]						=num_channels
       lowercase       : List[str]						=embedding_size
       lowercase       : Union[str, Any]						=hidden_sizes
       lowercase       : Union[str, Any]						=depths
       lowercase       : Optional[int]						=layer_type
       lowercase       : Optional[Any]						=hidden_act
       lowercase       : List[str]						=global_padding
       lowercase       : Dict						=num_groups
       lowercase       : Optional[Any]						=drop_path_rate
       lowercase       : Union[str, Any]						=embedding_dynamic_padding
       lowercase       : Any						=output_stride
       lowercase       : Union[str, Any]						=width_factor

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



