






'''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,
    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


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)


if is_vision_available():
		import PIL







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	['''pixel_values''']

							def __init__(		self			:str       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:Dict[str, int] = None       ,							lowerCAmelCase__			:PILImageResampling = PILImageResampling.BICUBIC       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:Dict[str, int] = None       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:Union[int, float] = 1 / 255       ,							lowerCAmelCase__			:bool = True       ,							lowerCAmelCase__			:Optional[Union[float, List[float]]] = None       ,							lowerCAmelCase__			:Optional[Union[float, List[float]]] = None       ,							lowerCAmelCase__			:bool = True       ,							**lowerCAmelCase__			:List[Any]       ,							)				->  None:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					List[str]   =       size if size is not None else {"shortest_edge": 224}
										snake_case_				:					List[Any]   =       get_size_dict(lowerCAmelCase__       ,							default_to_square=lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       crop_size if crop_size is not None else {"height": 224, "width": 224}
										snake_case_				:					Union[str, Any]   =       get_size_dict(lowerCAmelCase__       ,							default_to_square=lowerCAmelCase__       ,							param_name="crop_size"				)

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

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:np.ndarray       ,							lowerCAmelCase__			:Dict[str, int]       ,							lowerCAmelCase__			:PILImageResampling = PILImageResampling.BICUBIC       ,							lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None       ,							**lowerCAmelCase__			:int       ,							)				->  np.ndarray:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_size_dict(lowerCAmelCase__       ,							default_to_square=lowerCAmelCase__				)
										if "shortest_edge" not in size:
													raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''				)
										snake_case_				:					Optional[int]   =       get_resize_output_image_size(lowerCAmelCase__       ,							size=size["shortest_edge"]       ,							default_to_square=lowerCAmelCase__				)
										return resize(lowerCAmelCase__       ,							size=lowerCAmelCase__       ,							resample=lowerCAmelCase__       ,							data_format=lowerCAmelCase__       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:str       ,							lowerCAmelCase__			:np.ndarray       ,							lowerCAmelCase__			:Dict[str, int]       ,							lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None       ,							**lowerCAmelCase__			:Any       ,							)				->  np.ndarray:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       get_size_dict(lowerCAmelCase__				)
										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(lowerCAmelCase__       ,							size=(size["height"], size["width"])       ,							data_format=lowerCAmelCase__       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:np.ndarray       ,							lowerCAmelCase__			:Union[int, float]       ,							lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None       ,							**lowerCAmelCase__			:str       ,							)				->  List[str]:


										'''simple docstring'''




										return rescale(lowerCAmelCase__       ,							scale=lowerCAmelCase__       ,							data_format=lowerCAmelCase__       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:np.ndarray       ,							lowerCAmelCase__			:Union[float, List[float]]       ,							lowerCAmelCase__			:Union[float, List[float]]       ,							lowerCAmelCase__			:Optional[Union[str, ChannelDimension]] = None       ,							**lowerCAmelCase__			:int       ,							)				->  np.ndarray:


										'''simple docstring'''




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






							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:ImageInput       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:Dict[str, int] = None       ,							lowerCAmelCase__			:PILImageResampling = None       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:int = None       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:float = None       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:Optional[Union[float, List[float]]] = None       ,							lowerCAmelCase__			:Optional[Union[float, List[float]]] = None       ,							lowerCAmelCase__			:bool = None       ,							lowerCAmelCase__			:Optional[Union[str, TensorType]] = None       ,							lowerCAmelCase__			:Optional[ChannelDimension] = ChannelDimension.FIRST       ,							**lowerCAmelCase__			:str       ,							)				->  PIL.Image.Image:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       do_resize if do_resize is not None else self.do_resize
										snake_case_				:					List[Any]   =       size if size is not None else self.size
										snake_case_				:					Optional[int]   =       get_size_dict(lowerCAmelCase__       ,							param_name="size"       ,							default_to_square=lowerCAmelCase__				)
										snake_case_				:					List[str]   =       resample if resample is not None else self.resample
										snake_case_				:					Union[str, Any]   =       do_center_crop if do_center_crop is not None else self.do_center_crop
										snake_case_				:					str   =       crop_size if crop_size is not None else self.crop_size
										snake_case_				:					Union[str, Any]   =       get_size_dict(lowerCAmelCase__       ,							param_name="crop_size"       ,							default_to_square=lowerCAmelCase__				)
										snake_case_				:					str   =       do_rescale if do_rescale is not None else self.do_rescale
										snake_case_				:					List[Any]   =       rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case_				:					Optional[Any]   =       do_normalize if do_normalize is not None else self.do_normalize
										snake_case_				:					Optional[Any]   =       image_mean if image_mean is not None else self.image_mean
										snake_case_				:					Any   =       image_std if image_std is not None else self.image_std
										snake_case_				:					Tuple   =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										snake_case_				:					Union[str, Any]   =       make_list_of_images(lowerCAmelCase__				)

										if not valid_images(lowerCAmelCase__				):
													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:
													snake_case_				:					Tuple   =       [convert_to_rgb(lowerCAmelCase__				) for image in images]

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

										if do_resize:
													snake_case_				:					Optional[int]   =       [self.resize(image=lowerCAmelCase__       ,							size=lowerCAmelCase__       ,							resample=lowerCAmelCase__				) for image in images]

										if do_center_crop:
													snake_case_				:					Union[str, Any]   =       [self.center_crop(image=lowerCAmelCase__       ,							size=lowerCAmelCase__				) for image in images]

										if do_rescale:
													snake_case_				:					Tuple   =       [self.rescale(image=lowerCAmelCase__       ,							scale=lowerCAmelCase__				) for image in images]

										if do_normalize:
													snake_case_				:					List[Any]   =       [self.normalize(image=lowerCAmelCase__       ,							mean=lowerCAmelCase__       ,							std=lowerCAmelCase__				) for image in images]

										snake_case_				:					Optional[int]   =       [to_channel_dimension_format(lowerCAmelCase__       ,							lowerCAmelCase__				) for image in images]

										snake_case_				:					Optional[int]   =       {"pixel_values": images}
										return BatchFeature(data=lowerCAmelCase__       ,							tensor_type=lowerCAmelCase__				)












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					int   							=					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''cvt'''

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[int]=3       ,							lowerCAmelCase__			:Any=[7, 3, 3]       ,							lowerCAmelCase__			:Dict=[4, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[2, 1, 1]       ,							lowerCAmelCase__			:Any=[64, 192, 384]       ,							lowerCAmelCase__			:List[str]=[1, 3, 6]       ,							lowerCAmelCase__			:str=[1, 2, 10]       ,							lowerCAmelCase__			:Any=[4.0, 4.0, 4.0]       ,							lowerCAmelCase__			:int=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Optional[Any]=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Dict=[0.0, 0.0, 0.1]       ,							lowerCAmelCase__			:List[Any]=[True, True, True]       ,							lowerCAmelCase__			:List[Any]=[False, False, True]       ,							lowerCAmelCase__			:Dict=["dw_bn", "dw_bn", "dw_bn"]       ,							lowerCAmelCase__			:Any=[3, 3, 3]       ,							lowerCAmelCase__			:Tuple=[1, 1, 1]       ,							lowerCAmelCase__			:Optional[int]=[2, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[1, 1, 1]       ,							lowerCAmelCase__			:Any=[1, 1, 1]       ,							lowerCAmelCase__			:List[str]=0.0_2       ,							lowerCAmelCase__			:Dict=1E-1_2       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  str:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					int   =       num_channels
										snake_case_				:					int   =       patch_sizes
										snake_case_				:					Optional[Any]   =       patch_stride
										snake_case_				:					Dict   =       patch_padding
										snake_case_				:					Tuple   =       embed_dim
										snake_case_				:					Optional[int]   =       num_heads
										snake_case_				:					Union[str, Any]   =       depth
										snake_case_				:					Optional[int]   =       mlp_ratio
										snake_case_				:					Tuple   =       attention_drop_rate
										snake_case_				:					str   =       drop_rate
										snake_case_				:					Tuple   =       drop_path_rate
										snake_case_				:					Any   =       qkv_bias
										snake_case_				:					Union[str, Any]   =       cls_token
										snake_case_				:					int   =       qkv_projection_method
										snake_case_				:					Any   =       kernel_qkv
										snake_case_				:					Union[str, Any]   =       padding_kv
										snake_case_				:					str   =       stride_kv
										snake_case_				:					Dict   =       padding_q
										snake_case_				:					Tuple   =       stride_q
										snake_case_				:					Any   =       initializer_range
										snake_case_				:					Any   =       layer_norm_eps




