

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    '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  (	UpperCAmelCase				):
						_lowercase    						=			"cvt"


						def __init__(       self						,					A_=3						,					A_=[7, 3, 3]						,					A_=[4, 2, 2]						,					A_=[2, 1, 1]						,					A_=[64, 192, 384]						,					A_=[1, 3, 6]						,					A_=[1, 2, 10]						,					A_=[4.0, 4.0, 4.0]						,					A_=[0.0, 0.0, 0.0]						,					A_=[0.0, 0.0, 0.0]						,					A_=[0.0, 0.0, 0.1]						,					A_=[True, True, True]						,					A_=[False, False, True]						,					A_=["dw_bn", "dw_bn", "dw_bn"]						,					A_=[3, 3, 3]						,					A_=[1, 1, 1]						,					A_=[2, 2, 2]						,					A_=[1, 1, 1]						,					A_=[1, 1, 1]						,					A_=0.02						,					A_=1e-12						,					**A_						,					):







										'''simple docstring'''


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





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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Union[str, Any] )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Dict 		=    b.T
				_UpperCAmelCase       :		Dict 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=1 )
				_UpperCAmelCase       :		Optional[Any] 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=0 )
				_UpperCAmelCase       :		str 		=    np.matmul(lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    aa[:, None] - 2 * ab + ba[None, :]
				return d

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Dict )   ->       int:
				_UpperCAmelCase       :		Any 		=    x.reshape(-1  ,      3 )
				_UpperCAmelCase       :		List[str] 		=    squared_euclidean_distance(lowerCAmelCase  ,      lowerCAmelCase )
				return np.argmin(lowerCAmelCase  ,      axis=1 )





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["pixel_values"]


						def __init__(       self						,					A_ = None						,					A_ = True						,					A_ = None						,					A_ = PILImageResampling.BILINEAR						,					A_ = True						,					A_ = True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    size if size is not None else {"height": 256, "width": 256}
										_UpperCAmelCase       :		Optional[int] 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    np.array(A_	) if clusters is not None else None
										_UpperCAmelCase       :		int 		=    do_resize
										_UpperCAmelCase       :		Union[str, Any] 		=    size
										_UpperCAmelCase       :		Optional[Any] 		=    resample
										_UpperCAmelCase       :		str 		=    do_normalize
										_UpperCAmelCase       :		List[str] 		=    do_color_quantize


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BILINEAR						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    get_size_dict(A_	)
										if "height" not in size or "width" not in size:
														raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'	)
										return resize(
										    A_						,					size=(size["height"], size["width"])						,					resample=A_						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    rescale(image=A_						,					scale=1 / 1_27.5						,					data_format=A_	)
										_UpperCAmelCase       :		List[Any] 		=    image - 1
										return image





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = ChannelDimension.FIRST						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase       :		Any 		=    size if size is not None else self.size
										_UpperCAmelCase       :		Dict 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		List[Any] 		=    resample if resample is not None else self.resample
										_UpperCAmelCase       :		Any 		=    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase       :		Optional[Any] 		=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										_UpperCAmelCase       :		Any 		=    clusters if clusters is not None else self.clusters
										_UpperCAmelCase       :		Optional[int] 		=    np.array(A_	)

										_UpperCAmelCase       :		List[str] 		=    make_list_of_images(A_	)

										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."	)

										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_color_quantize and clusters is None:
														raise ValueError("Clusters must be specified if do_color_quantize is True."	)

										# All transformations expect numpy arrays.
										_UpperCAmelCase       :		List[str] 		=    [to_numpy_array(A_	) for image in images]

										if do_resize:
														_UpperCAmelCase       :		int 		=    [self.resize(image=A_						,					size=A_						,					resample=A_	) for image in images]

										if do_normalize:
														_UpperCAmelCase       :		List[str] 		=    [self.normalize(image=A_	) for image in images]

										if do_color_quantize:
														_UpperCAmelCase       :		Tuple 		=    [to_channel_dimension_format(A_						,					ChannelDimension.LAST	) for image in images]
														# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
														_UpperCAmelCase       :		List[str] 		=    np.array(A_	)
														_UpperCAmelCase       :		List[Any] 		=    color_quantize(A_						,					A_	).reshape(images.shape[:-1]	)

														# flatten to (batch_size, height*width)
														_UpperCAmelCase       :		Any 		=    images.shape[0]
														_UpperCAmelCase       :		List[Any] 		=    images.reshape(A_						,					-1	)

														# We need to convert back to a list of images to keep consistent behaviour across processors.
														_UpperCAmelCase       :		Union[str, Any] 		=    list(A_	)
										else:
														_UpperCAmelCase       :		Optional[Any] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

										_UpperCAmelCase       :		List[Any] 		=    {"input_ids": images}
										return BatchFeature(data=A_						,					tensor_type=A_	)


