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


_snake_case								=  logging.get_logger(__name__)

_snake_case								=  {
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class     _snake_case  (  __lowerCamelCase    ,					__lowerCamelCase ):
					lowerCamelCase__:						Optional[int]     =   """nat"""

					lowerCamelCase__:						List[Any]     =   {
					    """num_attention_heads""": """num_heads""",
					    """num_hidden_layers""": """num_layers""",
					}
					def __init__(     self: Any		,     __lowerCamelCase: Any=4		,     __lowerCamelCase: Tuple=3		,     __lowerCamelCase: Tuple=64		,     __lowerCamelCase: str=[3, 4, 6, 5]		,     __lowerCamelCase: Dict=[2, 4, 8, 16]		,     __lowerCamelCase: List[str]=7		,     __lowerCamelCase: Dict=3.0		,     __lowerCamelCase: Union[str, Any]=True		,     __lowerCamelCase: List[Any]=0.0		,     __lowerCamelCase: Optional[Any]=0.0		,     __lowerCamelCase: Tuple=0.1		,     __lowerCamelCase: Tuple="gelu"		,     __lowerCamelCase: Optional[int]=0.02		,     __lowerCamelCase: Optional[Any]=1e-5		,     __lowerCamelCase: Union[str, Any]=0.0		,     __lowerCamelCase: List[str]=None		,     __lowerCamelCase: Dict=None		,     **__lowerCamelCase: int		,     )			-> Any:
												super().__init__(**__lowerCamelCase   )

												__UpperCAmelCase  :	List[Any]   			=      patch_size
												__UpperCAmelCase  :	int   			=      num_channels
												__UpperCAmelCase  :	str   			=      embed_dim
												__UpperCAmelCase  :	int   			=      depths
												__UpperCAmelCase  :	Optional[Any]   			=      len(__lowerCamelCase   )
												__UpperCAmelCase  :	int   			=      num_heads
												__UpperCAmelCase  :	Optional[Any]   			=      kernel_size
												__UpperCAmelCase  :	Optional[Any]   			=      mlp_ratio
												__UpperCAmelCase  :	Tuple   			=      qkv_bias
												__UpperCAmelCase  :	Optional[int]   			=      hidden_dropout_prob
												__UpperCAmelCase  :	Optional[Any]   			=      attention_probs_dropout_prob
												__UpperCAmelCase  :	int   			=      drop_path_rate
												__UpperCAmelCase  :	Dict   			=      hidden_act
												__UpperCAmelCase  :	str   			=      layer_norm_eps
												__UpperCAmelCase  :	Any   			=      initializer_range
												# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												__UpperCAmelCase  :	List[str]   			=      int(embed_dim * 2 ** (len(__lowerCamelCase   ) - 1)   )
												__UpperCAmelCase  :	List[Any]   			=      layer_scale_init_value
												__UpperCAmelCase  :	Any   			=      ["stem"] + [f'''stage{idx}''' for idx in range(1		,     len(__lowerCamelCase   ) + 1   )]
												__UpperCAmelCase			,		__UpperCAmelCase  :	Any   			=      get_aligned_output_features_output_indices(
												    out_features=__lowerCamelCase		,     out_indices=__lowerCamelCase		,     stage_names=self.stage_names   )






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


UpperCAmelCase	      = logging.get_logger(__name__)


if is_vision_available():
	import PIL







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     str							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = True		,    snake_case = None		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'shortest_edge': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		lowercase       =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case		,    param_name='crop_size'       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_center_crop
		lowercase       =    crop_size
		lowercase       =    do_rescale
		lowercase       =    rescale_factor
		lowercase       =    do_normalize
		lowercase       =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
		lowercase       =    image_std if image_std is not None else OPENAI_CLIP_STD
		lowercase       =    do_convert_rgb





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		if "shortest_edge" not in size:
			raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
		lowercase       =    get_resize_output_image_size(snake_case		,    size=size['shortest_edge']		,    default_to_square=snake_case       )
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		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(snake_case		,    size=(size['height'], size['width'])		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       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 	SCREAMING_SNAKE_CASE__						(       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		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case		,    param_name='size'		,    default_to_square=snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_center_crop if do_center_crop is not None else self.do_center_crop
		lowercase       =    crop_size if crop_size is not None else self.crop_size
		lowercase       =    get_size_dict(snake_case		,    param_name='crop_size'		,    default_to_square=snake_case       )
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std
		lowercase       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

		lowercase       =    make_list_of_images(snake_case       )

		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.'       )

		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:
			lowercase       =    [convert_to_rgb(snake_case       ) for image in images]

		# All transformations expect numpy arrays.
		lowercase       =    [to_numpy_array(snake_case       ) for image in images]

		if do_resize:
			lowercase       =    [self.resize(image=snake_case		,    size=snake_case		,    resample=snake_case       ) for image in images]

		if do_center_crop:
			lowercase       =    [self.center_crop(image=snake_case		,    size=snake_case       ) for image in images]

		if do_rescale:
			lowercase       =    [self.rescale(image=snake_case		,    scale=snake_case       ) for image in images]

		if do_normalize:
			lowercase       =    [self.normalize(image=snake_case		,    mean=snake_case		,    std=snake_case       ) for image in images]

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    {'pixel_values': images}
		return BatchFeature(data=snake_case		,    tensor_type=snake_case       )

