






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


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

class     _SCREAMING_SNAKE_CASE       (			SCREAMING_SNAKE_CASE__      ):
	snake_case__							:     Optional[Any]     	=  ['''pixel_values''']





	def __init__(		self				:					str ,  __lowerCamelCase				:					Any = True ,  __lowerCamelCase				:					Union[str, Any] = 32 ,  __lowerCamelCase				:					Dict=PILImageResampling.BILINEAR ,  __lowerCamelCase				:					Optional[int] = True ,  **__lowerCamelCase				:					List[Any] ,  ):
		UpperCamelCase     :Any          =	do_resize
		UpperCamelCase     :Dict          =	do_rescale
		UpperCamelCase     :List[Any]          =	size_divisor
		UpperCamelCase     :Tuple          =	resample
		super().__init__(**A__	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[Any] = None ,  **__lowerCamelCase				:					str	):
		UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	get_image_size(A__	)
		# Rounds the height and width down to the closest multiple of size_divisor
		UpperCamelCase     :Optional[int]          =	height // size_divisor * size_divisor
		UpperCamelCase     :Optional[Any]          =	width // size_divisor * size_divisor
		UpperCamelCase     :Optional[Any]          =	resize(A__ ,  (new_h, new_w) ,  resample=A__ ,  data_format=A__ ,  **A__	)
		return image





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					List[Any] = None ,  **__lowerCamelCase				:					Dict	):
		return rescale(image=A__ ,  scale=A__ ,  data_format=A__ ,  **A__	)





	def 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Union[str, Any] = None ,  __lowerCamelCase				:					Any = None ,  __lowerCamelCase				:					Union[str, Any]=None ,  __lowerCamelCase				:					List[str] = None ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					Union[str, Any] = ChannelDimension.FIRST ,  **__lowerCamelCase				:					Tuple ,  ):
		UpperCamelCase     :Union[str, Any]          =	do_resize if do_resize is not None else self.do_resize
		UpperCamelCase     :Optional[Any]          =	do_rescale if do_rescale is not None else self.do_rescale
		UpperCamelCase     :Dict          =	size_divisor if size_divisor is not None else self.size_divisor
		UpperCamelCase     :Optional[int]          =	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"""	)

		UpperCamelCase     :List[Any]          =	make_list_of_images(A__	)

		if not valid_images(A__	):
			raise ValueError("""Invalid image(s)"""	)

		# All transformations expect numpy arrays.
		UpperCamelCase     :List[Any]          =	[to_numpy_array(A__	) for img in images]

		if do_resize:
			UpperCamelCase     :str          =	[self.resize(A__ ,  size_divisor=A__ ,  resample=A__	) for image in images]

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

		UpperCamelCase     :str          =	[to_channel_dimension_format(A__ ,  A__	) for image in images]

		UpperCamelCase     :Optional[Any]          =	{"""pixel_values""": images}
		return BatchFeature(data=A__ ,  tensor_type=A__	)


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__		:int   =    logging.get_logger(__name__)

lowercase__		:Any   =    {
    "sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[Any]       					='''poolformer'''




							def __init__(				self   ,A__=3   ,A__=1_6   ,A__=1_6   ,A__=3   ,A__=4.0   ,A__=[2, 2, 6, 2]   ,A__=[6_4, 1_2_8, 3_2_0, 5_1_2]   ,A__=[7, 3, 3, 3]   ,A__=[4, 2, 2, 2]   ,A__=[2, 1, 1, 1]   ,A__=4   ,A__=0.0   ,A__="gelu"   ,A__=True   ,A__=1E-5   ,A__=0.02   ,**A__   ,):
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						stride
												lowercase							=						padding
												lowercase							=						pool_size
												lowercase							=						hidden_sizes
												lowercase							=						mlp_ratio
												lowercase							=						depths
												lowercase							=						patch_sizes
												lowercase							=						strides
												lowercase							=						num_encoder_blocks
												lowercase							=						drop_path_rate
												lowercase							=						hidden_act
												lowercase							=						use_layer_scale
												lowercase							=						layer_scale_init_value
												lowercase							=						initializer_range
												super().__init__(**A__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       List[Any]       					=version.parse('''1.11'''		)




							@property
							def  A__      (				self):
												return OrderedDict(
												    [
												        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
												    ])





							@property
							def  A__      (				self):
												return 2E-3




