





"""simple docstring"""



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


a_					=     logging.get_logger(__name__)

a_					=     {
    """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 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""poolformer"""

 def __init__(       self   ,		__lowerCamelCase=3   ,		__lowerCamelCase=16   ,		__lowerCamelCase=16   ,		__lowerCamelCase=3   ,		__lowerCamelCase=4.0   ,		__lowerCamelCase=[2, 2, 6, 2]   ,		__lowerCamelCase=[64, 128, 320, 512]   ,		__lowerCamelCase=[7, 3, 3, 3]   ,		__lowerCamelCase=[4, 2, 2, 2]   ,		__lowerCamelCase=[2, 1, 1, 1]   ,		__lowerCamelCase=4   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=True   ,		__lowerCamelCase=1e-5   ,		__lowerCamelCase=0.0_2   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		str							=	num_channels
     __A       :		List[str]							=	patch_size
     __A       :		str							=	stride
     __A       :		Any							=	padding
     __A       :		Any							=	pool_size
     __A       :		Dict							=	hidden_sizes
     __A       :		Optional[Any]							=	mlp_ratio
     __A       :		Any							=	depths
     __A       :		List[str]							=	patch_sizes
     __A       :		Union[str, Any]							=	strides
     __A       :		List[str]							=	num_encoder_blocks
     __A       :		Optional[int]							=	drop_path_rate
     __A       :		Union[str, Any]							=	hidden_act
     __A       :		Optional[Any]							=	use_layer_scale
     __A       :		List[Any]							=	layer_scale_init_value
     __A       :		List[Any]							=	initializer_range
     super().__init__(**__lowerCamelCase    )






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	version.parse("""1.11"""	)

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 2e-3











"""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


a_					=     logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	["""pixel_values"""]

 def __init__(       self   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		__lowerCamelCase = 1 / 255   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase    )
     __A       :		Optional[int]							=	size if size is not None else {'''shortest_edge''': 224}
     __A       :		Optional[int]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     __A       :		Dict							=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     __A       :		str							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase   ,		param_name='''crop_size'''    )

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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	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()}"""    )
     __A       :		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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = ChannelDimension.FIRST   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




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

     __A       :		int							=	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:
         __A       :		List[str]							=	[convert_to_rgb(__lowerCamelCase    ) for image in images]

     # All transformations expect numpy arrays.
     __A       :		Optional[int]							=	[to_numpy_array(__lowerCamelCase    ) for image in images]

     if do_resize:
         __A       :		List[str]							=	[self.resize(image=__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase    ) for image in images]

     if do_center_crop:
         __A       :		Dict							=	[self.center_crop(image=__lowerCamelCase   ,		size=__lowerCamelCase    ) for image in images]

     if do_rescale:
         __A       :		str							=	[self.rescale(image=__lowerCamelCase   ,		scale=__lowerCamelCase    ) for image in images]

     if do_normalize:
         __A       :		Optional[Any]							=	[self.normalize(image=__lowerCamelCase   ,		mean=__lowerCamelCase   ,		std=__lowerCamelCase    ) for image in images]

     __A       :		str							=	[to_channel_dimension_format(__lowerCamelCase   ,		__lowerCamelCase    ) for image in images]

     __A       :		Union[str, Any]							=	{'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase   ,		tensor_type=__lowerCamelCase    )




