
"""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    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			int									=						['pixel_values']




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

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




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = PILImageResampling.BICUBIC       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase       ,	default_to_square=UpperCAmelCase      )
							lowerCamelCase_ 		=     get_resize_output_image_size(
							    UpperCAmelCase       ,	size=(size['''height'''], size['''width'''])       ,	default_to_square=UpperCAmelCase      )
							return resize(UpperCAmelCase       ,	size=UpperCAmelCase       ,	resample=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							return center_crop(UpperCAmelCase       ,	size=(size['''height'''], size['''width'''])       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							return rescale(UpperCAmelCase       ,	scale=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							return normalize(UpperCAmelCase       ,	mean=UpperCAmelCase       ,	std=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = ChannelDimension.FIRST       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_ 		=     size if size is not None else self.size
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase       ,	default_to_square=UpperCAmelCase      )
							lowerCamelCase_ 		=     resample if resample is not None else self.resample
							lowerCamelCase_ 		=     do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase_ 		=     crop_size if crop_size is not None else self.crop_size
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							lowerCamelCase_ 		=     do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_ 		=     rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_ 		=     do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_ 		=     image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_ 		=     image_std if image_std is not None else self.image_std
							lowerCamelCase_ 		=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowerCamelCase_ 		=     make_list_of_images(UpperCAmelCase      )

							if not valid_images(UpperCAmelCase      ):
										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:
										lowerCamelCase_ 		=     [convert_to_rgb(UpperCAmelCase      ) for image in images]

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

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

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

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

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

							lowerCamelCase_ 		=     [to_channel_dimension_format(UpperCAmelCase       ,	UpperCAmelCase      ) for image in images]

							lowerCamelCase_ 		=     {'''pixel_values''': images}
							return BatchFeature(data=UpperCAmelCase       ,	tensor_type=UpperCAmelCase      )





"""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_						       =				{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Any									=						'swiftformer'




				def __init__(							self       ,	UpperCAmelCase=3       ,	UpperCAmelCase=[3, 3, 6, 4]       ,	UpperCAmelCase=[48, 56, 112, 220]       ,	UpperCAmelCase=4       ,	UpperCAmelCase=[True, True, True, True]       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=3       ,	UpperCAmelCase=2       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1e-5       ,	UpperCAmelCase=1e-5       ,	**UpperCAmelCase       ,	):
							super().__init__(**UpperCAmelCase      )
							lowerCamelCase_ 		=     num_channels
							lowerCamelCase_ 		=     depths
							lowerCamelCase_ 		=     embed_dims
							lowerCamelCase_ 		=     mlp_ratio
							lowerCamelCase_ 		=     downsamples
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     down_patch_size
							lowerCamelCase_ 		=     down_stride
							lowerCamelCase_ 		=     down_pad
							lowerCamelCase_ 		=     drop_path_rate
							lowerCamelCase_ 		=     use_layer_scale
							lowerCamelCase_ 		=     layer_scale_init_value
							lowerCamelCase_ 		=     batch_norm_eps




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			str									=						version.parse('1.11' )




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





				@property
				def 					UpperCAmelCase__	(							self      ):
							return 1e-4



