






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


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)
class 							__magic_name__      (  _A     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[str]	=   ["pixel_values"]


 def __init__(		self							,					_A = True							,					_A = None							,					_A = PILImageResampling.BILINEAR							,					_A = True							,					_A = 1 / 255							,					_A = True							,					_A = None							,					_A = None							,					**_A							,					)	->						None:

     """simple docstring"""



     super().__init__(**UpperCamelCase__)
     _UpperCAmelCase			: Optional[Any]     					=			size if size is not None else {'''height''': 224, '''width''': 224}
     _UpperCAmelCase			: Dict     					=			get_size_dict(UpperCamelCase__)
     _UpperCAmelCase			: Optional[int]     					=			do_resize
     _UpperCAmelCase			: Optional[Any]     					=			do_rescale
     _UpperCAmelCase			: Dict     					=			do_normalize
     _UpperCAmelCase			: Tuple     					=			size
     _UpperCAmelCase			: Dict     					=			resample
     _UpperCAmelCase			: int     					=			rescale_factor
     _UpperCAmelCase			: Optional[Any]     					=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _UpperCAmelCase			: str     					=			image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 	snake_case__			(		self							,					_A							,					_A							,					_A = PILImageResampling.BILINEAR							,					_A = None							,					**_A							,					)	->						np.ndarray:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			get_size_dict(UpperCamelCase__)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''')
     return resize(
         UpperCamelCase__							,					size=(size['''height'''], size['''width'''])							,					resample=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__)


 def 	snake_case__			(		self							,					_A							,					_A							,					_A = None							,					**_A)	->						np.ndarray:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A							,					_A = None							,					**_A							,					)	->						np.ndarray:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = ChannelDimension.FIRST							,					**_A							,					)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			do_resize if do_resize is not None else self.do_resize
     _UpperCAmelCase			: Dict     					=			do_rescale if do_rescale is not None else self.do_rescale
     _UpperCAmelCase			: Union[str, Any]     					=			do_normalize if do_normalize is not None else self.do_normalize
     _UpperCAmelCase			: List[str]     					=			resample if resample is not None else self.resample
     _UpperCAmelCase			: Tuple     					=			rescale_factor if rescale_factor is not None else self.rescale_factor
     _UpperCAmelCase			: Any     					=			image_mean if image_mean is not None else self.image_mean
     _UpperCAmelCase			: Tuple     					=			image_std if image_std is not None else self.image_std

     _UpperCAmelCase			: Dict     					=			size if size is not None else self.size
     _UpperCAmelCase			: Dict     					=			get_size_dict(UpperCamelCase__)

     _UpperCAmelCase			: Any     					=			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_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

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

     if do_resize:
         _UpperCAmelCase			: List[Any]     					=			[self.resize(image=UpperCamelCase__							,					size=UpperCamelCase__							,					resample=UpperCamelCase__) for image in images]

     if do_rescale:
         _UpperCAmelCase			: Tuple     					=			[self.rescale(image=UpperCamelCase__							,					scale=UpperCamelCase__) for image in images]

     if do_normalize:
         _UpperCAmelCase			: Union[str, Any]     					=			[self.normalize(image=UpperCamelCase__							,					mean=UpperCamelCase__							,					std=UpperCamelCase__) for image in images]

     _UpperCAmelCase			: List[Any]     					=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__) for image in images]

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












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


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "swiftformer"


 def __init__(		self							,					_A=3							,					_A=[3, 3, 6, 4]							,					_A=[48, 56, 112, 220]							,					_A=4							,					_A=[True, True, True, True]							,					_A="gelu"							,					_A=3							,					_A=2							,					_A=1							,					_A=0.0							,					_A=True							,					_A=1e-5							,					_A=1e-5							,					**_A							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase			: Tuple     					=			num_channels
     _UpperCAmelCase			: Optional[int]     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			embed_dims
     _UpperCAmelCase			: Dict     					=			mlp_ratio
     _UpperCAmelCase			: Tuple     					=			downsamples
     _UpperCAmelCase			: str     					=			hidden_act
     _UpperCAmelCase			: int     					=			down_patch_size
     _UpperCAmelCase			: Dict     					=			down_stride
     _UpperCAmelCase			: Dict     					=			down_pad
     _UpperCAmelCase			: Tuple     					=			drop_path_rate
     _UpperCAmelCase			: Tuple     					=			use_layer_scale
     _UpperCAmelCase			: int     					=			layer_scale_init_value
     _UpperCAmelCase			: int     					=			batch_norm_eps



class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[Any]	=   version.parse("1.11"     )


 @property
 def 	snake_case__			(		self)	->						Mapping[str, Mapping[int, str]]:

     """simple docstring"""



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


 @property
 def 	snake_case__			(		self)	->						float:

     """simple docstring"""



     return 1e-4




