





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = 0.9     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : int   					=						size if size is not None else {'shortest_edge': 224}
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__       : str   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Tuple   					=						do_resize
     lowerCamelCase__       : str   					=						size
     lowerCamelCase__       : List[str]   					=						crop_pct
     lowerCamelCase__       : Any   					=						resample
     lowerCamelCase__       : Tuple   					=						do_center_crop
     lowerCamelCase__       : Any   					=						crop_size
     lowerCamelCase__       : Optional[int]   					=						do_rescale
     lowerCamelCase__       : Optional[Any]   					=						rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     lowerCamelCase__       : Optional[Any]   					=						image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[float] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : int     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Any   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
     if crop_pct is not None:
         if "shortest_edge" in size:
             lowerCamelCase__       : List[Any]   					=						int(size['shortest_edge'] / crop_pct      )
         elif "height" in size and "width" in size:
             if size["height"] == size["width"]:
                 lowerCamelCase__       : int   					=						int(size['height'] / crop_pct      )
             else:
                 lowerCamelCase__       : Any   					=						(int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

         lowerCamelCase__       : Union[str, Any]   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     else:
         if "shortest_edge" in size:
             lowerCamelCase__       : int   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=size['shortest_edge']     ,	default_to_square=UpperCAmelCase      )
         elif "height" in size and "width" in size:
             lowerCamelCase__       : List[Any]   					=						(size['height'], size['width'])
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Union[str, Any]   					=						get_size_dict(UpperCAmelCase      )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""      )
     return center_crop(UpperCAmelCase     ,	size=(size['height'], size['width'])     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      int:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )







 def 	A_      ( self    : str     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Any     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : Optional[Any]   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Any   					=						crop_pct if crop_pct is not None else self.crop_pct
     lowerCamelCase__       : Any   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Any   					=						do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : str   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__       : int   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : 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 or resample is None:
         raise ValueError('Size and resample must be specified if do_resize is True.'      )

     if do_center_crop and crop_pct is None:
         raise ValueError('Crop_pct 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.'      )

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

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

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

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

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

     lowerCamelCase__       : Dict   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

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












from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Union[str, Any]        =							{
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""efficientformer"""
 def __init__( self    : Dict     ,	UpperCAmelCase    : List[int] = [3, 2, 6, 4]     ,	UpperCAmelCase    : List[int] = [48, 96, 224, 448]     ,	UpperCAmelCase    : List[bool] = [True, True, True, True]     ,	UpperCAmelCase    : int = 448     ,	UpperCAmelCase    : int = 32     ,	UpperCAmelCase    : int = 4     ,	UpperCAmelCase    : int = 7     ,	UpperCAmelCase    : int = 5     ,	UpperCAmelCase    : int = 8     ,	UpperCAmelCase    : int = 4     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : int = 16     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 3     ,	UpperCAmelCase    : int = 2     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : float = 1e-5     ,	UpperCAmelCase    : str = "gelu"     ,	UpperCAmelCase    : float = 0.0_2     ,	UpperCAmelCase    : float = 1e-12     ,	UpperCAmelCase    : int = 224     ,	UpperCAmelCase    : float = 1e-05     ,	**UpperCAmelCase    : Tuple     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						hidden_act
     lowerCamelCase__       : int   					=						hidden_dropout_prob
     lowerCamelCase__       : List[Any]   					=						hidden_sizes
     lowerCamelCase__       : int   					=						num_hidden_layers
     lowerCamelCase__       : int   					=						num_attention_heads
     lowerCamelCase__       : Dict   					=						initializer_range
     lowerCamelCase__       : Optional[Any]   					=						layer_norm_eps
     lowerCamelCase__       : int   					=						patch_size
     lowerCamelCase__       : List[Any]   					=						num_channels
     lowerCamelCase__       : List[str]   					=						depths
     lowerCamelCase__       : Any   					=						mlp_expansion_ratio
     lowerCamelCase__       : Any   					=						downsamples
     lowerCamelCase__       : str   					=						dim
     lowerCamelCase__       : Tuple   					=						key_dim
     lowerCamelCase__       : int   					=						attention_ratio
     lowerCamelCase__       : int   					=						resolution
     lowerCamelCase__       : Dict   					=						pool_size
     lowerCamelCase__       : List[str]   					=						downsample_patch_size
     lowerCamelCase__       : Tuple   					=						downsample_stride
     lowerCamelCase__       : int   					=						downsample_pad
     lowerCamelCase__       : Optional[int]   					=						drop_path_rate
     lowerCamelCase__       : Optional[Any]   					=						num_metaad_blocks
     lowerCamelCase__       : Any   					=						distillation
     lowerCamelCase__       : Optional[int]   					=						use_layer_scale
     lowerCamelCase__       : Union[str, Any]   					=						layer_scale_init_value
     lowerCamelCase__       : Optional[int]   					=						image_size
     lowerCamelCase__       : Optional[Any]   					=						batch_norm_eps





