
"""simple docstring"""




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class 						a__		:
   def __init__(     self      ,       _a      ,       _a=sys.maxsize							):
          lowercase       :							str									=				'bilinear'
          lowercase       :							str									=				max_size
          lowercase       :							int									=				short_edge_length

   def __call__(     self      ,       _a							):
          lowercase       :							Optional[int]									=				[]
          for img in imgs:
                 lowercase       :							List[Any]									=				img.shape[:2]
                 # later: provide list and randomly choose index for resize
                 lowercase       :							Tuple									=				np.random.randint(self.short_edge_length[0]      ,       self.short_edge_length[1] + 1							)
                 if size == 0:
                        return img
                 lowercase       :							Optional[int]									=				size * 1.0 / min(lowercase_      ,       lowercase_							)
                 if h < w:
                        lowercase       :							int									=				size, scale * w
                 else:
                        lowercase       :							Union[str, Any]									=				scale * h, size
                 if max(lowercase_      ,       lowercase_							) > self.max_size:
                        lowercase       :							Tuple									=				self.max_size * 1.0 / max(lowercase_      ,       lowercase_							)
                        lowercase       :							List[Any]									=				newh * scale
                        lowercase       :							Optional[int]									=				neww * scale
                 lowercase       :							List[str]									=				int(neww + 0.5							)
                 lowercase       :							Optional[int]									=				int(newh + 0.5							)

                 if img.dtype == np.uinta:
                        lowercase       :							List[str]									=				Image.fromarray(lowercase_							)
                        lowercase       :							Optional[Any]									=				pil_image.resize((neww, newh)      ,       PILImageResampling.BILINEAR							)
                        lowercase       :							Any									=				np.asarray(lowercase_							)
                 else:
                        lowercase       :							Dict									=				img.permute(2      ,       0      ,       1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
                        lowercase       :							List[Any]									=				nn.functional.interpolate(
                            lowercase_      ,       (newh, neww)      ,       mode=self.interp_method      ,       align_corners=lowercase_							).squeeze(0							)
                 img_augs.append(lowercase_							)

          return img_augs


class 						a__		:
   def __init__(     self      ,       _a							):
          lowercase       :							Optional[Any]									=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,       cfg.INPUT.MAX_SIZE_TEST							)
          lowercase       :							List[Any]									=				cfg.INPUT.FORMAT
          lowercase       :							Tuple									=				cfg.SIZE_DIVISIBILITY
          lowercase       :							Tuple									=				cfg.PAD_VALUE
          lowercase       :							Optional[Any]									=				cfg.INPUT.MAX_SIZE_TEST
          lowercase       :							Tuple									=				cfg.MODEL.DEVICE
          lowercase       :							List[str]									=				torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)      ,       1      ,       1							)
          lowercase       :							Dict									=				torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)      ,       1      ,       1							)
          lowercase       :							Tuple									=				lambda _a							: (x - self.pixel_mean) / self.pixel_std
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							List[Any]									=				tuple(max(lowercase_							) for s in zip(*[img.shape for img in images]							)							)
          lowercase       :							Optional[Any]									=				[im.shape[-2:] for im in images]
          lowercase       :							Optional[int]									=				[
              nn.functional.pad(
                  lowercase_      ,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,       value=self.pad_value      ,       )
              for size, im in zip(lowercase_      ,       lowercase_							)
          ]

          return torch.stack(lowercase_							), torch.tensor(lowercase_							)

   def __call__(     self      ,       _a      ,       _a=False							):




          with torch.no_grad():
                 if not isinstance(lowercase_      ,       lowercase_							):
                        lowercase       :							List[Any]									=				[images]
                 if single_image:
                        assert len(lowercase_							) == 1
                 for i in range(len(lowercase_							)							):
                        if isinstance(images[i]      ,       torch.Tensor							):
                               images.insert(lowercase_      ,       images.pop(lowercase_							).to(self.device							).float()							)
                        elif not isinstance(images[i]      ,       torch.Tensor							):
                               images.insert(
                                   lowercase_      ,       torch.as_tensor(img_tensorize(images.pop(lowercase_							)      ,       input_format=self.input_format							)							)
                                   .to(self.device							)
                                   .float()      ,       )
            # resize smallest edge
                 lowercase       :							Optional[int]									=				torch.tensor([im.shape[:2] for im in images]							)
                 lowercase       :							Optional[Any]									=				self.aug(lowercase_							)
                 # transpose images and convert to torch tensors
                 # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                 # now normalize before pad to avoid useless arithmetic
                 lowercase       :							Union[str, Any]									=				[self.normalizer(lowercase_							) for x in images]
                 # now pad them to do the following operations
                 lowercase       :							Optional[int]									=				self.pad(lowercase_							)
                 # Normalize

                 if self.size_divisibility > 0:
                        raise NotImplementedError()
                 # pad
                 lowercase       :							List[Any]									=				torch.true_divide(lowercase_      ,       lowercase_							)
                 if single_image:
                        return images[0], sizes[0], scales_yx[0]
                 else:
                        return images, sizes, scales_yx


def     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			Optional[int]    )					->							int:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes


def     __magic_name__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			Tuple[int, int]    )					->							Tuple:
       assert torch.isfinite(SCREAMING_SNAKE_CASE__    ).all(), "Box tensor contains infinite or NaN!"
       lowercase       :							Any									=				box_size
       tensor[:, 0].clamp_(min=0	,					max=SCREAMING_SNAKE_CASE__    )
       tensor[:, 1].clamp_(min=0	,					max=SCREAMING_SNAKE_CASE__    )
       tensor[:, 2].clamp_(min=0	,					max=SCREAMING_SNAKE_CASE__    )
       tensor[:, 3].clamp_(min=0	,					max=SCREAMING_SNAKE_CASE__    )






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}




class A    (  UpperCamelCase_       ,				UpperCamelCase_	):
      UpperCamelCase__				:    str ='focalnet'






      def __init__(   self		:	Optional[int]     ,  lowercase_		:	List[Any]=224     ,  lowercase_		:	str=4     ,  lowercase_		:	Any=3     ,  lowercase_		:	Dict=96     ,  lowercase_		:	List[str]=False     ,  lowercase_		:	Any=[192, 384, 768, 768]     ,  lowercase_		:	Dict=[2, 2, 6, 2]     ,  lowercase_		:	Optional[int]=[2, 2, 2, 2]     ,  lowercase_		:	List[Any]=[3, 3, 3, 3]     ,  lowercase_		:	Any="gelu"     ,  lowercase_		:	str=4.0     ,  lowercase_		:	List[Any]=0.0     ,  lowercase_		:	Tuple=0.1     ,  lowercase_		:	Optional[Any]=False     ,  lowercase_		:	Optional[int]=1E-4     ,  lowercase_		:	Dict=False     ,  lowercase_		:	Optional[Any]=False     ,  lowercase_		:	Dict=False     ,  lowercase_		:	List[Any]=0.02     ,  lowercase_		:	Any=1E-5     ,  lowercase_		:	Any=32     ,  lowercase_		:	List[str]=None     ,  lowercase_		:	int=None     ,  **lowercase_		:	Optional[Any]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             super().__init__(**lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =image_size
             _lowerCamelCase			:						str            =patch_size
             _lowerCamelCase			:						str            =num_channels
             _lowerCamelCase			:						Tuple            =embed_dim
             _lowerCamelCase			:						Dict            =use_conv_embed
             _lowerCamelCase			:						int            =hidden_sizes
             _lowerCamelCase			:						str            =depths
             _lowerCamelCase			:						int            =focal_levels
             _lowerCamelCase			:						List[Any]            =focal_windows
             _lowerCamelCase			:						List[Any]            =hidden_act
             _lowerCamelCase			:						int            =mlp_ratio
             _lowerCamelCase			:						Any            =hidden_dropout_prob
             _lowerCamelCase			:						Optional[Any]            =drop_path_rate
             _lowerCamelCase			:						int            =use_layerscale
             _lowerCamelCase			:						str            =layerscale_value
             _lowerCamelCase			:						Dict            =use_post_layernorm
             _lowerCamelCase			:						str            =use_post_layernorm_in_modulation
             _lowerCamelCase			:						List[Any]            =normalize_modulator
             _lowerCamelCase			:						Optional[Any]            =initializer_range
             _lowerCamelCase			:						List[str]            =layer_norm_eps
             _lowerCamelCase			:						Optional[int]            =encoder_stride
             _lowerCamelCase			:						List[str]            =['stem'] + [F'''stage{idx}''' for idx in range(1     ,  len(self.depths					) + 1					)]
             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =get_aligned_output_features_output_indices(
                 out_features=lowercase_     ,  out_indices=lowercase_     ,  stage_names=self.stage_names					)


