






'''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)			->      List[str]:
						"""simple docstring"""






						__snake_case				:      Union[str, Any]     							= 'bilinear'
						__snake_case				:      Optional[int]     							= max_size
						__snake_case				:      str     							= short_edge_length







			def __call__(self					,   __a)			->      int:
						"""simple docstring"""






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

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

						return img_augs




class a_    :







			def __init__(self					,   __a)			->      int:
						"""simple docstring"""






						__snake_case				:      Union[str, Any]     							= ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]					,   cfg.INPUT.MAX_SIZE_TEST)
						__snake_case				:      str     							= cfg.INPUT.FORMAT
						__snake_case				:      Any     							= cfg.SIZE_DIVISIBILITY
						__snake_case				:      Union[str, Any]     							= cfg.PAD_VALUE
						__snake_case				:      Union[str, Any]     							= cfg.INPUT.MAX_SIZE_TEST
						__snake_case				:      List[Any]     							= cfg.MODEL.DEVICE
						__snake_case				:      List[str]     							= torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)					,   1					,   1)
						__snake_case				:      Union[str, Any]     							= torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)					,   1					,   1)
						__snake_case				:      Optional[Any]     							= lambda __a: (x - self.pixel_mean) / self.pixel_std







			def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Dict:
						"""simple docstring"""






						__snake_case				:      int     							= tuple(max(__a) for s in zip(*[img.shape for img in images]))
						__snake_case				:      int     							= [im.shape[-2:] for im in images]
						__snake_case				:      Dict     							= [
						    nn.functional.pad(
						        __a					,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]					,   value=self.pad_value					,   )
						    for size, im in zip(__a					,   __a)
						]

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







			def __call__(self					,   __a					,   __a=False)			->      Union[str, Any]:
						"""simple docstring"""






						with torch.no_grad():
									if not isinstance(__a					,   __a):
												__snake_case				:      List[Any]     							= [images]
									if single_image:
												assert len(__a) == 1
									for i in range(len(__a)):
												if isinstance(images[i]					,   torch.Tensor):
															images.insert(__a					,   images.pop(__a).to(self.device).float())
												elif not isinstance(images[i]					,   torch.Tensor):
															images.insert(
															    __a					,   torch.as_tensor(img_tensorize(images.pop(__a)					,   input_format=self.input_format))
															    .to(self.device)
															    .float()					,   )
												# resize smallest edge
									__snake_case				:      List[Any]     							= torch.tensor([im.shape[:2] for im in images])
									__snake_case				:      Optional[Any]     							= self.aug(__a)
									# 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
									__snake_case				:      int     							= [self.normalizer(__a) for x in images]
									# now pad them to do the following operations
									__snake_case				:      Tuple     							= self.pad(__a)
									# Normalize

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




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       List[Any]  )    -> Union[str, Any]:







			"""simple docstring"""
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes





def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       Tuple[int, int]  )    -> Optional[int]:







			"""simple docstring"""
			assert torch.isfinite(A  ).all(), "Box tensor contains infinite or NaN!"
			__snake_case				:      Optional[int]     							= box_size
			tensor[:, 0].clamp_(min=0  ,		max=A  )
			tensor[:, 1].clamp_(min=0  ,		max=A  )
			tensor[:, 2].clamp_(min=0  ,		max=A  )
			tensor[:, 3].clamp_(min=0  ,		max=A  )







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A    	=   {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''TimmBackbone''']


if TYPE_CHECKING:
      from .configuration_timm_backbone import TimmBackboneConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_timm_backbone import TimmBackbone

else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)