

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      ):





            """simple docstring"""

            _UpperCamelCase              =						'''bilinear'''
            _UpperCamelCase              =						max_size
            _UpperCamelCase              =						short_edge_length



     def __call__(      self,       A      ):





            """simple docstring"""

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

                   if img.dtype == np.uinta:
                          _UpperCamelCase              =						Image.fromarray(A      )
                          _UpperCamelCase              =						pil_image.resize((neww, newh),       PILImageResampling.BILINEAR      )
                          _UpperCamelCase              =						np.asarray(A      )
                   else:
                          _UpperCamelCase              =						img.permute(2,       0,       1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                          _UpperCamelCase              =						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      ):





            """simple docstring"""

            _UpperCamelCase              =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],       cfg.INPUT.MAX_SIZE_TEST      )
            _UpperCamelCase              =						cfg.INPUT.FORMAT
            _UpperCamelCase              =						cfg.SIZE_DIVISIBILITY
            _UpperCamelCase              =						cfg.PAD_VALUE
            _UpperCamelCase              =						cfg.INPUT.MAX_SIZE_TEST
            _UpperCamelCase              =						cfg.MODEL.DEVICE
            _UpperCamelCase              =						torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      ),       1,       1      )
            _UpperCamelCase              =						torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      ),       1,       1      )
            _UpperCamelCase              =						lambda A      : (x - self.pixel_mean) / self.pixel_std

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						tuple(max(A      ) for s in zip(*[img.shape for img in images]      )      )
            _UpperCamelCase              =						[im.shape[-2:] for im in images]
            _UpperCamelCase              =						[
                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      ):





            """simple docstring"""

            with torch.no_grad():
                   if not isinstance(A,       A      ):
                          _UpperCamelCase              =						[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
                   _UpperCamelCase              =						torch.tensor([im.shape[:2] for im in images]      )
                   _UpperCamelCase              =						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
                   _UpperCamelCase              =						[self.normalizer(A      ) for x in images]
                   # now pad them to do the following operations
                   _UpperCamelCase       ,		_UpperCamelCase              =						self.pad(A      )
                   # Normalize

                   if self.size_divisibility > 0:
                          raise NotImplementedError()
                   # pad
                   _UpperCamelCase              =						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			(      lowerCAmelCase    ,       lowerCAmelCase					):
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       assert torch.isfinite(lowerCAmelCase					).all(), "Box tensor contains infinite or NaN!"
       _UpperCamelCase       ,		_UpperCamelCase              =						box_size
       tensor[:, 0].clamp_(min=0    ,       max=lowerCAmelCase					)
       tensor[:, 1].clamp_(min=0    ,       max=lowerCAmelCase					)
       tensor[:, 2].clamp_(min=0    ,       max=lowerCAmelCase					)
       tensor[:, 3].clamp_(min=0    ,       max=lowerCAmelCase					)













import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase    : Optional[int]    	=  logging.get_logger(__name__)

lowercase    : List[Any]    	=  {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "detr"
     __A	  =       ["past_key_values"]
     __A	  =       {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }

     def __init__(      self,       A=True,       A=None,       A=3,       A=100,       A=6,       A=2048,       A=8,       A=6,       A=2048,       A=8,       A=0.0,       A=0.0,       A=True,       A="relu",       A=256,       A=0.1,       A=0.0,       A=0.0,       A=0.02,       A=1.0,       A=False,       A="sine",       A="resnet50",       A=True,       A=False,       A=1,       A=5,       A=2,       A=1,       A=1,       A=5,       A=2,       A=0.1,       **A,       ):





            """simple docstring"""

            if backbone_config is not None and use_timm_backbone:
                   raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''      )

            if not use_timm_backbone:
                   if backbone_config is None:
                          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
                   elif isinstance(A,       A      ):
                          _UpperCamelCase              =						backbone_config.get('''model_type'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING[backbone_model_type]
                          _UpperCamelCase              =						config_class.from_dict(A      )
                   # set timm attributes to None
                   _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						None, None, None

            _UpperCamelCase              =						use_timm_backbone
            _UpperCamelCase              =						backbone_config
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						num_queries
            _UpperCamelCase              =						d_model
            _UpperCamelCase              =						encoder_ffn_dim
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						encoder_attention_heads
            _UpperCamelCase              =						decoder_ffn_dim
            _UpperCamelCase              =						decoder_layers
            _UpperCamelCase              =						decoder_attention_heads
            _UpperCamelCase              =						dropout
            _UpperCamelCase              =						attention_dropout
            _UpperCamelCase              =						activation_dropout
            _UpperCamelCase              =						activation_function
            _UpperCamelCase              =						init_std
            _UpperCamelCase              =						init_xavier_std
            _UpperCamelCase              =						encoder_layerdrop
            _UpperCamelCase              =						decoder_layerdrop
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						auxiliary_loss
            _UpperCamelCase              =						position_embedding_type
            _UpperCamelCase              =						backbone
            _UpperCamelCase              =						use_pretrained_backbone
            _UpperCamelCase              =						dilation
            # Hungarian matcher
            _UpperCamelCase              =						class_cost
            _UpperCamelCase              =						bbox_cost
            _UpperCamelCase              =						giou_cost
            # Loss coefficients
            _UpperCamelCase              =						mask_loss_coefficient
            _UpperCamelCase              =						dice_loss_coefficient
            _UpperCamelCase              =						bbox_loss_coefficient
            _UpperCamelCase              =						giou_loss_coefficient
            _UpperCamelCase              =						eos_coefficient
            super().__init__(is_encoder_decoder=A,       **A      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.encoder_attention_heads

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.d_model

     @classmethod
     def 	_UpperCamelCase     (      cls,       A,       **A      ):





            """simple docstring"""

            return cls(backbone_config=A,       **A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						copy.deepcopy(self.__dict__      )
            if output["backbone_config"] is not None:
                   _UpperCamelCase              =						self.backbone_config.to_dict()
            _UpperCamelCase              =						self.__class__.model_type
            return output






class  __A(						__UpperCAmelCase ):
     __A	  =       version.parse("1.11" )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 1E-5



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 12






