





import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=XCLIPTextConfig()

   # derive patch size from model name
   UpperCAmelCase__						=model_name.find("patch"     )
   UpperCAmelCase__						=int(model_name[start_idx + len("patch"     ) : start_idx + len("patch"     ) + 2]     )
   UpperCAmelCase__						=XCLIPVisionConfig(patch_size=__snake_case				,							num_frames=__snake_case     )

   if "large" in model_name:
      UpperCAmelCase__						=768
      UpperCAmelCase__						=3072
      UpperCAmelCase__						=12

      UpperCAmelCase__						=1024
      UpperCAmelCase__						=4096
      UpperCAmelCase__						=16
      UpperCAmelCase__						=24
      UpperCAmelCase__						=768
      UpperCAmelCase__						=3072

   if model_name == "xclip-large-patch14-16-frames":
      UpperCAmelCase__						=336

   UpperCAmelCase__						=XCLIPConfig.from_text_vision_configs(__snake_case				,							__snake_case     )

   if "large" in model_name:
      UpperCAmelCase__						=768

   return config






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if name == "token_embedding.weight":
      UpperCAmelCase__						=name.replace("token_embedding.weight"				,							"text_model.embeddings.token_embedding.weight"     )
   if name == "positional_embedding":
      UpperCAmelCase__						=name.replace("positional_embedding"				,							"text_model.embeddings.position_embedding.weight"     )
   if "ln_1" in name:
      UpperCAmelCase__						=name.replace("ln_1"				,							"layer_norm1"     )
   if "ln_2" in name:
      UpperCAmelCase__						=name.replace("ln_2"				,							"layer_norm2"     )
   if "c_fc" in name:
      UpperCAmelCase__						=name.replace("c_fc"				,							"fc1"     )
   if "c_proj" in name:
      UpperCAmelCase__						=name.replace("c_proj"				,							"fc2"     )
   if name.startswith("transformer.resblocks"     ):
      UpperCAmelCase__						=name.replace("transformer.resblocks"				,							"text_model.encoder.layers"     )
   if "attn.out_proj" in name and "message" not in name:
      UpperCAmelCase__						=name.replace("attn.out_proj"				,							"self_attn.out_proj"     )
   if "ln_final" in name:
      UpperCAmelCase__						=name.replace("ln_final"				,							"text_model.final_layer_norm"     )
   # visual encoder
   if name == "visual.class_embedding":
      UpperCAmelCase__						=name.replace("visual.class_embedding"				,							"vision_model.embeddings.class_embedding"     )
   if name == "visual.positional_embedding":
      UpperCAmelCase__						=name.replace("visual.positional_embedding"				,							"vision_model.embeddings.position_embedding.weight"     )
   if name.startswith("visual.transformer.resblocks"     ):
      UpperCAmelCase__						=name.replace("visual.transformer.resblocks"				,							"vision_model.encoder.layers"     )
   if "visual.conv1" in name:
      UpperCAmelCase__						=name.replace("visual.conv1"				,							"vision_model.embeddings.patch_embedding"     )
   if "visual.ln_pre" in name:
      UpperCAmelCase__						=name.replace("visual.ln_pre"				,							"vision_model.pre_layernorm"     )
   if "visual.ln_post" in name:
      UpperCAmelCase__						=name.replace("visual.ln_post"				,							"vision_model.post_layernorm"     )
   if "visual.proj" in name:
      UpperCAmelCase__						=name.replace("visual.proj"				,							"visual_projection.weight"     )
   if "text_projection" in name:
      UpperCAmelCase__						=name.replace("text_projection"				,							"text_projection.weight"     )
   # things on top
   if "prompts_visual_proj" in name:
      UpperCAmelCase__						=name.replace("prompts_visual_proj"				,							"prompts_visual_projection"     )
   if "prompts_visual_ln" in name:
      UpperCAmelCase__						=name.replace("prompts_visual_ln"				,							"prompts_visual_layernorm"     )
   # mit
   if name == "mit.positional_embedding":
      UpperCAmelCase__						=name.replace("positional"				,							"position"     )
   if name.startswith("mit.resblocks"     ):
      UpperCAmelCase__						=name.replace("mit.resblocks"				,							"mit.encoder.layers"     )
   # prompts generator
   if name.startswith("prompts_generator.norm"     ):
      UpperCAmelCase__						=name.replace("prompts_generator.norm"				,							"prompts_generator.layernorm"     )

   return name






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   for key in orig_state_dict.copy().keys():
      UpperCAmelCase__						=orig_state_dict.pop(__snake_case     )

      if "attn.in_proj" in key:
         UpperCAmelCase__						=key.split("."     )
         if key.startswith("visual"     ):
            UpperCAmelCase__						=key_split[3]
            UpperCAmelCase__						=config.vision_config.hidden_size
            if "message_attn" in key:
               if "weight" in key:
                  UpperCAmelCase__						=val[
                      :dim, :
                  ]
                  UpperCAmelCase__						=val[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase__						=val[
                      -dim:, :
                  ]
               else:
                  UpperCAmelCase__						=val[
                      :dim
                  ]
                  UpperCAmelCase__						=val[
                      dim : dim * 2
                  ]
                  UpperCAmelCase__						=val[
                      -dim:
                  ]
            else:
               if "weight" in key:
                  UpperCAmelCase__						=val[
                      :dim, :
                  ]
                  UpperCAmelCase__						=val[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase__						=val[
                      -dim:, :
                  ]
               else:
                  UpperCAmelCase__						=val[:dim]
                  UpperCAmelCase__						=val[
                      dim : dim * 2
                  ]
                  UpperCAmelCase__						=val[-dim:]
         elif key.startswith("mit"     ):
            UpperCAmelCase__						=key_split[2]
            UpperCAmelCase__						=config.vision_config.mit_hidden_size
            if "weight" in key:
               UpperCAmelCase__						=val[:dim, :]
               UpperCAmelCase__						=val[dim : dim * 2, :]
               UpperCAmelCase__						=val[-dim:, :]
            else:
               UpperCAmelCase__						=val[:dim]
               UpperCAmelCase__						=val[dim : dim * 2]
               UpperCAmelCase__						=val[-dim:]
         else:
            UpperCAmelCase__						=key_split[2]
            UpperCAmelCase__						=config.text_config.hidden_size
            if "weight" in key:
               UpperCAmelCase__						=val[:dim, :]
               UpperCAmelCase__						=val[
                   dim : dim * 2, :
               ]
               UpperCAmelCase__						=val[-dim:, :]
            else:
               UpperCAmelCase__						=val[:dim]
               UpperCAmelCase__						=val[
                   dim : dim * 2
               ]
               UpperCAmelCase__						=val[-dim:]
      else:
         UpperCAmelCase__						=rename_key(__snake_case     )
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            UpperCAmelCase__						=val.T
         UpperCAmelCase__						=val

   return orig_state_dict






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if num_frames == 8:
      UpperCAmelCase__						='eating_spaghetti_8_frames.npy'
   elif num_frames == 16:
      UpperCAmelCase__						='eating_spaghetti.npy'
   elif num_frames == 32:
      UpperCAmelCase__						='eating_spaghetti_32_frames.npy'
   UpperCAmelCase__						=hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video"				,							filename=__snake_case				,							repo_type="dataset"				,							)
   UpperCAmelCase__						=np.load(__snake_case     )
   return list(__snake_case     )






def 				_UpperCAmelCase			(    A				,							A=None				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__						={
       # fully supervised kinetics-400 checkpoints
       'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
       'xclip-base-patch32-16-frames': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
       ),
       'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
       'xclip-base-patch16-16-frames': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
       ),
       'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
       'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
       # fully supervised kinetics-600 checkpoints
       'xclip-base-patch16-kinetics-600': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
       ),
       'xclip-base-patch16-kinetics-600-16-frames': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
       ),
       'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
       # few shot
       'xclip-base-patch16-hmdb-2-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
       ),
       'xclip-base-patch16-hmdb-4-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
       ),
       'xclip-base-patch16-hmdb-8-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
       ),
       'xclip-base-patch16-hmdb-16-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
       ),
       'xclip-base-patch16-ucf-2-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
       ),
       'xclip-base-patch16-ucf-4-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
       ),
       'xclip-base-patch16-ucf-8-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
       ),
       'xclip-base-patch16-ucf-16-shot': (
           'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
       ),
       # zero shot
       'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
   }

   UpperCAmelCase__						=model_to_url[model_name]
   UpperCAmelCase__						=8
   if "16-frames" in model_name:
      UpperCAmelCase__						=16
   elif "shot" in model_name:
      UpperCAmelCase__						=32

   UpperCAmelCase__						=get_xclip_config(__snake_case				,							__snake_case     )
   UpperCAmelCase__						=XCLIPModel(__snake_case     )
   model.eval()

   if "drive" in checkpoint_url:
      UpperCAmelCase__						='pytorch_model.bin'
      gdown.cached_download(__snake_case				,							__snake_case				,							quiet=__snake_case     )
      UpperCAmelCase__						=torch.load(__snake_case				,							map_location="cpu"     )['model']
   else:
      UpperCAmelCase__						=torch.hub.load_state_dict_from_url(__snake_case     )['model']

   UpperCAmelCase__						=convert_state_dict(__snake_case				,							__snake_case     )

   UpperCAmelCase__						=XCLIPModel(__snake_case     )
   UpperCAmelCase__						=model.load_state_dict(__snake_case				,							strict=__snake_case     )
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   UpperCAmelCase__						=336 if model_name == 'xclip-large-patch14-16-frames' else 224
   UpperCAmelCase__						=VideoMAEImageProcessor(size=__snake_case     )
   UpperCAmelCase__						=CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"     )
   UpperCAmelCase__						=CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"     )
   UpperCAmelCase__						=XCLIPProcessor(image_processor=__snake_case				,							tokenizer=__snake_case     )

   UpperCAmelCase__						=prepare_video(__snake_case     )
   UpperCAmelCase__						=processor(
       text=["playing sports", "eating spaghetti", "go shopping"]				,							videos=__snake_case				,							return_tensors="pt"				,							padding=__snake_case     )

   print("Shape of pixel values:"				,							inputs.pixel_values.shape     )

   with torch.no_grad():
      UpperCAmelCase__						=model(**__snake_case     )

   # Verify outputs
   UpperCAmelCase__						=outputs.logits_per_video
   UpperCAmelCase__						=logits_per_video.softmax(dim=1     )
   print("Probs:"				,							__snake_case     )
   # kinetics-400
   if model_name == "xclip-base-patch32":
      UpperCAmelCase__						=torch.tensor([[0.00_19, 0.99_51, 0.00_30]]     )
   elif model_name == "xclip-base-patch32-16-frames":
      UpperCAmelCase__						=torch.tensor([[7.0_9_9_9e-0_4, 9.9_8_8_3e-0_1, 4.5_5_8_0e-0_4]]     )
   elif model_name == "xclip-base-patch16":
      UpperCAmelCase__						=torch.tensor([[0.00_83, 0.96_81, 0.02_36]]     )
   elif model_name == "xclip-base-patch16-16-frames":
      UpperCAmelCase__						=torch.tensor([[7.6_9_3_7e-0_4, 9.9_7_2_8e-0_1, 1.9_4_7_3e-0_3]]     )
   elif model_name == "xclip-large-patch14":
      UpperCAmelCase__						=torch.tensor([[0.00_62, 0.98_64, 0.00_75]]     )
   elif model_name == "xclip-large-patch14-16-frames":
      UpperCAmelCase__						=torch.tensor([[3.3_8_7_7e-0_4, 9.9_9_3_7e-0_1, 2.8_8_8_8e-0_4]]     )
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      UpperCAmelCase__						=torch.tensor([[0.05_55, 0.89_14, 0.05_31]]     )
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      UpperCAmelCase__						=torch.tensor([[3.8_5_5_4e-0_4, 9.9_9_2_9e-0_1, 3.2_7_5_4e-0_4]]     )
   elif model_name == "xclip-large-patch14-kinetics-600":
      UpperCAmelCase__						=torch.tensor([[0.00_36, 0.99_20, 0.00_45]]     )
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      UpperCAmelCase__						=torch.tensor([[7.1_8_9_0e-0_6, 9.9_9_9_4e-0_1, 5.6_5_5_9e-0_5]]     )
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      UpperCAmelCase__						=torch.tensor([[1.0_3_2_0e-0_5, 9.9_9_9_3e-0_1, 6.2_4_3_5e-0_5]]     )
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      UpperCAmelCase__						=torch.tensor([[4.1_3_7_7e-0_6, 9.9_9_9_0e-0_1, 9.8_3_8_6e-0_5]]     )
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      UpperCAmelCase__						=torch.tensor([[4.1_3_4_7e-0_5, 9.9_9_6_2e-0_1, 3.3_4_1_1e-0_4]]     )
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      UpperCAmelCase__						=torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]     )
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      UpperCAmelCase__						=torch.tensor([[8.5_8_5_7e-0_5, 9.9_9_2_8e-0_1, 6.3_2_9_1e-0_4]]     )
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      UpperCAmelCase__						=torch.tensor([[0.00_27, 0.99_04, 0.00_70]]     )
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      UpperCAmelCase__						=torch.tensor([[9.8_2_1_9e-0_4, 9.9_5_9_3e-0_1, 3.0_8_6_3e-0_3]]     )
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      UpperCAmelCase__						=torch.tensor([[3.5_0_8_2e-0_4, 9.9_7_8_5e-0_1, 1.7_9_6_6e-0_3]]     )
   else:
      raise ValueError(F"""Model name {model_name} not supported"""     )
   assert torch.allclose(__snake_case				,							__snake_case				,							atol=1e-3     )
   print("Looks ok!"     )

   if pytorch_dump_folder_path is not None:
      print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""     )
      model.save_pretrained(__snake_case     )

   if push_to_hub:
      print("Pushing model, processor and slow tokenizer files to the hub..."     )
      model.push_to_hub(__snake_case				,							organization="nielsr"     )
      processor.push_to_hub(__snake_case				,							organization="nielsr"     )
      slow_tokenizer.push_to_hub(__snake_case				,							organization="nielsr"     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='xclip-base-patch32',
        type=str,
        help='Name of the model.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    UpperCamelCase_												=	parser.parse_args()
    convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''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 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=sys.maxsize):

          '''simple docstring'''






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


     def __call__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






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

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

          return img_augs




class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






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

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


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

          '''simple docstring'''






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

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






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					str     )	->	Dict:
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes







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