code: 
  
  
  
  
  
  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 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase=sys.maxsize   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[str]        =       "bilinear"
          __snake_case   :				Tuple        =       max_size
          __snake_case   :				Dict        =       short_edge_length
  
  
  
  
  
  
  
        def __call__( self				,						UpperCAmelCase   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       []
          for img in imgs:
            __snake_case   :				Tuple        =       img.shape[:2]
            # later: provide list and randomly choose index for resize
            __snake_case   :				List[Any]        =       np.random.randint(self.short_edge_length[0]				,						self.short_edge_length[1] + 1   )
            if size == 0:
              return img
            __snake_case   :				Any        =       size * 1.0 / min(UpperCAmelCase				,						UpperCAmelCase   )
            if h < w:
              __snake_case   :				Optional[int]        =       size, scale * w
            else:
              __snake_case   :				str        =       scale * h, size
            if max(UpperCAmelCase				,						UpperCAmelCase   ) > self.max_size:
              __snake_case   :				Union[str, Any]        =       self.max_size * 1.0 / max(UpperCAmelCase				,						UpperCAmelCase   )
              __snake_case   :				Optional[Any]        =       newh * scale
              __snake_case   :				List[Any]        =       neww * scale
            __snake_case   :				List[Any]        =       int(neww + 0.5   )
            __snake_case   :				Optional[Any]        =       int(newh + 0.5   )
  
            if img.dtype == np.uinta:
              __snake_case   :				List[str]        =       Image.fromarray(UpperCAmelCase   )
              __snake_case   :				int        =       pil_image.resize((neww, newh)				,						PILImageResampling.BILINEAR   )
              __snake_case   :				List[Any]        =       np.asarray(UpperCAmelCase   )
            else:
              __snake_case   :				Union[str, Any]        =       img.permute(2				,						0				,						1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
              __snake_case   :				str        =       nn.functional.interpolate(
                  UpperCAmelCase				,						(newh, neww)				,						mode=self.interp_method				,						align_corners=UpperCAmelCase   ).squeeze(0   )
            img_augs.append(UpperCAmelCase   )
  
          return img_augs
  
  
  
  
  class 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				str        =       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,						cfg.INPUT.MAX_SIZE_TEST   )
          __snake_case   :				Dict        =       cfg.INPUT.FORMAT
          __snake_case   :				Union[str, Any]        =       cfg.SIZE_DIVISIBILITY
          __snake_case   :				Dict        =       cfg.PAD_VALUE
          __snake_case   :				Optional[Any]        =       cfg.INPUT.MAX_SIZE_TEST
          __snake_case   :				List[str]        =       cfg.MODEL.DEVICE
          __snake_case   :				Dict        =       torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,						1				,						1   )
          __snake_case   :				Optional[int]        =       torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,						1				,						1   )
          __snake_case   :				List[Any]        =       lambda UpperCAmelCase   : (x - self.pixel_mean) / self.pixel_std
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				str        =       tuple(max(UpperCAmelCase   ) for s in zip(*[img.shape for img in images]   )   )
          __snake_case   :				Optional[Any]        =       [im.shape[-2:] for im in images]
          __snake_case   :				Tuple        =       [
              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   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          with torch.no_grad():
            if not isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
              __snake_case   :				Tuple        =       [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
            __snake_case   :				Union[str, Any]        =       torch.tensor([im.shape[:2] for im in images]   )
            __snake_case   :				Tuple        =       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
            __snake_case   :				Dict        =       [self.normalizer(UpperCAmelCase   ) for x in images]
            # now pad them to do the following operations
            __snake_case   :				str        =       self.pad(UpperCAmelCase   )
            # Normalize
  
            if self.size_divisibility > 0:
              raise NotImplementedError()
            # pad
            __snake_case   :				Tuple        =       torch.true_divide(UpperCAmelCase				,						UpperCAmelCase   )
            if single_image:
              return images[0], sizes[0], scales_yx[0]
            else:
              return images, sizes, scales_yx
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    List[str]   ,     lowercase       :    Tuple				) ->    Optional[Any]:
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Union[str, Any]   ,     lowercase       :    Tuple[int, int]				) ->    Optional[Any]:
    assert torch.isfinite(lowercase				).all(), "Box tensor contains infinite or NaN!"
    __snake_case   :				Optional[Any]        =       box_size
    tensor[:, 0].clamp_(min=0   ,     max=lowercase				)
    tensor[:, 1].clamp_(min=0   ,     max=lowercase				)
    tensor[:, 2].clamp_(min=0   ,     max=lowercase				)
    tensor[:, 3].clamp_(min=0   ,     max=lowercase				)
  
  
style_context: 
  
  
  
  
  
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       parser.add_parser("login"				,						help="Log in using the same credentials as on huggingface.co"   )
          login_parser.set_defaults(func=lambda UpperCAmelCase   : LoginCommand(UpperCAmelCase   )   )
          __snake_case   :				int        =       parser.add_parser("whoami"				,						help="Find out which huggingface.co account you are logged in as."   )
          whoami_parser.set_defaults(func=lambda UpperCAmelCase   : WhoamiCommand(UpperCAmelCase   )   )
          __snake_case   :				int        =       parser.add_parser("logout"				,						help="Log out"   )
          logout_parser.set_defaults(func=lambda UpperCAmelCase   : LogoutCommand(UpperCAmelCase   )   )
  
          # new system: git-based repo system
          __snake_case   :				Union[str, Any]        =       parser.add_parser(
              "repo"				,						help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."				,						)
          __snake_case   :				List[str]        =       repo_parser.add_subparsers(
              help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"   )
          __snake_case   :				int        =       repo_subparsers.add_parser(
              "create"				,						help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"   )
          repo_create_parser.add_argument(
              "name"				,						type=UpperCAmelCase				,						help="Name for your model's repo. Will be namespaced under your username to build the model id."				,						)
          repo_create_parser.add_argument("--organization"				,						type=UpperCAmelCase				,						help="Optional: organization namespace."   )
          repo_create_parser.add_argument("-y"				,						"--yes"				,						action="store_true"				,						help="Optional: answer Yes to the prompt"   )
          repo_create_parser.set_defaults(func=lambda UpperCAmelCase   : RepoCreateCommand(UpperCAmelCase   )   )
  
  
  
  
  class 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      Tuple       ="\u001b[1m"
        UpperCAmelCase_						:      int       ="\u001b[31m"
        UpperCAmelCase_						:      Optional[Any]       ="\u001b[90m"
        UpperCAmelCase_						:      Optional[Any]       ="\u001b[0m"
  
  
  
  
  
  
  
        @classmethod
        def 						UpperCAmelCase		( cls				,						UpperCAmelCase   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return F"""{cls._bold}{s}{cls._reset}"""
  
  
  
  
  
  
  
        @classmethod
        def 						UpperCAmelCase		( cls				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return F"""{cls._bold}{cls._red}{s}{cls._reset}"""
  
  
  
  
  
  
  
        @classmethod
        def 						UpperCAmelCase		( cls				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return F"""{cls._gray}{s}{cls._reset}"""
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    List[List[Union[str, int]]]   ,     lowercase       :    List[str]				) ->    str:
    __snake_case   :				Dict        =       [max(len(str(lowercase				)				) for x in col				) for col in zip(*lowercase   ,     lowercase				)]
    __snake_case   :				str        =       ("{{:{}}} " * len(lowercase				)).format(*lowercase				)
    __snake_case   :				Tuple        =       []
    lines.append(row_format.format(*lowercase				)				)
    lines.append(row_format.format(*["-" * w for w in col_widths]				)				)
    for row in rows:
      lines.append(row_format.format(*lowercase				)				)
    return "\n".join(lowercase				)
  
  
  
  
  class 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[str]        =       args
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          print(
              ANSI.red(
                  "ERROR! `huggingface-cli login` uses an outdated login mechanism "
                  "that is not compatible with the Hugging Face Hub backend anymore. "
                  "Please use `huggingface-cli login instead."   )   )
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          print(
              ANSI.red(
                  "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
                  "`huggingface-cli whoami` instead."   )   )
          __snake_case   :				Optional[Any]        =       HfFolder.get_token()
          if token is None:
            print("Not logged in"   )
            exit()
          try:
            __snake_case    ,   __snake_case   :				Tuple        =       whoami(UpperCAmelCase   )
            print(UpperCAmelCase   )
            if orgs:
              print(ANSI.bold("orgs: "   )				,						",".join(UpperCAmelCase   )   )
          except HTTPError as e:
            print(UpperCAmelCase   )
            print(ANSI.red(e.response.text   )   )
            exit(1   )
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          print(
              ANSI.red(
                  "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
                  "that is not compatible with the Hugging Face Hub backend anymore. "
                  "Please use `huggingface-cli logout instead."   )   )
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          print(
              ANSI.red(
                  "WARNING! Managing repositories through transformers-cli is deprecated. "
                  "Please use `huggingface-cli` instead."   )   )
          __snake_case   :				Dict        =       HfFolder.get_token()
          if token is None:
            print("Not logged in"   )
            exit(1   )
          try:
            __snake_case   :				Optional[int]        =       subprocess.check_output(["git", "--version"]   ).decode("utf-8"   )
            print(ANSI.gray(stdout.strip()   )   )
          except FileNotFoundError:
            print("Looks like you do not have git installed, please install."   )
  
          try:
            __snake_case   :				Union[str, Any]        =       subprocess.check_output(["git-lfs", "--version"]   ).decode("utf-8"   )
            print(ANSI.gray(stdout.strip()   )   )
          except FileNotFoundError:
            print(
                ANSI.red(
                    "Looks like you do not have git-lfs installed, please install."
                    " You can install from https://git-lfs.github.com/."
                    " Then run `git lfs install` (you only have to do this once)."   )   )
          print(""   )
  
          __snake_case    ,   __snake_case   :				Any        =       whoami(UpperCAmelCase   )
          __snake_case   :				List[str]        =       self.args.organization if self.args.organization is not None else user
          __snake_case   :				Any        =       F"""{namespace}/{self.args.name}"""
          print(F"""You are about to create {ANSI.bold(UpperCAmelCase   )}"""   )
  
          if not self.args.yes:
            __snake_case   :				Optional[int]        =       input("Proceed? [Y/n] "   ).lower()
            if not (choice == "" or choice == "y" or choice == "yes"):
              print("Abort"   )
              exit()
          try:
            __snake_case   :				List[str]        =       create_repo(UpperCAmelCase				,						name=self.args.name				,						organization=self.args.organization   )
          except HTTPError as e:
            print(UpperCAmelCase   )
            print(ANSI.red(e.response.text   )   )
            exit(1   )
          print("\nYour repo now lives at:"   )
          print(F"""  {ANSI.bold(UpperCAmelCase   )}"""   )
          print("\nYou can clone it locally with the command below, and commit/push as usual."   )
          print(F"""\n  git clone {url}"""   )
          print(""   )
  
  