





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				)








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(""   )

