






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import OwlViTImageProcessor





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=18							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.4814_5466, 0.457_8275, 0.4082_1073]							, lowercase__=[0.2686_2954, 0.2613_0258, 0.2757_7711]							, lowercase__=True							, )				->				int:
				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							image_size
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size if size is not None else {"height": 18, "width": 18}
				__A    =							do_center_crop
				__A    =							crop_size if crop_size is not None else {"height": 18, "width": 18}
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_convert_rgb






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_convert_rgb": self.do_convert_rgb,
				}





@require_torch
@require_vision
class        a__      (	a_	,       unittest.TestCase		):
		UpperCAmelCase_							:       Tuple 					= OwlViTImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							OwlViTImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				List[str]:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(_lowerCamelCase							, "do_resize"						)						)
				self.assertTrue(hasattr(_lowerCamelCase							, "size"						)						)
				self.assertTrue(hasattr(_lowerCamelCase							, "do_center_crop"						)						)
				self.assertTrue(hasattr(_lowerCamelCase							, "center_crop"						)						)
				self.assertTrue(hasattr(_lowerCamelCase							, "do_normalize"						)						)
				self.assertTrue(hasattr(_lowerCamelCase							, "image_mean"						)						)
				self.assertTrue(hasattr(_lowerCamelCase							, "image_std"						)						)
				self.assertTrue(hasattr(_lowerCamelCase							, "do_convert_rgb"						)						)






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size							, {"height": 18, "width": 18}						)
				self.assertEqual(image_processor.crop_size							, {"height": 18, "width": 18}						)

				__A    =							self.image_processing_class.from_dict(self.image_processor_dict							, size=42							, crop_size=84						)
				self.assertEqual(image_processor.size							, {"height": 42, "width": 42}						)
				self.assertEqual(image_processor.crop_size							, {"height": 84, "width": 84}						)






		def 					_lowerCamelCase					(      self						)				->				int:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=_lowerCamelCase						)

				for image in image_inputs:
						self.assertIsInstance(_lowerCamelCase							, Image.Image						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(_lowerCamelCase							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=_lowerCamelCase							, numpify=_lowerCamelCase						)
				for image in image_inputs:
						self.assertIsInstance(_lowerCamelCase							, np.ndarray						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(_lowerCamelCase							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Any:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=_lowerCamelCase							, torchify=_lowerCamelCase						)
				for image in image_inputs:
						self.assertIsInstance(_lowerCamelCase							, torch.Tensor						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(_lowerCamelCase							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )							, )









"""simple docstring"""



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 			a	(  a_      ):







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase			):
      lowercase				       =			parser.add_parser('login'				,						help='Log in using the same credentials as on huggingface.co'			)
      login_parser.set_defaults(func=lambda _lowerCamelCase			: LoginCommand(_lowerCamelCase			)			)
      lowercase				       =			parser.add_parser('whoami'				,						help='Find out which huggingface.co account you are logged in as.'			)
      whoami_parser.set_defaults(func=lambda _lowerCamelCase			: WhoamiCommand(_lowerCamelCase			)			)
      lowercase				       =			parser.add_parser('logout'				,						help='Log out'			)
      logout_parser.set_defaults(func=lambda _lowerCamelCase			: LogoutCommand(_lowerCamelCase			)			)

      # new system: git-based repo system
      lowercase				       =			parser.add_parser(
          'repo'				,						help='Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'				,						)
      lowercase				       =			repo_parser.add_subparsers(
          help='Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands'			)
      lowercase				       =			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=_lowerCamelCase				,						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=_lowerCamelCase				,						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 _lowerCamelCase			: RepoCreateCommand(_lowerCamelCase			)			)


class 			a	:
    UpperCAmelCase_					:	Union[str, Any]      	="\u001b[1m"
    UpperCAmelCase_					:	Optional[Any]      	="\u001b[31m"
    UpperCAmelCase_					:	List[str]      	="\u001b[90m"
    UpperCAmelCase_					:	str      	="\u001b[0m"







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase			):
      return F'{cls._bold}{s}{cls._reset}'







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase			):
      return F'{cls._bold}{cls._red}{s}{cls._reset}'







    @classmethod
    def      UpperCamelCase_ (     cls				,						_lowerCamelCase			):
      return F'{cls._gray}{s}{cls._reset}'


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[List[Union[str, int]]]						,   __snake_case     :    List[str]       ):





  '''simple docstring'''





  lowercase				       =			[max(len(str(__snake_case       )       ) for x in col       ) for col in zip(*__snake_case						,   __snake_case       )]
  lowercase				       =			('{{:{}}} ' * len(__snake_case       )).format(*__snake_case       )
  lowercase				       =			[]
  lines.append(row_format.format(*__snake_case       )       )
  lines.append(row_format.format(*['-' * w for w in col_widths]       )       )
  for row in rows:
    lines.append(row_format.format(*__snake_case       )       )
  return "\n".join(__snake_case       )

class 			a	:







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			args


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      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 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      print(
          ANSI.red(
              'WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '
              '`huggingface-cli whoami` instead.'			)			)
      lowercase				       =			HfFolder.get_token()
      if token is None:
        print('Not logged in'			)
        exit()
      try:
        lowercase       ,	lowercase				       =			whoami(_lowerCamelCase			)
        print(_lowerCamelCase			)
        if orgs:
          print(ANSI.bold('orgs: '			)				,						','.join(_lowerCamelCase			)			)
      except HTTPError as e:
        print(_lowerCamelCase			)
        print(ANSI.red(e.response.text			)			)
        exit(1			)


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      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 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      print(
          ANSI.red(
              'WARNING! Managing repositories through transformers-cli is deprecated. '
              'Please use `huggingface-cli` instead.'			)			)
      lowercase				       =			HfFolder.get_token()
      if token is None:
        print('Not logged in'			)
        exit(1			)
      try:
        lowercase				       =			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:
        lowercase				       =			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(''			)

      lowercase       ,	lowercase				       =			whoami(_lowerCamelCase			)
      lowercase				       =			self.args.organization if self.args.organization is not None else user
      lowercase				       =			F'{namespace}/{self.args.name}'
      print(F'You are about to create {ANSI.bold(_lowerCamelCase			)}'			)

      if not self.args.yes:
        lowercase				       =			input('Proceed? [Y/n] '			).lower()
        if not (choice == "" or choice == "y" or choice == "yes"):
          print('Abort'			)
          exit()
      try:
        lowercase				       =			create_repo(_lowerCamelCase				,						name=self.args.name				,						organization=self.args.organization			)
      except HTTPError as e:
        print(_lowerCamelCase			)
        print(ANSI.red(e.response.text			)			)
        exit(1			)
      print('\nYour repo now lives at:'			)
      print(F'  {ANSI.bold(_lowerCamelCase			)}'			)
      print('\nYou can clone it locally with the command below, and commit/push as usual.'			)
      print(F'\n  git clone {url}'			)
      print(''			)


