




'''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      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





					@staticmethod
					def 						__snake_case	(  _lowercase    :	ArgumentParser)						->       Optional[int]:
								A_       		=   parser.add_parser('login'			,					help='Log in using the same credentials as on huggingface.co')
								login_parser.set_defaults(func=lambda _lowercase: LoginCommand(_lowercase))
								A_       		=   parser.add_parser('whoami'			,					help='Find out which huggingface.co account you are logged in as.')
								whoami_parser.set_defaults(func=lambda _lowercase: WhoamiCommand(_lowercase))
								A_       		=   parser.add_parser('logout'			,					help='Log out')
								logout_parser.set_defaults(func=lambda _lowercase: LogoutCommand(_lowercase))

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





class      __UpperCAmelCase							:



					'''simple docstring'''





					_UpperCamelCase  	=		"""\u001b[1m"""
					_UpperCamelCase  	=		"""\u001b[31m"""
					_UpperCamelCase  	=		"""\u001b[90m"""
					_UpperCamelCase  	=		"""\u001b[0m"""



					@classmethod
					def 						__snake_case	(  cls    :	Union[str, Any]			,					_lowercase    :	Union[str, Any])						->       Tuple:
								return F'{cls._bold}{s}{cls._reset}'



					@classmethod
					def 						__snake_case	(  cls    :	Tuple			,					_lowercase    :	Any)						->       Tuple:
								return F'{cls._bold}{cls._red}{s}{cls._reset}'



					@classmethod
					def 						__snake_case	(  cls    :	List[Any]			,					_lowercase    :	int)						->       int:
								return F'{cls._gray}{s}{cls._reset}'





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		str:
			A_       		=   [max(len(str(SCREAMING_SNAKE_CASE_    )    ) for x in col    ) for col in zip(*SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )]
			A_       		=   ('{{:{}}} ' * len(SCREAMING_SNAKE_CASE_    )).format(*SCREAMING_SNAKE_CASE_    )
			A_       		=   []
			lines.append(row_format.format(*SCREAMING_SNAKE_CASE_    )    )
			lines.append(row_format.format(*['-' * w for w in col_widths]    )    )
			for row in rows:
						lines.append(row_format.format(*SCREAMING_SNAKE_CASE_    )    )
			return "\n".join(SCREAMING_SNAKE_CASE_    )
class      __UpperCAmelCase							:



					'''simple docstring'''





					def __init__(  self    :	int			,					_lowercase    :	int)						->       Dict:
								A_       		=   args





class      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





					def 						__snake_case	(  self    :	int)						->       Dict:
								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      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





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





class      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





					def 						__snake_case	(  self    :	Optional[Any])						->       Dict:
								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      __UpperCAmelCase							(    lowerCAmelCase							):



					'''simple docstring'''





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

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

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










'''simple docstring'''
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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import CLIPImageProcessor
class      __UpperCAmelCase							(    unittest.TestCase							):



					'''simple docstring'''





					def __init__(  self    :	Dict			,					_lowercase    :	List[Any]			,					_lowercase    :	List[Any]=7			,					_lowercase    :	Dict=3			,					_lowercase    :	List[Any]=18			,					_lowercase    :	List[str]=30			,					_lowercase    :	List[str]=400			,					_lowercase    :	Any=True			,					_lowercase    :	Any=None			,					_lowercase    :	Any=True			,					_lowercase    :	Dict=None			,					_lowercase    :	Optional[int]=True			,					_lowercase    :	int=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,					_lowercase    :	Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,					_lowercase    :	Optional[Any]=True			,					)						->       List[str]:
								A_       		=   size if size is not None else {'shortest_edge': 20}
								A_       		=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
								A_       		=   parent
								A_       		=   batch_size
								A_       		=   num_channels
								A_       		=   image_size
								A_       		=   min_resolution
								A_       		=   max_resolution
								A_       		=   do_resize
								A_       		=   size
								A_       		=   do_center_crop
								A_       		=   crop_size
								A_       		=   do_normalize
								A_       		=   image_mean
								A_       		=   image_std
								A_       		=   do_convert_rgb



					def 						__snake_case	(  self    :	Optional[Any])						->       List[Any]:
								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,
								}



					def 						__snake_case	(  self    :	Dict			,					_lowercase    :	Tuple=False			,					_lowercase    :	Tuple=False			,					_lowercase    :	Union[str, Any]=False)						->       List[Any]:
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
											A_       		=   []
											for i in range(self.batch_size):
														image_inputs.append(
														    np.random.randint(
														        255			,					size=(self.num_channels, self.max_resolution, self.max_resolution)			,					dtype=np.uinta))
								else:
											A_       		=   []
											for i in range(self.batch_size):
														A_		,   A_       		=   np.random.choice(np.arange(self.min_resolution			,					self.max_resolution)			,					2)
														image_inputs.append(np.random.randint(255			,					size=(self.num_channels, width, height)			,					dtype=np.uinta))

								if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											A_       		=   [Image.fromarray(np.moveaxis(_lowercase			,					0			,					-1)) for x in image_inputs]

								if torchify:
											A_       		=   [torch.from_numpy(_lowercase) for x in image_inputs]

								return image_inputs





@require_torch
@require_vision
class      __UpperCAmelCase							(    lowerCAmelCase ,unittest.TestCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	Optional[Any])						->       List[str]:
								A_       		=   CLIPImageProcessingTester(self)



					@property
					def 						__snake_case	(  self    :	Dict)						->       Optional[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						__snake_case	(  self    :	Optional[Any])						->       str:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Union[str, Any])						->       Optional[int]:
								A_       		=   self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size			,					{'shortest_edge': 20})
								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			,					{'shortest_edge': 42})
								self.assertEqual(image_processor.crop_size			,					{'height': 84, 'width': 84})



					def 						__snake_case	(  self    :	str)						->       List[str]:
								pass



					def 						__snake_case	(  self    :	List[str])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					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(_lowercase			,					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 						__snake_case	(  self    :	Union[str, Any])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					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(_lowercase			,					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 						__snake_case	(  self    :	Optional[Any])						->       Tuple:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					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(_lowercase			,					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'],
								    )			,					)





@require_torch
@require_vision
class      __UpperCAmelCase							(    lowerCAmelCase ,unittest.TestCase							):



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	int)						->       List[Any]:
								A_       		=   CLIPImageProcessingTester(self			,					num_channels=4)
								A_       		=   3



					@property
					def 						__snake_case	(  self    :	str)						->       Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						__snake_case	(  self    :	Any)						->       Dict:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Any)						->       Optional[int]:
								pass



					def 						__snake_case	(  self    :	str)						->       List[Any]:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					Image.Image)

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

								# Test batched
								A_       		=   image_processing(_lowercase			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)




