






"""simple docstring"""



import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor

class 			a	(  unittest.TestCase      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=7				,						_lowerCamelCase=3				,						_lowerCamelCase=3_0				,						_lowerCamelCase=4_0_0				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=[0.5, 0.5, 0.5]				,						_lowerCamelCase=[0.5, 0.5, 0.5]				,						_lowerCamelCase=False				,						):
      lowercase				       =			size if size is not None else {'height': 3_0, 'width': 3_0}
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			num_channels
      lowercase				       =			min_resolution
      lowercase				       =			max_resolution
      lowercase				       =			do_resize
      lowercase				       =			size
      lowercase				       =			do_normalize
      lowercase				       =			image_mean
      lowercase				       =			image_std
      lowercase				       =			do_reduce_labels







    def      UpperCamelCase_ (     self			):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			load_dataset('hf-internal-testing/fixtures_ade20k'						,   split='test'       )

  lowercase				       =			Image.open(dataset[0]['file']       )
  lowercase				       =			Image.open(dataset[1]['file']       )

  return image, map
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			load_dataset('hf-internal-testing/fixtures_ade20k'						,   split='test'       )

  lowercase				       =			Image.open(dataset[0]['file']       )
  lowercase				       =			Image.open(dataset[1]['file']       )
  lowercase				       =			Image.open(dataset[2]['file']       )
  lowercase				       =			Image.open(dataset[3]['file']       )

  return [imagea, imagea], [mapa, mapa]



@require_torch
@require_vision
class 			a	(  a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	List[str]      	=SegformerImageProcessor if is_vision_available() else None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			SegformerImageProcessingTester(self			)







    @property
    def      UpperCamelCase_ (     self			):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_resize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'size'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_normalize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_mean'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_std'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_reduce_labels'			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size				,						{'height': 3_0, 'width': 3_0}			)
      self.assertEqual(image_processor.do_reduce_labels				,						_lowerCamelCase			)

      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict				,						size=4_2				,						reduce_labels=_lowerCamelCase			)
      self.assertEqual(image_processor.size				,						{'height': 4_2, 'width': 4_2}			)
      self.assertEqual(image_processor.do_reduce_labels				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase			)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						Image.Image			)

      # Test not batched input
      lowercase				       =			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.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)

      # Test batched
      lowercase				       =			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.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      lowercase				       =			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
      lowercase				       =			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.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)

      # Test batched
      lowercase				       =			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.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      lowercase				       =			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
      lowercase				       =			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.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)

      # Test batched
      lowercase				       =			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.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase				,						torchify=_lowerCamelCase			)
      lowercase				       =			[]
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						torch.Tensor			)
        maps.append(torch.zeros(image.shape[-2:]			).long()			)

      # Test not batched input
      lowercase				       =			image_processing(image_inputs[0]				,						maps[0]				,						return_tensors='pt'			)
      self.assertEqual(
          encoding['pixel_values'].shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(
          encoding['labels'].shape				,						(
              1,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(encoding['labels'].dtype				,						torch.long			)
      self.assertTrue(encoding['labels'].min().item() >= 0			)
      self.assertTrue(encoding['labels'].max().item() <= 2_5_5			)

      # Test batched
      lowercase				       =			image_processing(_lowerCamelCase				,						_lowerCamelCase				,						return_tensors='pt'			)
      self.assertEqual(
          encoding['pixel_values'].shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(
          encoding['labels'].shape				,						(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(encoding['labels'].dtype				,						torch.long			)
      self.assertTrue(encoding['labels'].min().item() >= 0			)
      self.assertTrue(encoding['labels'].max().item() <= 2_5_5			)

      # Test not batched input (PIL images)
      lowercase       ,	lowercase				       =			prepare_semantic_single_inputs()

      lowercase				       =			image_processing(_lowerCamelCase				,						_lowerCamelCase				,						return_tensors='pt'			)
      self.assertEqual(
          encoding['pixel_values'].shape				,						(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(
          encoding['labels'].shape				,						(
              1,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(encoding['labels'].dtype				,						torch.long			)
      self.assertTrue(encoding['labels'].min().item() >= 0			)
      self.assertTrue(encoding['labels'].max().item() <= 2_5_5			)

      # Test batched input (PIL images)
      lowercase       ,	lowercase				       =			prepare_semantic_batch_inputs()

      lowercase				       =			image_processing(_lowerCamelCase				,						_lowerCamelCase				,						return_tensors='pt'			)
      self.assertEqual(
          encoding['pixel_values'].shape				,						(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(
          encoding['labels'].shape				,						(
              2,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )				,						)
      self.assertEqual(encoding['labels'].dtype				,						torch.long			)
      self.assertTrue(encoding['labels'].min().item() >= 0			)
      self.assertTrue(encoding['labels'].max().item() <= 2_5_5			)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      lowercase       ,	lowercase				       =			prepare_semantic_single_inputs()
      lowercase				       =			image_processing(_lowerCamelCase				,						_lowerCamelCase				,						return_tensors='pt'			)
      self.assertTrue(encoding['labels'].min().item() >= 0			)
      self.assertTrue(encoding['labels'].max().item() <= 1_5_0			)

      lowercase				       =			True
      lowercase				       =			image_processing(_lowerCamelCase				,						_lowerCamelCase				,						return_tensors='pt'			)
      self.assertTrue(encoding['labels'].min().item() >= 0			)
      self.assertTrue(encoding['labels'].max().item() <= 2_5_5			)










"""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(''			)


