



"""simple docstring"""






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				['image_processor', 'tokenizer']
					_a       =				'LayoutLMv2ImageProcessor'
					_a       =				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    Any=None,    lowerCamelCase				:    int=None,    **lowerCamelCase				:    Any	)->   Optional[int]:
								lowerCamelCase__						:						List[str]						      =None
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.''',    lowerCamelCase,    )
											lowerCamelCase__						:						Dict						      =kwargs.pop('''feature_extractor'''	)

								lowerCamelCase__						:						Tuple						      =image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''	)
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''	)

								super().__init__(lowerCamelCase,    lowerCamelCase	)



					def __call__(			self				:    Union[str, Any],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,    lowerCamelCase				:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,    lowerCamelCase				:    Union[List[List[int]], List[List[List[int]]]] = None,    lowerCamelCase				:    Optional[Union[List[int], List[List[int]]]] = None,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Union[bool, str, PaddingStrategy] = False,    lowerCamelCase				:    Union[bool, str, TruncationStrategy] = False,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    int = 0,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Optional[Union[str, TensorType]] = None,    **lowerCamelCase				:    Optional[Any],    )->   BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''	)

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''	)

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''	)

								# first, apply the image processor
								lowerCamelCase__						:						Dict						      =self.image_processor(images=lowerCamelCase,    return_tensors=lowerCamelCase	)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(lowerCamelCase,    lowerCamelCase	):
														lowerCamelCase__						:						List[str]						      =[text]  # add batch dimension (as the image processor always adds a batch dimension)
											lowerCamelCase__						:						int						      =features['''words''']

								lowerCamelCase__						:						Tuple						      =self.tokenizer(
								    text=text if text is not None else features['''words'''],    text_pair=text_pair if text_pair is not None else None,    boxes=boxes if boxes is not None else features['''boxes'''],    word_labels=lowerCamelCase,    add_special_tokens=lowerCamelCase,    padding=lowerCamelCase,    truncation=lowerCamelCase,    max_length=lowerCamelCase,    stride=lowerCamelCase,    pad_to_multiple_of=lowerCamelCase,    return_token_type_ids=lowerCamelCase,    return_attention_mask=lowerCamelCase,    return_overflowing_tokens=lowerCamelCase,    return_special_tokens_mask=lowerCamelCase,    return_offsets_mapping=lowerCamelCase,    return_length=lowerCamelCase,    verbose=lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase,    )

								# add pixel values
								lowerCamelCase__						:						List[Any]						      =features.pop('''pixel_values'''	)
								if return_overflowing_tokens is True:
											lowerCamelCase__						:						Union[str, Any]						      =self.get_overflowing_images(lowerCamelCase,    encoded_inputs['''overflow_to_sample_mapping''']	)
								lowerCamelCase__						:						Optional[int]						      =images

								return encoded_inputs



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Dict	)->   Optional[int]:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								lowerCamelCase__						:						Optional[int]						      =[]
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]	)

								if len(lowerCamelCase	) != len(lowerCamelCase	):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F''' {len(lowerCamelCase	)} and {len(lowerCamelCase	)}'''	)

								return images_with_overflow



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    List[str],    **lowerCamelCase				:    str	)->   List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    Dict,    **lowerCamelCase				:    Tuple	)->   Any:
								return self.tokenizer.decode(*lowerCamelCase,    **lowerCamelCase	)



					@property
					def 						snake_case			(			self				:    List[str]	)->   Tuple:
								return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



					@property
					def 						snake_case			(			self				:    List[Any]	)->   List[str]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',    lowerCamelCase,    )
								return self.image_processor_class




					@property
					def 						snake_case			(			self				:    Optional[Any]	)->   Optional[Any]:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',    lowerCamelCase,    )
								return self.image_processor












"""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(''			)


