





"""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			):


     @staticmethod
     def      lowerCamelCase	( __UpperCamelCase      :ArgumentParser		):
            A		=  parser.add_parser("login"					,	help="Log in using the same credentials as on huggingface.co"		)
            login_parser.set_defaults(func=lambda __UpperCamelCase		: LoginCommand(_UpperCamelCase		)		)
            A		=  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		)		)
            A		=  parser.add_parser("logout"					,	help="Log out"		)
            logout_parser.set_defaults(func=lambda __UpperCamelCase		: LogoutCommand(_UpperCamelCase		)		)

            # 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=_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  _UpperCAmelCase       :
     UpperCamelCase								=							"\u001b[1m"
     UpperCamelCase								=							"\u001b[31m"
     UpperCamelCase								=							"\u001b[90m"
     UpperCamelCase								=							"\u001b[0m"


     @classmethod
     def      lowerCamelCase	( cls      :Tuple					,	__UpperCamelCase      :str		):
            return f"{cls._bold}{s}{cls._reset}"


     @classmethod
     def      lowerCamelCase	( cls      :int					,	__UpperCamelCase      :Union[str, Any]		):
            return f"{cls._bold}{cls._red}{s}{cls._reset}"


     @classmethod
     def      lowerCamelCase	( cls      :Optional[int]					,	__UpperCamelCase      :List[Any]		):
            return f"{cls._gray}{s}{cls._reset}"







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






class  _UpperCAmelCase       :


     def __init__( self      :Union[str, Any]					,	__UpperCamelCase      :Optional[int]		):
            A		=  args







class  _UpperCAmelCase       (     __lowerCamelCase			):


     def      lowerCamelCase	( self      :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			):


     def      lowerCamelCase	( self      :Any		):
            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(_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  _UpperCAmelCase       (     __lowerCamelCase			):


     def      lowerCamelCase	( self      :Any		):
            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			):


     def      lowerCamelCase	( self      :List[str]		):
            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(_UpperCamelCase		)
            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(_UpperCamelCase		)}"		)

            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(_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(""		)









"""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  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."					,	__UpperCamelCase					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  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__(__UpperCamelCase					,	__UpperCamelCase		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# 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
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  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=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

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

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	__UpperCamelCase					,	)
												return self.image_processor_class


					@property
					def      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


