code: 
  
  
  
  
  
  '''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			( UpperCamelCase__       ):
   @staticmethod
   def   UpperCAmelCase_							(       lowercase_   :ArgumentParser							)->							Tuple:
       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			:
   __lowercase     	=       """\u001b[1m"""
   __lowercase     	=       """\u001b[31m"""
   __lowercase     	=       """\u001b[90m"""
   __lowercase     	=       """\u001b[0m"""
   @classmethod
   def   UpperCAmelCase_							(       cls   :List[str]		, lowercase_   :Tuple							)->							Any:
       return F"{cls._bold}{s}{cls._reset}"
   @classmethod
   def   UpperCAmelCase_							(       cls   :str		, lowercase_   :Tuple							)->							Optional[int]:
       return F"{cls._bold}{cls._red}{s}{cls._reset}"
  
  
  
   @classmethod
   def   UpperCAmelCase_							(       cls   :str		, lowercase_   :List[Any]							)->							Union[str, Any]:
       return F"{cls._gray}{s}{cls._reset}"
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[List[Union[str, int]]]		, _lowerCamelCase      :      List[str] ):
      A__       =    [max(len(str(_lowerCamelCase ) ) for x in col ) for col in zip(*_lowerCamelCase		, _lowerCamelCase )]
      A__       =    ("{{:{}}} " * len(_lowerCamelCase )).format(*_lowerCamelCase )
      A__       =    []
      lines.append(row_format.format(*_lowerCamelCase ) )
      lines.append(row_format.format(*["-" * w for w in col_widths] ) )
      for row in rows:
          lines.append(row_format.format(*_lowerCamelCase ) )
      return "\n".join(_lowerCamelCase )
  
  
  
  
  
  
  class  UpperCAmelCase			:
   def __init__(       self   :Optional[int]		, lowercase_   :Tuple							)->							Any:
       A__       =    args
  
  
  
  
  
  
  class  UpperCAmelCase			( UpperCamelCase__       ):
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							List[Any]:
       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			( UpperCamelCase__       ):
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Dict:
       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			( UpperCamelCase__       ):
   def   UpperCAmelCase_							(       self   :Any							)->							str:
       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			( UpperCamelCase__       ):
   def   UpperCAmelCase_							(       self   :Any							)->							Dict:
       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(""							)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  __lowerCAmelCase							:			Tuple				="\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"
  
  __lowerCAmelCase							:			Dict				="\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"
  
  __lowerCAmelCase							:			Dict				="\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION       )
  class      UpperCAmelCase			( datasets.Metric       ):
   def   UpperCAmelCase_							(       self   :int							)->							Union[str, Any]:
       if version.parse(scb.__version__							) < version.parse("1.4.12"							):
           raise ImportWarning(
               "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
               "You can install it with `pip install \"sacrebleu>=1.4.12\"`."							)
       return datasets.MetricInfo(
           description=_DESCRIPTION		, citation=_CITATION		, homepage="https://github.com/mjpost/sacreBLEU"		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
               {
                   "predictions": datasets.Value("string"		, id="sequence"							),
                   "references": datasets.Sequence(datasets.Value("string"		, id="sequence"							)		, id="references"							),
               }							)		, codebase_urls=["https://github.com/mjpost/sacreBLEU"]		, reference_urls=[
               "https://github.com/mjpost/sacreBLEU",
               "https://en.wikipedia.org/wiki/BLEU",
               "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
           ]		, )
  
  
  
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Optional[Any]		, lowercase_   :List[str]="exp"		, lowercase_   :str=None		, lowercase_   :Tuple=False		, lowercase_   :Any=False		, lowercase_   :Optional[Any]=None		, lowercase_   :Any=False		, )->							Tuple:
       A__       =    len(references[0]							)
       if any(len(lowercase_							) != references_per_prediction for refs in references							):
           raise ValueError("Sacrebleu requires the same number of references for each prediction"							)
       A__       =    [[refs[i] for refs in references] for i in range(lowercase_							)]
       A__       =    scb.corpus_bleu(
           lowercase_		, lowercase_		, smooth_method=lowercase_		, smooth_value=lowercase_		, force=lowercase_		, lowercase=lowercase_		, use_effective_order=lowercase_		, **({"tokenize": tokenize} if tokenize else {})		, )
       A__       =    {
           "score": output.score,
           "counts": output.counts,
           "totals": output.totals,
           "precisions": output.precisions,
           "bp": output.bp,
           "sys_len": output.sys_len,
           "ref_len": output.ref_len,
       }
       return output_dict
  
  
  
  