





'''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(""							)










'''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



