



"""simple docstring"""






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__snake_case							:     Optional[int]   =					logging.get_logger(__name__)

__snake_case							:     List[Any]   =					'▁'

__snake_case							:     int   =					{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__snake_case							:     int   =					{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

__snake_case							:     Any   =					{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
__snake_case							:     List[Any]   =					{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}



class     A__     (						__SCREAMING_SNAKE_CASE							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE					   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE					   =			PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE					   =			['input_ids', 'attention_mask']

 SCREAMING_SNAKE_CASE					   =			[]
 SCREAMING_SNAKE_CASE					   =			[]

 def __init__(  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	Optional[int]=None ,    _SCREAMING_SNAKE_CASE:	str=None ,    _SCREAMING_SNAKE_CASE:	Any="<s>" ,    _SCREAMING_SNAKE_CASE:	int="</s>" ,    _SCREAMING_SNAKE_CASE:	int="</s>" ,    _SCREAMING_SNAKE_CASE:	List[str]="<pad>" ,    _SCREAMING_SNAKE_CASE:	Tuple="<unk>" ,    _SCREAMING_SNAKE_CASE:	List[str]="m2m100" ,    _SCREAMING_SNAKE_CASE:	Optional[Dict[str, Any]] = None ,    _SCREAMING_SNAKE_CASE:	List[str]=8 ,    **_SCREAMING_SNAKE_CASE:	List[Any] ,    )       -> None:

    """simple docstring"""

    __lowerCAmelCase   :						Dict				= {} if sp_model_kwargs is None else sp_model_kwargs

    __lowerCAmelCase   :						Any				= language_codes
    __lowerCAmelCase   :						str				= FAIRSEQ_LANGUAGE_CODES[language_codes]
    __lowerCAmelCase   :						Optional[Any]				= {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

    __lowerCAmelCase   :						Tuple				= kwargs.get("additional_special_tokens" ,    [])
    kwargs["additional_special_tokens"] += [
        self.get_lang_token(_SCREAMING_SNAKE_CASE)
        for lang_code in fairseq_language_code
        if self.get_lang_token(_SCREAMING_SNAKE_CASE) not in kwargs["additional_special_tokens"]
    ]

    super().__init__(
        src_lang=_SCREAMING_SNAKE_CASE ,    tgt_lang=_SCREAMING_SNAKE_CASE ,    bos_token=_SCREAMING_SNAKE_CASE ,    eos_token=_SCREAMING_SNAKE_CASE ,    sep_token=_SCREAMING_SNAKE_CASE ,    unk_token=_SCREAMING_SNAKE_CASE ,    pad_token=_SCREAMING_SNAKE_CASE ,    language_codes=_SCREAMING_SNAKE_CASE ,    sp_model_kwargs=self.sp_model_kwargs ,    num_madeup_words=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE ,    )

    __lowerCAmelCase   :						Optional[Any]				= vocab_file
    __lowerCAmelCase   :						List[Any]				= load_json(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Tuple				= {v: k for k, v in self.encoder.items()}
    __lowerCAmelCase   :						int				= spm_file
    __lowerCAmelCase   :						Tuple				= load_spm(_SCREAMING_SNAKE_CASE ,    self.sp_model_kwargs)

    __lowerCAmelCase   :						Any				= len(self.encoder)

    __lowerCAmelCase   :						Dict				= {
        self.get_lang_token(_SCREAMING_SNAKE_CASE): self.encoder_size + i for i, lang_code in enumerate(_SCREAMING_SNAKE_CASE)
    }
    __lowerCAmelCase   :						Dict				= {lang_code: self.encoder_size + i for i, lang_code in enumerate(_SCREAMING_SNAKE_CASE)}
    __lowerCAmelCase   :						List[Any]				= {v: k for k, v in self.lang_token_to_id.items()}

    __lowerCAmelCase   :						List[str]				= src_lang if src_lang is not None else "en"
    __lowerCAmelCase   :						str				= tgt_lang
    __lowerCAmelCase   :						Dict				= self.get_lang_id(self._src_lang)
    self.set_src_lang_special_tokens(self._src_lang)

    __lowerCAmelCase   :						str				= num_madeup_words

 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

    """simple docstring"""

    return len(self.encoder) + len(self.lang_token_to_id)

 @property
 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> str:

    """simple docstring"""

    return self._src_lang

 @src_lang.setter
 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	str)       -> None:

    """simple docstring"""

    __lowerCAmelCase   :						List[Any]				= new_src_lang
    self.set_src_lang_special_tokens(self._src_lang)

 def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	str)       -> List[str]:

    """simple docstring"""

    return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,    out_type=_SCREAMING_SNAKE_CASE)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	int)       -> Optional[Any]:

    """simple docstring"""

    if token in self.lang_token_to_id:
       return self.lang_token_to_id[token]
    return self.encoder.get(_SCREAMING_SNAKE_CASE ,    self.encoder[self.unk_token])

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	int)       -> str:

    """simple docstring"""

    if index in self.id_to_lang_token:
       return self.id_to_lang_token[index]
    return self.decoder.get(_SCREAMING_SNAKE_CASE ,    self.unk_token)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	str)       -> List[str]:

    """simple docstring"""

    __lowerCAmelCase   :						Union[str, Any]				= []
    __lowerCAmelCase   :						List[str]				= ""
    for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
          out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE) + token
          __lowerCAmelCase   :						Dict				= []
       else:
          current_sub_tokens.append(_SCREAMING_SNAKE_CASE)
    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE)
    return out_string.strip()

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[int] ,    _SCREAMING_SNAKE_CASE:	Optional[List[int]] = None ,    _SCREAMING_SNAKE_CASE:	bool = False)       -> List[int]:

    """simple docstring"""

    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_SCREAMING_SNAKE_CASE ,    token_ids_a=_SCREAMING_SNAKE_CASE ,    already_has_special_tokens=_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						Optional[Any]				= [1] * len(self.prefix_tokens)
    __lowerCAmelCase   :						List[str]				= [1] * len(self.suffix_tokens)
    if token_ids_a is None:
       return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE)) + suffix_ones
    return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE)) + ([0] * len(_SCREAMING_SNAKE_CASE)) + suffix_ones

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[int] ,    _SCREAMING_SNAKE_CASE:	Optional[List[int]] = None)       -> List[int]:

    """simple docstring"""

    if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						Tuple				= {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
    vocab.update(self.added_tokens_encoder)
    return vocab

 def __getstate__(  self:	List[str])       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						List[str]				= self.__dict__.copy()
    __lowerCAmelCase   :						Optional[int]				= None
    return state

 def __setstate__(  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Dict)       -> None:

    """simple docstring"""

    __lowerCAmelCase   :						Tuple				= d

    # for backward compatibility
    if not hasattr(self ,    "sp_model_kwargs"):
       __lowerCAmelCase   :						int				= {}

    __lowerCAmelCase   :						Optional[int]				= load_spm(self.spm_file ,    self.sp_model_kwargs)

 def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Optional[str] = None)       -> Tuple[str]:

    """simple docstring"""

    __lowerCAmelCase   :						List[Any]				= Path(_SCREAMING_SNAKE_CASE)
    if not save_dir.is_dir():
       raise OSError(F"""{save_directory} should be a directory""")
    __lowerCAmelCase   :						Union[str, Any]				= save_dir / (
        (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
    )
    __lowerCAmelCase   :						Dict				= save_dir / (
        (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
    )

    save_json(self.encoder ,    _SCREAMING_SNAKE_CASE)

    if os.path.abspath(self.spm_file) != os.path.abspath(_SCREAMING_SNAKE_CASE) and os.path.isfile(self.spm_file):
       copyfile(self.spm_file ,    _SCREAMING_SNAKE_CASE)
    elif not os.path.isfile(self.spm_file):
       with open(_SCREAMING_SNAKE_CASE ,    "wb") as fi:
          __lowerCAmelCase   :						List[str]				= self.sp_model.serialized_model_proto()
          fi.write(_SCREAMING_SNAKE_CASE)

    return (str(_SCREAMING_SNAKE_CASE), str(_SCREAMING_SNAKE_CASE))

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	str = "en" ,    _SCREAMING_SNAKE_CASE:	Optional[List[str]] = None ,    _SCREAMING_SNAKE_CASE:	str = "ro" ,    **_SCREAMING_SNAKE_CASE:	int ,    )       -> BatchEncoding:

    """simple docstring"""

    __lowerCAmelCase   :						Union[str, Any]				= src_lang
    __lowerCAmelCase   :						Dict				= tgt_lang
    self.set_src_lang_special_tokens(self.src_lang)
    return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Optional[str] ,    _SCREAMING_SNAKE_CASE:	Optional[str] ,    **_SCREAMING_SNAKE_CASE:	Tuple)       -> Any:

    """simple docstring"""

    if src_lang is None or tgt_lang is None:
       raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
    __lowerCAmelCase   :						List[str]				= src_lang
    __lowerCAmelCase   :						Dict				= self(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						List[Any]				= self.get_lang_id(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						List[Any]				= tgt_lang_id
    return inputs

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Tuple:

    """simple docstring"""

    self.set_src_lang_special_tokens(self.src_lang)

 def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Union[str, Any]:

    """simple docstring"""

    self.set_tgt_lang_special_tokens(self.tgt_lang)

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	str)       -> None:

    """simple docstring"""

    __lowerCAmelCase   :						int				= self.get_lang_token(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						List[str]				= self.lang_token_to_id[lang_token]
    __lowerCAmelCase   :						Optional[Any]				= [self.cur_lang_id]
    __lowerCAmelCase   :						int				= [self.eos_token_id]

 def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	str)       -> None:

    """simple docstring"""

    __lowerCAmelCase   :						Tuple				= self.get_lang_token(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Tuple				= self.lang_token_to_id[lang_token]
    __lowerCAmelCase   :						Union[str, Any]				= [self.cur_lang_id]
    __lowerCAmelCase   :						Union[str, Any]				= [self.eos_token_id]

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	str)       -> str:

    """simple docstring"""

    return self.lang_code_to_token[lang]







 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	str)       -> int:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[Any]				= self.get_lang_token(_SCREAMING_SNAKE_CASE)
    return self.lang_token_to_id[lang_token]






def     _lowercase       (						__snake_case      ,__snake_case			)		->     sentencepiece.SentencePieceProcessor:
   __lowerCAmelCase   :						Optional[int]				= sentencepiece.SentencePieceProcessor(**__snake_case			)
   spm.Load(str(__snake_case			)			)
   return spm


def     _lowercase       (						__snake_case			)		->     Union[Dict, List]:
   with open(__snake_case      ,"r"			) as f:
      return json.load(__snake_case			)





def     _lowercase       (						__snake_case      ,__snake_case			)		->     None:
   with open(__snake_case      ,"w"			) as f:
      json.dump(__snake_case      ,__snake_case      ,indent=2			)




"""simple docstring"""






import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__snake_case							:     Tuple   =					logging.get_logger(__name__)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						Optional[Any]				= set()
			__lowerCAmelCase   :						List[Any]				= []

			def parse_line(__snake_case			):
						for line in fp:
									if isinstance(__snake_case      ,__snake_case			):
												__lowerCAmelCase   :						Tuple				= line.decode("UTF-8"			)
									if "warnings summary (final)" in line:
												continue
									# This means we are outside the body of a warning
									elif not line.startswith(" "			):
												# process a single warning and move it to `selected_warnings`.
												if len(__snake_case			) > 0:
															__lowerCAmelCase   :						List[Any]				= "\n".join(__snake_case			)
															# Only keep the warnings specified in `targets`
															if any(F""": {x}: """ in warning for x in targets			):
																		selected_warnings.add(__snake_case			)
															buffer.clear()
												continue
									else:
												__lowerCAmelCase   :						List[str]				= line.strip()
												buffer.append(__snake_case			)

			if from_gh:
						for filename in os.listdir(__snake_case			):
									__lowerCAmelCase   :						List[str]				= os.path.join(__snake_case      ,__snake_case			)
									if not os.path.isdir(__snake_case			):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(__snake_case			) as fp:
															parse_line(__snake_case			)
			else:
						try:
									with zipfile.ZipFile(__snake_case			) as z:
												for filename in z.namelist():
															if not os.path.isdir(__snake_case			):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(__snake_case			) as fp:
																					parse_line(__snake_case			)
						except Exception:
									logger.warning(
									    F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""			)

			return selected_warnings


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Any				= set()

			__lowerCAmelCase   :						str				= [os.path.join(__snake_case      ,__snake_case			) for p in os.listdir(__snake_case			) if (p.endswith(".zip"			) or from_gh)]
			for p in paths:
						selected_warnings.update(extract_warnings_from_single_artifact(__snake_case      ,__snake_case			)			)

			return selected_warnings


if __name__ == "__main__":


	def     _lowercase       (						__snake_case			)		->     Optional[Any]:
				return values.split(","			)

	__snake_case							:     Optional[int]   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
	parser.add_argument(
	    '--output_dir',
	    type=str,
	    required=True,
	    help='Where to store the downloaded artifacts and other result files.',
	)
	parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
	# optional parameters
	parser.add_argument(
	    '--targets',
	    default='DeprecationWarning,UserWarning,FutureWarning',
	    type=list_str,
	    help='Comma-separated list of target warning(s) which we want to extract.',
	)
	parser.add_argument(
	    '--from_gh',
	    action='store_true',
	    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
	)

	__snake_case							:     Tuple   =					parser.parse_args()

	__snake_case							:     Any   =					args.from_gh
	if from_gh:
		# The artifacts have to be downloaded using `actions/download-artifact@v3`
		pass
	else:
		os.makedirs(args.output_dir, exist_ok=True)

		# get download links
		__snake_case							:     str   =					get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
			json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		# download artifacts
		for idx, (name, url) in enumerate(artifacts.items()):
			print(name)
			print(url)
			print('=' * 80)
			download_artifact(name, url, args.output_dir, args.token)
			# Be gentle to GitHub
			time.sleep(1)

    # extract warnings from artifacts
	__snake_case							:     Tuple   =					extract_warnings(args.output_dir, args.targets)
	__snake_case							:     List[str]   =					sorted(selected_warnings)
	with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
		json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)