






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


lowerCamelCase	:							Union[str, Any]				=logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	Tuple:
     UpperCamelCase__			:   int       				=  set()
     UpperCamelCase__			:   int       				=  []

     def parse_line(__lowerCAmelCase			):
          for line in fp:
               if isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
                    UpperCamelCase__			:   Union[str, Any]       				=  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(__lowerCAmelCase			) > 0:
                         UpperCamelCase__			:   Union[str, Any]       				=  '''\n'''.join(__lowerCAmelCase			)
                         # Only keep the warnings specified in `targets`
                         if any(f': {x}: ' in warning for x in targets			):
                              selected_warnings.add(__lowerCAmelCase			)
                         buffer.clear()
                    continue
               else:
                    UpperCamelCase__			:   Dict       				=  line.strip()
                    buffer.append(__lowerCAmelCase			)

     if from_gh:
          for filename in os.listdir(__lowerCAmelCase			):
               UpperCamelCase__			:   Optional[int]       				=  os.path.join(__lowerCAmelCase					,    __lowerCAmelCase			)
               if not os.path.isdir(__lowerCAmelCase			):
                    # read the file
                    if filename != "warnings.txt":
                         continue
                    with open(__lowerCAmelCase			) as fp:
                         parse_line(__lowerCAmelCase			)
     else:
          try:
               with zipfile.ZipFile(__lowerCAmelCase			) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(__lowerCAmelCase			):
                              # read the file
                              if filename != "warnings.txt":
                                   continue
                              with z.open(__lowerCAmelCase			) as fp:
                                   parse_line(__lowerCAmelCase			)
          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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Union[str, Any]       				=  set()

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

     return selected_warnings


if __name__ == "__main__":







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Dict:
      return values.split(","			)

 lowerCamelCase	:							Any				=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.''',
 )

 lowerCamelCase	:							List[str]				=parser.parse_args()

 lowerCamelCase	:							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
  lowerCamelCase	:							Optional[Any]				=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
 lowerCamelCase	:							int				=extract_warnings(args.output_dir, args.targets)
 lowerCamelCase	:							Optional[Any]				=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)




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


A__								=						logging.get_logger(__name__)

A__								=						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__								=						{
    '''vocab_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
        ),
        '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-reader''': (
            '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-reader''': (
            '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
        ),
    },
}

A__								=						{
    '''google/realm-cc-news-pretrained-embedder''': 512,
    '''google/realm-cc-news-pretrained-encoder''': 512,
    '''google/realm-cc-news-pretrained-scorer''': 512,
    '''google/realm-cc-news-pretrained-openqa''': 512,
    '''google/realm-orqa-nq-openqa''': 512,
    '''google/realm-orqa-nq-reader''': 512,
    '''google/realm-orqa-wq-openqa''': 512,
    '''google/realm-orqa-wq-reader''': 512,
}

A__								=						{
    '''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    List[Any]											=					VOCAB_FILES_NAMES
		__lowerCAmelCase	:    Optional[Any]											=					PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase	:    Optional[Any]											=					PRETRAINED_INIT_CONFIGURATION
		__lowerCAmelCase	:    Optional[Any]											=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase	:    Optional[Any]											=					RealmTokenizer



		def __init__(     self     :Optional[Any]   ,__lowercase     :Dict=None   ,__lowercase     :Optional[int]=None   ,__lowercase     :Optional[Any]=True   ,__lowercase     :Optional[int]="[UNK]"   ,__lowercase     :List[str]="[SEP]"   ,__lowercase     :List[str]="[PAD]"   ,__lowercase     :int="[CLS]"   ,__lowercase     :str="[MASK]"   ,__lowercase     :Dict=True   ,__lowercase     :List[str]=None   ,**__lowercase     :Any   ,):
						super().__init__(
						    __lowercase   ,tokenizer_file=__lowercase   ,do_lower_case=__lowercase   ,unk_token=__lowercase   ,sep_token=__lowercase   ,pad_token=__lowercase   ,cls_token=__lowercase   ,mask_token=__lowercase   ,tokenize_chinese_chars=__lowercase   ,strip_accents=__lowercase   ,**__lowercase   ,)

						snake_case__	:      List[str]     		=	json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
						if (
						    normalizer_state.get('''lowercase'''   ,__lowercase  ) != do_lower_case
						    or normalizer_state.get('''strip_accents'''   ,__lowercase  ) != strip_accents
						    or normalizer_state.get('''handle_chinese_chars'''   ,__lowercase  ) != tokenize_chinese_chars
						):
										snake_case__	:      Optional[int]     		=	getattr(__lowercase   ,normalizer_state.pop('''type'''  )  )
										snake_case__	:      List[Any]     		=	do_lower_case
										snake_case__	:      Optional[Any]     		=	strip_accents
										snake_case__	:      List[str]     		=	tokenize_chinese_chars
										snake_case__	:      Dict     		=	normalizer_class(**__lowercase  )

						snake_case__	:      Tuple     		=	do_lower_case



		def 	__lowerCamelCase			(     self     :Union[str, Any]   ,__lowercase     :Union[str, Any]   ,**__lowercase     :Any  ):
						snake_case__	:      Dict     		=	PaddingStrategy.MAX_LENGTH

						snake_case__	:      List[str]     		=	text
						snake_case__	:      int     		=	kwargs.pop('''text_pair'''   ,__lowercase  )
						snake_case__	:      Optional[int]     		=	kwargs.pop('''return_tensors'''   ,__lowercase  )

						snake_case__	:      str     		=	{
						    '''input_ids''': [],
						    '''attention_mask''': [],
						    '''token_type_ids''': [],
						}

						for idx, candidate_text in enumerate(__lowercase  ):
										if batch_text_pair is not None:
														snake_case__	:      Optional[int]     		=	batch_text_pair[idx]
										else:
														snake_case__	:      Tuple     		=	None

										snake_case__	:      List[str]     		=	super().__call__(__lowercase   ,__lowercase   ,return_tensors=__lowercase   ,**__lowercase  )

										snake_case__	:      Optional[Any]     		=	encoded_candidates.get('''input_ids'''  )
										snake_case__	:      Optional[Any]     		=	encoded_candidates.get('''attention_mask'''  )
										snake_case__	:      List[Any]     		=	encoded_candidates.get('''token_type_ids'''  )

										if encoded_input_ids is not None:
														output_data["input_ids"].append(__lowercase  )
										if encoded_attention_mask is not None:
														output_data["attention_mask"].append(__lowercase  )
										if encoded_token_type_ids is not None:
														output_data["token_type_ids"].append(__lowercase  )

						snake_case__	:      Any     		=	{key: item for key, item in output_data.items() if len(__lowercase  ) != 0}

						return BatchEncoding(__lowercase   ,tensor_type=__lowercase  )



		def 	__lowerCamelCase			(     self     :List[Any]   ,__lowercase     :Tuple   ,__lowercase     :Tuple=None  ):
						snake_case__	:      List[str]     		=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
										output += token_ids_a + [self.sep_token_id]

						return output



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      Tuple     		=	[self.sep_token_id]
						snake_case__	:      Union[str, Any]     		=	[self.cls_token_id]
						if token_ids_a is None:
										return len(cls + token_ids_a + sep  ) * [0]
						return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]






		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[str] = None  ):
						snake_case__	:      Tuple     		=	self._tokenizer.model.save(__lowercase   ,name=__lowercase  )
						return tuple(__lowercase  )

