


"""simple docstring"""



from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  '''philschmid/bart-large-cnn-samsum'''
   __UpperCAmelCase			:	int							  =  (
       '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
       '''and returns a summary of the text.'''
   )
   __UpperCAmelCase			:	List[str]							  =  '''summarizer'''
   __UpperCAmelCase			:	Optional[int]							  =  AutoTokenizer
   __UpperCAmelCase			:	Optional[Any]							  =  AutoModelForSeqaSeqLM

   __UpperCAmelCase			:	List[Any]							  =  ['''text''']
   __UpperCAmelCase			:	List[str]							  =  ['''text''']







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

          '''simple docstring'''




          return self.pre_processor(UpperCamelCase__						,     return_tensors="pt"						,     truncation=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

          '''simple docstring'''




          return self.model.generate(**UpperCamelCase__						)[0]







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

          '''simple docstring'''




          return self.pre_processor.decode(UpperCamelCase__						,     skip_special_tokens=UpperCamelCase__						,     clean_up_tokenization_spaces=UpperCamelCase__						)









"""simple docstring"""



import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case             =   logging.getLogger(__name__)

if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser(
      description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
  )
  parser.add_argument(
      """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
  )
  parser.add_argument(
      """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
  )
  parser.add_argument("""--vocab_size""", default=30522, type=int)
  __snake_case             =   parser.parse_args()

  logger.info(F'''Loading data from {args.data_file}''')
  with open(args.data_file, """rb""") as fp:
    __snake_case             =   pickle.load(fp)

  logger.info("""Counting occurrences for MLM.""")
  __snake_case             =   Counter()
  for tk_ids in data:
    counter.update(tk_ids)
  __snake_case             =   [0] * args.vocab_size
  for k, v in counter.items():
    __snake_case             =   v

  logger.info(F'''Dump to {args.token_counts_dump}''')
  with open(args.token_counts_dump, """wb""") as handle:
    pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





