





"""simple docstring"""




from ...processing_utils import ProcessorMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "SpeechT5FeatureExtractor"
 lowercase_		=      "SpeechT5Tokenizer"

 def __init__(self    :       Dict	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_	,			UpperCAmelCase_)

 def __call__(self    :       str	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =kwargs.pop("audio"	,			UpperCAmelCase_)
   lowerCamelCase__:				Dict        =kwargs.pop("text"	,			UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =kwargs.pop("text_target"	,			UpperCAmelCase_)
   lowerCamelCase__:				Union[str, Any]        =kwargs.pop("audio_target"	,			UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =kwargs.pop("sampling_rate"	,			UpperCAmelCase_)

   if audio is not None and text is not None:
     raise ValueError(
         "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?")
   if audio_target is not None and text_target is not None:
     raise ValueError(
         "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?")
   if audio is None and audio_target is None and text is None and text_target is None:
     raise ValueError(
         "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.")

   if audio is not None:
     lowerCamelCase__:				Dict        =self.feature_extractor(UpperCAmelCase_	,			*UpperCAmelCase_	,			sampling_rate=UpperCAmelCase_	,			**UpperCAmelCase_)
   elif text is not None:
     lowerCamelCase__:				Dict        =self.tokenizer(UpperCAmelCase_	,			**UpperCAmelCase_)
   else:
     lowerCamelCase__:				Any        =None

   if audio_target is not None:
     lowerCamelCase__:				List[Any]        =self.feature_extractor(audio_target=UpperCAmelCase_	,			*UpperCAmelCase_	,			sampling_rate=UpperCAmelCase_	,			**UpperCAmelCase_)
     lowerCamelCase__:				str        =targets["input_values"]
   elif text_target is not None:
     lowerCamelCase__:				str        =self.tokenizer(UpperCAmelCase_	,			**UpperCAmelCase_)
     lowerCamelCase__:				str        =targets["input_ids"]
   else:
     lowerCamelCase__:				Optional[Any]        =None

   if inputs is None:
     return targets

   if targets is not None:
     lowerCamelCase__:				Any        =labels

     lowerCamelCase__:				Optional[Any]        =targets.get("attention_mask")
     if decoder_attention_mask is not None:
       lowerCamelCase__:				int        =decoder_attention_mask

   return inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			*UpperCAmelCase_    :       Union[str, Any]	,			**UpperCAmelCase_    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =kwargs.pop("input_values"	,			UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =kwargs.pop("input_ids"	,			UpperCAmelCase_)
   lowerCamelCase__:				Dict        =kwargs.pop("labels"	,			UpperCAmelCase_)

   if input_values is not None and input_ids is not None:
     raise ValueError("Cannot process both `input_values` and `input_ids` inputs.")
   if input_values is None and input_ids is None and labels is None:
     raise ValueError(
         "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.")

   if input_values is not None:
     lowerCamelCase__:				Tuple        =self.feature_extractor.pad(UpperCAmelCase_	,			*UpperCAmelCase_	,			**UpperCAmelCase_)
   elif input_ids is not None:
     lowerCamelCase__:				Optional[Any]        =self.tokenizer.pad(UpperCAmelCase_	,			**UpperCAmelCase_)
   else:
     lowerCamelCase__:				List[str]        =None

   if labels is not None:
     if "input_ids" in labels or (isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and "input_ids" in labels[0]):
       lowerCamelCase__:				Tuple        =self.tokenizer.pad(UpperCAmelCase_	,			**UpperCAmelCase_)
       lowerCamelCase__:				Any        =targets["input_ids"]
     else:
       lowerCamelCase__:				Any        =self.feature_extractor.feature_size
       lowerCamelCase__:				Optional[int]        =self.feature_extractor.num_mel_bins
       lowerCamelCase__:				Optional[Any]        =self.feature_extractor.pad(UpperCAmelCase_	,			*UpperCAmelCase_	,			**UpperCAmelCase_)
       lowerCamelCase__:				int        =feature_size_hack
       lowerCamelCase__:				Tuple        =targets["input_values"]
   else:
     lowerCamelCase__:				Optional[int]        =None

   if inputs is None:
     return targets

   if targets is not None:
     lowerCamelCase__:				Optional[Any]        =labels

     lowerCamelCase__:				Dict        =targets.get("attention_mask")
     if decoder_attention_mask is not None:
       lowerCamelCase__:				List[Any]        =decoder_attention_mask

   return inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			*UpperCAmelCase_    :       List[Any]	,			**UpperCAmelCase_    :       Dict)	->List[str]:



   '''simple docstring'''



   return self.tokenizer.batch_decode(*UpperCAmelCase_	,			**UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			*UpperCAmelCase_    :       Optional[Any]	,			**UpperCAmelCase_    :       Tuple)	->List[str]:



   '''simple docstring'''



   return self.tokenizer.decode(*UpperCAmelCase_	,			**UpperCAmelCase_)








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
)
__A     =    logging.getLogger(__name__)

if __name__ == "__main__":
     __A     =    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=3_0522, type=int)
     __A     =    parser.parse_args()

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

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

