



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Union[str, Any]       =						"AutoFeatureExtractor"
   A__      : List[str]       =						"Speech2Text2Tokenizer"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Dict							)	->      str:
       super().__init__(lowerCamelCase_  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					self.feature_extractor
       UpperCAmelCase_     :       Dict  		=					False


   def __call__(    self:     int  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     str							)	->      List[str]:
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor(*lowerCamelCase_  ,**lowerCamelCase_							)

       if "raw_speech" in kwargs:
           warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""							)
           UpperCAmelCase_     :       Any  		=					kwargs.pop("""raw_speech"""							)
       else:
           UpperCAmelCase_     :       Optional[int]  		=					kwargs.pop("""audio"""  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					kwargs.pop("""sampling_rate"""  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					kwargs.pop("""text"""  ,lowerCamelCase_							)
       if len(lowerCamelCase_							) > 0:
           UpperCAmelCase_     :       Union[str, Any]  		=					args[0]
           UpperCAmelCase_     :       Tuple  		=					args[1:]

       if audio is None and text is None:
           raise ValueError("""You need to specify either an `audio` or `text` input to process."""							)

       if audio is not None:
           UpperCAmelCase_     :       Dict  		=					self.feature_extractor(lowerCamelCase_  ,*lowerCamelCase_  ,sampling_rate=lowerCamelCase_  ,**lowerCamelCase_							)
       if text is not None:
           UpperCAmelCase_     :       int  		=					self.tokenizer(lowerCamelCase_  ,**lowerCamelCase_							)

       if text is None:
           return inputs
       elif audio is None:
           return encodings
       else:
           UpperCAmelCase_     :       Optional[Any]  		=					encodings["""input_ids"""]
           return inputs


   def       A__    (    self:     Optional[int]  ,*lowerCamelCase_:     List[str]  ,**lowerCamelCase_:     Union[str, Any]							)	->      Optional[Any]:
       return self.tokenizer.batch_decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     str  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     List[Any]							)	->      List[str]:
       return self.tokenizer.decode(*lowerCamelCase_  ,**lowerCamelCase_							)






   @contextmanager
   def       A__    (    self:     Dict							)	->      Optional[Any]:
       warnings.warn(
           """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
           """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
           """your audio inputs, or in a separate call."""							)
       UpperCAmelCase_     :       int  		=					True
       UpperCAmelCase_     :       Tuple  		=					self.tokenizer
       yield
       UpperCAmelCase_     :       Tuple  		=					self.feature_extractor
       UpperCAmelCase_     :       Dict  		=					False




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


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__)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str							):
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=argparse.ArgumentParser()
     parser.add_argument(
         '--model_name_or_path'     ,	default=SCREAMING_SNAKE_CASE__     ,	type=SCREAMING_SNAKE_CASE__     ,	required=SCREAMING_SNAKE_CASE__     ,	)
     parser.add_argument('--prompt'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''							)
     parser.add_argument('--length'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=20							)
     parser.add_argument('--stop_token'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=SCREAMING_SNAKE_CASE__     ,	help='Token at which text generation is stopped'							)
     parser.add_argument(
         '--temperature'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='temperature of 1.0 has no effect, lower tend toward greedy sampling'     ,	)
     parser.add_argument(
         '--repetition_penalty'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='primarily useful for CTRL model; in that case, use 1.2'							)
     parser.add_argument('--k'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0							)
     parser.add_argument('--penalty_alpha'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.0							)
     parser.add_argument('--p'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.9							)

     parser.add_argument('--prefix'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Text added prior to input.'							)
     parser.add_argument('--padding_text'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Deprecated, the use of `--prefix` is preferred.'							)
     parser.add_argument('--xlm_language'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Optional language when used with the XLM model.'							)

     parser.add_argument('--seed'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=42     ,	help='random seed for initialization'							)
     parser.add_argument('--no_cuda'     ,	action='store_true'     ,	help='Avoid using CUDA when available'							)
     parser.add_argument(
         '--fp16'     ,	action='store_true'     ,	help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,	)
     __UpperCamelCase       				=parser.parse_args()

     __UpperCamelCase       				=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     __UpperCamelCase       				=0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'							)

     set_seed(SCREAMING_SNAKE_CASE__							)

     # Initialize the model and tokenizer
     __UpperCamelCase       				=AutoTokenizer.from_pretrained(args.model_name_or_path							)
     __UpperCamelCase       				=AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

     # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
     # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
     model.to(args.device							)

     if args.fpaa:
          model.half()

     logger.info(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=args.prompt if args.prompt else input('Model prompt >>> '							)

     __UpperCamelCase       				=tokenizer(SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				={key: value.to(args.device							) for key, value in inputs.items()}

     __UpperCamelCase       				=model.generate(
         **SCREAMING_SNAKE_CASE__     ,	max_length=args.length + len(inputs['input_ids'][0]							)     ,	penalty_alpha=args.penalty_alpha     ,	top_k=args.k     ,	)

     __UpperCamelCase       				=[]
     for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE__							):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
          __UpperCamelCase       				=generated_sequence.tolist()

          # Decode text
          __UpperCamelCase       				=tokenizer.decode(SCREAMING_SNAKE_CASE__     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)

          # Remove all text after the stop token
          __UpperCamelCase       				=text[: text.find(args.stop_token							) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          __UpperCamelCase       				=(
              prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)							) :]
          )

          generated_sequences.append(SCREAMING_SNAKE_CASE__							)
          print(SCREAMING_SNAKE_CASE__							)

     return generated_sequences


if __name__ == "__main__":
   main()





