





'''simple docstring'''







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 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str)			->						int:

     '''simple docstring'''




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

def 		SCREAMING_SNAKE_CASE      (					)			->						int:

     '''simple docstring'''




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

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

     parser.add_argument('--seed'       ,  type=lowerCAmelCase__       ,  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'       ,  )
     _lowercase							:      str  						=							parser.parse_args()

     _lowercase							:      Tuple  						=							torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
     _lowercase							:      str  						=							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(lowerCAmelCase__)

     # Initialize the model and tokenizer
     _lowercase							:      Any  						=							AutoTokenizer.from_pretrained(args.model_name_or_path)
     _lowercase							:      List[str]  						=							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(lowerCAmelCase__)
     _lowercase							:      Dict  						=							args.prompt if args.prompt else input('Model prompt >>> ')

     _lowercase							:      Dict  						=							tokenizer(lowerCAmelCase__       ,  return_tensors='pt'       ,  add_special_tokens=lowerCAmelCase__)
     _lowercase							:      Union[str, Any]  						=							{key: value.to(args.device) for key, value in inputs.items()}

     _lowercase							:      Optional[Any]  						=							model.generate(
         **lowerCAmelCase__       ,  max_length=args.length + len(inputs['input_ids'][0])       ,  penalty_alpha=args.penalty_alpha       ,  top_k=args.k       ,  )

     _lowercase							:      int  						=							[]
     for generated_sequence_idx, generated_sequence in enumerate(lowerCAmelCase__):
          print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''')
          _lowercase							:      Dict  						=							generated_sequence.tolist()

          # Decode text
          _lowercase							:      Tuple  						=							tokenizer.decode(lowerCAmelCase__       ,  clean_up_tokenization_spaces=lowerCAmelCase__       ,  add_special_tokens=lowerCAmelCase__)

          # Remove all text after the stop token
          _lowercase							:      Optional[int]  						=							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
          _lowercase							:      Tuple  						=							(
              prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]       ,  clean_up_tokenization_spaces=lowerCAmelCase__)) :]
          )

          generated_sequences.append(lowerCAmelCase__)
          print(lowerCAmelCase__)

     return generated_sequences


if __name__ == "__main__":
    main()






'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


A						    =							logging.get_logger(__name__)



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      def __init__(							self    : Tuple   ,*UpperCamelCase    : str   ,**UpperCamelCase    : List[Any]  )		->  None:
           warnings.warn(
               'The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
               ' Please use MobileNetV2ImageProcessor instead.'   ,UpperCamelCase   ,)
           super().__init__(*UpperCamelCase   ,**UpperCamelCase  )