'''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,
)
_lowerCamelCase :				Optional[int]														= logging.getLogger(__name__)



def  _lowerCAmelCase				(				__a      )  ->      List[str]:
     '''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  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







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

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

     parser.add_argument("""--seed"""			,      type=__a			,      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  :Union[str, Any]       =parser.parse_args()

     _UpperCamelCase  :List[str]       =torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
     _UpperCamelCase  :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(__a      )

     # Initialize the model and tokenizer
     _UpperCamelCase  :str       =AutoTokenizer.from_pretrained(args.model_name_or_path      )
     _UpperCamelCase  :List[Any]       =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(__a      )
     _UpperCamelCase  :int       =args.prompt if args.prompt else input("""Model prompt >>> """      )

     _UpperCamelCase  :Dict       =tokenizer(__a			,      return_tensors="""pt"""			,      add_special_tokens=__a      )
     _UpperCamelCase  :Any       ={key: value.to(args.device      ) for key, value in inputs.items()}

     _UpperCamelCase  :Optional[int]       =model.generate(
         **__a			,      max_length=args.length + len(inputs["""input_ids"""][0]      )			,      penalty_alpha=args.penalty_alpha			,      top_k=args.k			,      )

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

          # Decode text
          _UpperCamelCase  :Dict       =tokenizer.decode(__a			,      clean_up_tokenization_spaces=__a			,      add_special_tokens=__a      )

          # Remove all text after the stop token
          _UpperCamelCase  :List[Any]       =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  :Optional[Any]       =(
              prompt_text + text[len(tokenizer.decode(inputs["""input_ids"""][0]			,      clean_up_tokenization_spaces=__a      )      ) :]
          )

          generated_sequences.append(__a      )
          print(__a      )

     return generated_sequences


if __name__ == "__main__":
      main()

"""simple docstring"""

import sys

from transformers import AutoTokenizer


_A				=					sys.argv[1]
_A				=					sys.argv[2]
_A				=					int(sys.argv[3])

_A				=					0

_A				=					AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
					for line in f_p:
										_A				=					line.rstrip()

										if not line:
															print(line)
															_A				=					0
															continue

										_A				=					line.split()[0]

										_A				=					len(tokenizer.tokenize(token))

										# Token contains strange control characters like \x96 or \x95
										# Just filter out the complete line
										if current_subwords_len == 0:
															continue

										if (subword_len_counter + current_subwords_len) > max_len:
															print('')
															print(line)
															_A				=					current_subwords_len
															continue

										subword_len_counter += current_subwords_len

										print(line)






