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,
)
_SCREAMING_SNAKE_CASE     =     logging.getLogger(__name__)
def 					__lowercase						(    lowerCamelCase__       )					->Tuple:

  '''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 					__lowercase						(    )					->Any:

  '''simple docstring'''




  a__  		=  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"			,					)
  a__  		=  parser.parse_args()

  a__  		=  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"       )
  a__  		=  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
  a__  		=  AutoTokenizer.from_pretrained(args.model_name_or_path       )
  a__  		=  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__       )
  a__  		=  args.prompt if args.prompt else input("Model prompt >>> "       )

  a__  		=  tokenizer(lowerCamelCase__			,					return_tensors="pt"			,					add_special_tokens=lowerCamelCase__       )
  a__  		=  {key: value.to(args.device       ) for key, value in inputs.items()}

  a__  		=  model.generate(
      **lowerCamelCase__			,					max_length=args.length + len(inputs["input_ids"][0]       )			,					penalty_alpha=args.penalty_alpha			,					top_k=args.k			,					)

  a__  		=  []
  for generated_sequence_idx, generated_sequence in enumerate(lowerCamelCase__       ):
    print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="       )
    a__  		=  generated_sequence.tolist()

    # Decode text
    a__  		=  tokenizer.decode(lowerCamelCase__			,					clean_up_tokenization_spaces=lowerCamelCase__			,					add_special_tokens=lowerCamelCase__       )

    # Remove all text after the stop token
    a__  		=  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
    a__  		=  (
        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()


from __future__ import annotations

from statistics import mean
def 					__lowercase						(    lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  a__  		=  2
  a__  		=  list(lowerCamelCase__       )
  a__  		=  [0] * len(lowerCamelCase__       )
  a__  		=  0
  while True:
    a__  		=  True
    for i, burst_time in enumerate(lowerCamelCase__       ):
      if rem_burst_times[i] > 0:
        a__  		=  False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          a__  		=  t - burst_time
          a__  		=  0
    if done is True:
      return waiting_times
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  return [burst + waiting for burst, waiting in zip(lowerCamelCase__			,					lowerCamelCase__       )]


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     [3, 5, 7]
     _SCREAMING_SNAKE_CASE     =     calculate_waiting_times(burst_times)
     _SCREAMING_SNAKE_CASE     =     calculate_turn_around_times(burst_times, waiting_times)
     print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
     for i, burst_time in enumerate(burst_times):
          print(
              F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              F"""{turn_around_times[i]}"""
          )
     print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
     print(F"""Average turn around time = {mean(turn_around_times):.5f}""")

