




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,
)
UpperCamelCase								=     logging.getLogger(__name__)



def 							_A   (						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 							_A   (						):
     """simple docstring"""

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

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

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

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

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

          # Decode text
          lowerCAmelCase__		= tokenizer.decode(lowerCAmelCase_   ,				clean_up_tokenization_spaces=lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_			)

          # Remove all text after the stop token
          lowerCAmelCase__		= 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
          lowerCAmelCase__		= (
              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 typing import Generic, TypeVar

UpperCamelCase								=     TypeVar('T')





class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       List[Any]:
         lowerCAmelCase__		= data  # Assign data
         lowerCAmelCase__		= None  # Initialize next as null
         lowerCAmelCase__		= None  # Initialize prev as null






class  __lowerCamelCase       (    Generic[T]    ):




    """simple docstring"""

    def __init__(       self     :     Any   )      ->       None:
         lowerCAmelCase__		= None


    def    a      (       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     T   )      ->       None:
         if self.head is None:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
         else:
              lowerCAmelCase__		= Node(SCREAMING_SNAKE_CASE__   )
              lowerCAmelCase__		= new_node
              lowerCAmelCase__		= self.head
              lowerCAmelCase__		= None
              lowerCAmelCase__		= new_node


    def    a      (       self     :     int   )      ->       T | None:
         if self.head is None:
              return None
         else:
              assert self.head is not None
              lowerCAmelCase__		= self.head.data
              lowerCAmelCase__		= self.head.next
              if self.head is not None:
                   lowerCAmelCase__		= None
              return temp


    def    a      (       self     :     int   )      ->       T | None:
         return self.head.data if self.head is not None else None


    def __len__(       self     :     List[str]   )      ->       int:
         lowerCAmelCase__		= self.head
         lowerCAmelCase__		= 0
         while temp is not None:
              count += 1
              lowerCAmelCase__		= temp.next
         return count


    def    a      (       self     :     Tuple   )      ->       bool:
         return self.head is None


    def    a      (       self     :     Dict   )      ->       None:
         print("stack elements are:"   )
         lowerCAmelCase__		= self.head
         while temp is not None:
              print(temp.data   ,	end="->"   )
              lowerCAmelCase__		= temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     UpperCamelCase								=     Stack()

     # Insert 4 at the beginning. So stack becomes 4->None
     print('Stack operations using Doubly LinkedList')
     stack.push(4)

     # Insert 5 at the beginning. So stack becomes 4->5->None
     stack.push(5)

     # Insert 6 at the beginning. So stack becomes 4->5->6->None
     stack.push(6)

     # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
     stack.push(7)

     # Print the stack
     stack.print_stack()

     # Print the top element
     print('\nTop element is ', stack.top())

     # Print the stack size
     print('Size of the stack is ', len(stack))

     # pop the top element
     stack.pop()

     # pop the top element
     stack.pop()

     # two elements have now been popped off
     stack.print_stack()

     # Print True if the stack is empty else False
     print('\nstack is empty:', stack.is_empty())
