






"""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,
)
UpperCamelCase					=     logging.getLogger(__name__)






def 							A(    snake_case_						):
       """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"""





       lowercase__:							Optional[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"		,  )
       lowercase__:							List[Any]         =     parser.parse_args()

       lowercase__:							Dict         =     torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
       lowercase__:							Union[str, Any]         =     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
       lowercase__:							Dict         =     AutoTokenizer.from_pretrained(args.model_name_or_path						)
       lowercase__:							Dict         =     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						)
       lowercase__:							Dict         =     args.prompt if args.prompt else input("Model prompt >>> "						)

       lowercase__:							Union[str, Any]         =     tokenizer(__A		,  return_tensors="pt"		,  add_special_tokens=__A						)
       lowercase__:							Tuple         =     {key: value.to(args.device						) for key, value in inputs.items()}

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

       lowercase__:							Union[str, Any]         =     []
       for generated_sequence_idx, generated_sequence in enumerate(__A						):
              print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""						)
              lowercase__:							List[str]         =     generated_sequence.tolist()

              # Decode text
              lowercase__:							str         =     tokenizer.decode(__A		,  clean_up_tokenization_spaces=__A		,  add_special_tokens=__A						)

              # Remove all text after the stop token
              lowercase__:							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
              lowercase__:							List[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'''







from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a__    :   Dict								=			TypeVar('T')
a__    :   Optional[Any]								=			TypeVar('U')
class    lowercase_	(       Generic[T, U]  ):


    def __init__(	self    ,   a    ,   a       ):
          UpperCamelCase__      =							key
          UpperCamelCase__      =							val
          UpperCamelCase__      =							0
          UpperCamelCase__      =							None
          UpperCamelCase__      =							None


    def __repr__(	self       ):
          return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
              self.key    ,   self.val    ,   self.freq    ,   self.next is not None    ,   self.prev is not None       )

class    lowercase_	(       Generic[T, U]  ):


    def __init__(	self       ):
          UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
          UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
          UpperCamelCase__   ,      UpperCamelCase__      =							self.rear, self.head


    def __repr__(	self       ):
          UpperCamelCase__      =							["DoubleLinkedList"]
          UpperCamelCase__      =							self.head
          while node.next is not None:
                rep.append(str(a       )       )
                UpperCamelCase__      =							node.next
          rep.append(str(self.rear       )       )
          return ",\n    ".join(a       )


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							self.rear.prev

          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None

          UpperCamelCase__      =							node
          UpperCamelCase__      =							previous
          UpperCamelCase__      =							node
          UpperCamelCase__      =							self.rear
          node.freq += 1
          self._position_node(a       )


    def   __a				(	self    ,   a       ):

          while node.prev is not None and node.prev.freq > node.freq:
                # swap node with previous node
                UpperCamelCase__      =							node.prev

                UpperCamelCase__      =							previous_node.prev
                UpperCamelCase__      =							node.prev
                UpperCamelCase__      =							previous_node
                UpperCamelCase__      =							node


    def   __a				(	self    ,   a       ):

          if node.prev is None or node.next is None:
                return None

          UpperCamelCase__      =							node.next
          UpperCamelCase__      =							node.prev
          UpperCamelCase__      =							None
          UpperCamelCase__      =							None
          return node

class    lowercase_	(       Generic[T, U]  ):
    __UpperCAmelCase       =      {}


    def __init__(	self    ,   a       ):
          UpperCamelCase__      =							DoubleLinkedList()
          UpperCamelCase__      =							capacity
          UpperCamelCase__      =							0
          UpperCamelCase__      =							0
          UpperCamelCase__      =							0
          UpperCamelCase__      =							{}


    def __repr__(	self       ):
          return (
              f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
              f'''capacity={self.capacity}, current_size={self.num_keys})'''
          )


    def __contains__(	self    ,   a       ):
          return key in self.cache


    def   __a				(	self    ,   a       ):

          if key in self.cache:
                self.hits += 1
                UpperCamelCase__      =							self.cache[key]
                UpperCamelCase__      =							self.list.remove(self.cache[key]       )
                assert node == value_node

                # node is guaranteed not None because it is in self.cache
                assert node is not None
                self.list.add(a       )
                return node.val
          self.miss += 1
          return None


    def   __a				(	self    ,   a    ,   a       ):

          if key not in self.cache:
                if self.num_keys >= self.capacity:
                      # delete first node when over capacity
                      UpperCamelCase__      =							self.list.head.next

                      # guaranteed to have a non-None first node when num_keys > 0
                      # explain to type checker via assertions
                      assert first_node is not None
                      assert first_node.key is not None
                      assert self.list.remove(a       ) is not None
                      # first_node guaranteed to be in list

                      del self.cache[first_node.key]
                      self.num_keys -= 1
                UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
                self.list.add(self.cache[key]       )
                self.num_keys += 1

          else:
                UpperCamelCase__      =							self.list.remove(self.cache[key]       )
                assert node is not None  # node guaranteed to be in list
                UpperCamelCase__      =							value
                self.list.add(a       )


    @classmethod
    def   __a				(	cls    ,   a = 1_28       ):

          def cache_decorator_inner(a       ) -> Callable[..., U]:
                def cache_decorator_wrapper(*a       ) -> U:
                      if func not in cls.decorator_function_to_instance_map:
                            UpperCamelCase__      =							LFUCache(a       )

                      UpperCamelCase__      =							cls.decorator_function_to_instance_map[func].get(args[0]       )
                      if result is None:
                            UpperCamelCase__      =							func(*a       )
                            cls.decorator_function_to_instance_map[func].put(args[0]    ,   a       )
                      return result

                def cache_info() -> LFUCache[T, U]:
                      return cls.decorator_function_to_instance_map[func]

                setattr(a    ,   "cache_info"    ,   a       )  # noqa: B010

                return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()





