


'''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 					_snake_case   (     A       )						->    int:
							np.random.seed(args.seed       )
							torch.manual_seed(args.seed       )
							if args.n_gpu > 0:
														torch.cuda.manual_seed_all(args.seed       )


def 					_snake_case   (     )						->    Any:
							lowerCAmelCase__          =	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''' ,    )
							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(A       )

							# 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(A       )
							lowerCAmelCase__          =	args.prompt if args.prompt else input('''Model prompt >>> '''       )

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

							lowerCAmelCase__          =	model.generate(
							    **A ,    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(A       ):
														print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""       )
														lowerCAmelCase__          =	generated_sequence.tolist()

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

														# 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=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

__UpperCAmelCase    				=							TypeVar('''T''')
__UpperCAmelCase    				=							TypeVar('''U''')




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	key
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None







							def __repr__(		self   )       ->							str:
														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 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self   )       ->							None:
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.rear, self.head







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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__          =	self.rear.prev

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

														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	previous
														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	self.rear
														node.freq += 1
														self._position_node(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:

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

																					lowerCAmelCase__          =	previous_node.prev
																					lowerCAmelCase__          =	node.prev
																					lowerCAmelCase__          =	previous_node
																					lowerCAmelCase__          =	node







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							DoubleLinkedListNode[T, U] | None:

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

														lowerCAmelCase__          =	node.next
														lowerCAmelCase__          =	node.prev
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														return node




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							lowercase__			:   dict[Callable[[T], U], LFUCache[T, U]]							=      {}







							def __init__(		self					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	DoubleLinkedList()
														lowerCAmelCase__          =	capacity
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	{}







							def __repr__(		self   )       ->							str:
														return (
														    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
														    F"""capacity={self.capacity}, current_size={self.num_keys})"""
														)







							def __contains__(		self					,    lowerCamelCase_   )       ->							bool:
														return key in self.cache







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							U | None:

														if key in self.cache:
																					self.hits += 1
																					lowerCAmelCase__          =	self.cache[key]
																					lowerCAmelCase__          =	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(lowerCamelCase_   )
																					return node.val
														self.miss += 1
														return None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:

														if key not in self.cache:
																					if self.num_keys >= self.capacity:
																												# delete first node when over capacity
																												lowerCAmelCase__          =	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(lowerCamelCase_   ) is not None
																												# first_node guaranteed to be in list

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

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







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_ = 1_28   )       ->							Callable[[Callable[[T], U]], Callable[..., U]]:

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

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

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

																					setattr(lowerCamelCase_					,    '''cache_info'''					,    lowerCamelCase_   )  # noqa: B010

																					return cache_decorator_wrapper

														return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()