



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,
)
__A             =logging.getLogger(__name__)

def 	a						(				_UpperCAmelCase							: Dict						):







		'''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'''





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

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

		parser.add_argument('''--seed'''			,       type=_UpperCAmelCase			,       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   :					List[str]													=     parser.parse_args()

		__UpperCAmelCase   :					Optional[Any]													=     torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
		__UpperCAmelCase   :					Optional[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(_UpperCAmelCase						)

		# Initialize the model and tokenizer
		__UpperCAmelCase   :					str													=     AutoTokenizer.from_pretrained(args.model_name_or_path						)
		__UpperCAmelCase   :					Optional[int]													=     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(_UpperCAmelCase						)
		__UpperCAmelCase   :					Optional[int]													=     args.prompt if args.prompt else input('''Model prompt >>> '''						)

		__UpperCAmelCase   :					Any													=     tokenizer(_UpperCAmelCase			,       return_tensors='''pt'''			,       add_special_tokens=_UpperCAmelCase						)
		__UpperCAmelCase   :					str													=     {key: value.to(args.device						) for key, value in inputs.items()}

		__UpperCAmelCase   :					Dict													=     model.generate(
		    **_UpperCAmelCase			,       max_length=args.length + len(inputs['''input_ids'''][0]						)			,       penalty_alpha=args.penalty_alpha			,       top_k=args.k			,       )

		__UpperCAmelCase   :					str													=     []
		for generated_sequence_idx, generated_sequence in enumerate(_UpperCAmelCase						):
				print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='						)
				__UpperCAmelCase   :					int													=     generated_sequence.tolist()

				# Decode text
				__UpperCAmelCase   :					Optional[int]													=     tokenizer.decode(_UpperCAmelCase			,       clean_up_tokenization_spaces=_UpperCAmelCase			,       add_special_tokens=_UpperCAmelCase						)

				# Remove all text after the stop token
				__UpperCAmelCase   :					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   :					Dict													=     (
				    prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]			,       clean_up_tokenization_spaces=_UpperCAmelCase						)						) :]
				)

				generated_sequences.append(_UpperCAmelCase						)
				print(_UpperCAmelCase						)

		return generated_sequences


if __name__ == "__main__":
				main()










from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A             =TypeVar("T", bound=bool)

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			List[Any]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					T													=     value
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None




			@property
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					return self._value




			@staticmethod
			def 			snake_case__					(       a_							:			RandomizedHeapNode[T] | None	,	a_							:			RandomizedHeapNode[T] | None			):
					'''simple docstring'''





					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__UpperCAmelCase							, __UpperCAmelCase   :					int													=     roota, roota

					if random.choice([True, False]			):
							__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     roota.right, roota.left

					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(roota.left	,	a_			)

					return roota

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			Iterable[T] | None = ()			):
					'''simple docstring'''





					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None

					if data:
							for item in data:
									self.insert(a_			)




			def 			snake_case__					(       self							:			List[str]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(self._root	,	RandomizedHeapNode(a_			)			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.top()

					if self._root is None:
							return None

					__UpperCAmelCase   :					Tuple													=     RandomizedHeapNode.merge(self._root.left	,	self._root.right			)

					return result




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					if not self._root:
							raise IndexError('''Can\'t get top element for the empty heap.'''			)
					return self._root.value




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     None




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     []
					while self:
							result.append(self.pop()			)

					return result




			def __bool__(       self							:			List[Any]			):
					'''simple docstring'''





					return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





