

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_							:     Tuple     =	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'''


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

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

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

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

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

						# Decode text
						SCREAMING_SNAKE_CASE__									= tokenizer.decode(_lowerCamelCase						,			clean_up_tokenization_spaces=_lowerCamelCase						,			add_special_tokens=_lowerCamelCase      )

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





"""simple docstring"""




from .hash_table import HashTable





class 				lowerCamelCase  (A__     ):

			def __init__(   self			:  List[str]      ,			*__UpperCAmelCase			:  Union[str, Any]      ,			**__UpperCAmelCase			:  Any    )				->      Optional[int]:
						super().__init__(*__UpperCAmelCase      ,			**__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=None    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= self.hash_function(key + i * i    )

						while self.values[new_key] is not None and self.values[new_key] != key:
									i += 1
									SCREAMING_SNAKE_CASE__									= (
									    self.hash_function(key + i * i    )
									    if not self.balanced_factor() >= self.lim_charge
									    else None
									)

									if new_key is None:
												break

						return new_key


