






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






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''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 _lowerCAmelCase						(	):

						'''simple docstring'''






						__magic_name__      =  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=2_0 )
						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=4_2			,    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"			,    )
						__magic_name__      =  parser.parse_args()

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

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

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

						__magic_name__      =  []
						for generated_sequence_idx, generated_sequence in enumerate(__lowerCamelCase ):
												print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
												__magic_name__      =  generated_sequence.tolist()

												# Decode text
												__magic_name__      =  tokenizer.decode(__lowerCamelCase			,    clean_up_tokenization_spaces=__lowerCamelCase			,    add_special_tokens=__lowerCamelCase )

												# Remove all text after the stop token
												__magic_name__      =  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
												__magic_name__      =  (
												    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"""

import math






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						if number == 0:
												__magic_name__      =  "Zero"
												return words
						else:
												__magic_name__      =  math.logaa(__lowerCamelCase )
												__magic_name__      =  digits + 1
												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "One"
												__magic_name__      =  "Two"
												__magic_name__      =  "Three"
												__magic_name__      =  "Four"
												__magic_name__      =  "Five"
												__magic_name__      =  "Six"
												__magic_name__      =  "Seven"
												__magic_name__      =  "Eight"
												__magic_name__      =  "Nine"

												__magic_name__      =  {}
												__magic_name__      =  ""
												__magic_name__      =  "Twenty"
												__magic_name__      =  "Thirty"
												__magic_name__      =  "Forty"
												__magic_name__      =  "Fifty"
												__magic_name__      =  "Sixty"
												__magic_name__      =  "Seventy"
												__magic_name__      =  "Eighty"
												__magic_name__      =  "Ninety"

												__magic_name__      =  {}
												__magic_name__      =  "Ten"
												__magic_name__      =  "Eleven"
												__magic_name__      =  "Twelve"
												__magic_name__      =  "Thirteen"
												__magic_name__      =  "Fourteen"
												__magic_name__      =  "Fifteen"
												__magic_name__      =  "Sixteen"
												__magic_name__      =  "Seventeen"
												__magic_name__      =  "Eighteen"
												__magic_name__      =  "Nineteen"

												__magic_name__      =  {}
												__magic_name__      =  "Hundred,"
												__magic_name__      =  "Thousand,"
												__magic_name__      =  "Lakh,"
												__magic_name__      =  "Crore,"

												__magic_name__      =  number
												__magic_name__      =  ""
												__magic_name__      =  0
												__magic_name__      =  int(__lowerCamelCase )
												while counter < digits:
																		__magic_name__      =  temp_num % 1_0
																		if counter % 2 == 0:
																								__magic_name__      =  ""
																								if counter in placevalue and current != 0:
																														__magic_name__      =  placevalue[counter]
																								if counter == 2:
																														__magic_name__      =  singles[current] + addition + words
																								elif counter == 0:
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words

																								else:
																														__magic_name__      =  doubles[current] + addition + words

																		else:
																								if counter == 1:
																														if current == 1:
																																				__magic_name__      =  teens[number % 1_0] + words
																														else:
																																				__magic_name__      =  ""
																																				if counter in placevalue:
																																										__magic_name__      =  placevalue[counter]
																																				__magic_name__      =  doubles[current] + addition + words
																								else:
																														__magic_name__      =  ""
																														if counter in placevalue:
																																				if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
																																										__magic_name__      =  ""
																																				else:
																																										__magic_name__      =  placevalue[counter]
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				__magic_name__      =  teens[current] + addition + words
																																				__magic_name__      =  temp_num // 1_0
																																				counter += 1
																														else:
																																				__magic_name__      =  singles[current] + addition + words
																		counter += 1
																		__magic_name__      =  temp_num // 1_0
						return words


if __name__ == "__main__":
			import doctest

			doctest.testmod()



