



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 						_UpperCAmelCase							( 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 						_UpperCAmelCase							( ):





					"""simple docstring"""

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

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

					parser.add_argument("""--seed"""      ,		type=snake_case      ,		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(snake_case     )

					# 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(snake_case     )
					_lowerCAmelCase         =       args.prompt if args.prompt else input("""Model prompt >>> """     )

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

					_lowerCAmelCase         =       model.generate(
					    **snake_case      ,		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(snake_case     ):
										print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='     )
										_lowerCAmelCase         =       generated_sequence.tolist()

										# Decode text
										_lowerCAmelCase         =       tokenizer.decode(snake_case      ,		clean_up_tokenization_spaces=snake_case      ,		add_special_tokens=snake_case     )

										# 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=snake_case     )     ) :]
										)

										generated_sequences.append(snake_case     )
										print(snake_case     )

					return generated_sequences


if __name__ == "__main__":
	main()







"""simple docstring"""






def 			A__		(					_UpperCAmelCase    :      str	)							->	list:

	'''simple docstring'''


	snake_case__  :						Any		=      []
	snake_case__  :						List[Any]		=      1

	for i in range(len(_UpperCAmelCase	)	):
		if i + 1 < len(_UpperCAmelCase	) and text[i] == text[i + 1]:
			count += 1
		else:
			encoded.append((text[i], count)	)
			snake_case__  :						Dict		=      1

	return encoded



def 			A__		(					_UpperCAmelCase    :      list	)							->	str:

	'''simple docstring'''


	return "".join(char * length for char, length in encoded	)


if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""run_length_encode""", verbose=True)
					testmod(name="""run_length_decode""", verbose=True)





