
"""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 __magic_name__  (							_lowerCamelCase:		Optional[int] )				->	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 __magic_name__  (							)				->	Any:

	'''simple docstring'''




	lowerCAmelCase				       =     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''',				)
	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(_lowerCamelCase )

	# 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(_lowerCamelCase )
	lowerCAmelCase				       =     args.prompt if args.prompt else input('''Model prompt >>> ''' )

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

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

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

		# 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=_lowerCamelCase ) ) :]
		)

		generated_sequences.append(_lowerCamelCase )
		print(_lowerCamelCase )

	return generated_sequences


if __name__ == "__main__":
					main()




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		int,				_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     1.0
	for i in range(1,				r + 1 ):
		ret *= (n + 1 - i) / i
	return round(_lowerCamelCase )




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return choose(8 + n,				_lowerCamelCase ) + choose(9 + n,				_lowerCamelCase ) - 10




def __magic_name__  (							_lowerCamelCase:		int )				->	int:

	'''simple docstring'''




	return sum(non_bouncy_exact(_lowerCamelCase ) for i in range(1,				n + 1 ) )




def __magic_name__  (							_lowerCamelCase:		int = 100 )				->	int:

	'''simple docstring'''




	return non_bouncy_upto(_lowerCamelCase )


if __name__ == "__main__":
					print(f"""{solution() = }""")


