

'''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,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
		np.random.seed(args.seed  )
		torch.manual_seed(args.seed  )
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed  )


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

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

		parser.add_argument('--seed'    ,				type=SCREAMING_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'    ,				)
		UpperCAmelCase  							=		parser.parse_args()

		UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
		UpperCAmelCase  							=		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(SCREAMING_SNAKE_CASE  )

		# Initialize the model and tokenizer
		UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
		UpperCAmelCase  							=		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(SCREAMING_SNAKE_CASE  )
		UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

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

		UpperCAmelCase  							=		model.generate(
		    **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

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

				# Decode text
				UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

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

				generated_sequences.append(SCREAMING_SNAKE_CASE  )
				print(SCREAMING_SNAKE_CASE  )

		return generated_sequences


if __name__ == "__main__":
		main()





'''simple docstring'''




from collections import namedtuple

_a			:	int								=  namedtuple('from_to', 'from_ to')

_a			:	Dict								=  {
    'atm': from_to(1, 1),
    'pascal': from_to(0.0000098, 101_325),
    'bar': from_to(0.986923, 1.01325),
    'kilopascal': from_to(0.00986923, 101.325),
    'megapascal': from_to(9.86923, 0.101325),
    'psi': from_to(0.068046, 14.6959),
    'inHg': from_to(0.0334211, 29.9213),
    'torr': from_to(0.00131579, 760),
}


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				float    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				str  ):
  if from_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
        + ', '.join(SCREAMING_SNAKE_CASE  )  )
  if to_type not in PRESSURE_CONVERSION:
    raise ValueError(
        f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
        + ', '.join(SCREAMING_SNAKE_CASE  )  )
  return (
      value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


