




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,
)
_lowerCamelCase    				=		logging.getLogger(__name__)




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Dict   )						->							Union[str, Any]:
				np.random.seed(args.seed   )
				torch.manual_seed(args.seed   )
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed   )




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

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

				parser.add_argument('''--seed''' ,  type=__UpperCamelCase ,  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(__UpperCamelCase   )

				# 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(__UpperCamelCase   )
				UpperCAmelCase_											=  args.prompt if args.prompt else input('''Model prompt >>> '''   )

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

				UpperCAmelCase_											=  model.generate(
				    **__UpperCamelCase ,  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(__UpperCamelCase   ):
								print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='   )
								UpperCAmelCase_											=  generated_sequence.tolist()

								# Decode text
								UpperCAmelCase_											=  tokenizer.decode(__UpperCamelCase ,  clean_up_tokenization_spaces=__UpperCamelCase ,  add_special_tokens=__UpperCamelCase   )

								# 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=__UpperCamelCase   )   ) :]
								)

								generated_sequences.append(__UpperCamelCase   )
								print(__UpperCamelCase   )

				return generated_sequences


if __name__ == "__main__":
	main()











_lowerCamelCase    				=		10_00




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		float   )						->							float:
    return x * x




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int] ,  __UpperCamelCase    :		float ,  __UpperCamelCase    :		float ,  __UpperCamelCase    :		int = 4   )						->							float:
    assert callable(
        __UpperCamelCase   ), f'the function(object) passed should be callable your input : {function}'
    assert isinstance(__UpperCamelCase ,  (float, int)   ), f'a should be float or integer your input : {a}'
    assert isinstance(function(__UpperCamelCase   ) ,  (float, int)   ), (
        "the function should return integer or float return type of your function, "
        f'{type(__UpperCamelCase   )}'
    )
    assert isinstance(__UpperCamelCase ,  (float, int)   ), f'b should be float or integer your input : {b}'
    assert (
        isinstance(__UpperCamelCase ,  __UpperCamelCase   ) and precision > 0
    ), f'precision should be positive integer your input : {precision}'

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    UpperCAmelCase_											=  (b - a) / N_STEPS
    UpperCAmelCase_											=  function(__UpperCamelCase   ) + function(__UpperCamelCase   )

    for i in range(1 ,  __UpperCamelCase   ):
        UpperCAmelCase_											=  a + h * i
        result += function(__UpperCamelCase   ) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__UpperCamelCase ,  __UpperCamelCase   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





