


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   lowerCamelCase_					( _a ):




		"""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"""






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

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

		parser.add_argument('''--seed'''			,     type=_a			,     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__     :    Any					  =      parser.parse_args()

		lowerCAmelCase__     :    str					  =      torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
		lowerCAmelCase__     :    List[str]					  =      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(_a )

		# Initialize the model and tokenizer
		lowerCAmelCase__     :    List[str]					  =      AutoTokenizer.from_pretrained(args.model_name_or_path )
		lowerCAmelCase__     :    Optional[Any]					  =      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(_a )
		lowerCAmelCase__     :    Dict					  =      args.prompt if args.prompt else input('''Model prompt >>> ''' )

		lowerCAmelCase__     :    List[Any]					  =      tokenizer(_a			,     return_tensors='''pt'''			,     add_special_tokens=_a )
		lowerCAmelCase__     :    Optional[Any]					  =      {key: value.to(args.device ) for key, value in inputs.items()}

		lowerCAmelCase__     :    List[str]					  =      model.generate(
		    **_a			,     max_length=args.length + len(inputs['''input_ids'''][0] )			,     penalty_alpha=args.penalty_alpha			,     top_k=args.k			,     )

		lowerCAmelCase__     :    Any					  =      []
		for generated_sequence_idx, generated_sequence in enumerate(_a ):
				print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
				lowerCAmelCase__     :    List[Any]					  =      generated_sequence.tolist()

				# Decode text
				lowerCAmelCase__     :    int					  =      tokenizer.decode(_a			,     clean_up_tokenization_spaces=_a			,     add_special_tokens=_a )

				# Remove all text after the stop token
				lowerCAmelCase__     :    Any					  =      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__     :    int					  =      (
				    prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]			,     clean_up_tokenization_spaces=_a ) ) :]
				)

				generated_sequences.append(_a )
				print(_a )

		return generated_sequences


if __name__ == "__main__":
		main()








from itertools import count
from math import asin, pi, sqrt






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		return (
		    (1 - 2 * point) * sqrt(point - point**2 ) + 2 * point + asin(sqrt(1 - point ) )
		) / 4






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Union[str, Any]					  =      (circles_number + 1 - sqrt(2 * circles_number )) / (
		    2 * (circles_number**2 + 1)
		)
		lowerCAmelCase__     :    Tuple					  =      circles_number * intersection_y

		lowerCAmelCase__     :    Any					  =      intersection_x * intersection_y / 2
		lowerCAmelCase__     :    Optional[Any]					  =      circle_bottom_arc_integral(
		    1 / 2 ) - circle_bottom_arc_integral(_a )

		return triangle_area + concave_region_area






def   lowerCamelCase_					( _a = 1 / 1_000 ):




		"""simple docstring"""






		lowerCAmelCase__     :    Any					  =      (1 - pi / 4) / 4

		for n in count(1 ):
				if concave_triangle_area(_a ) / l_section_area < fraction:
						return n

		return -1


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




