
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 					_a      (		lowerCamelCase							):
 np.random.seed(args.seed							)
 torch.manual_seed(args.seed							)
 if args.n_gpu > 0:
  torch.cuda.manual_seed_all(args.seed							)





def 					_a      (		):
 lowerCamelCase	:			List[str]           =   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	:			Any           =   parser.parse_args()

 lowerCamelCase	:			str           =   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""							)
 lowerCamelCase	:			int           =   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	:			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(lowerCamelCase							)
 lowerCamelCase	:			List[Any]           =   args.prompt if args.prompt else input("""Model prompt >>> """							)

 lowerCamelCase	:			Union[str, Any]           =   tokenizer(lowerCamelCase,			return_tensors="""pt""",			add_special_tokens=lowerCamelCase							)
 lowerCamelCase	:			Optional[Any]           =   {key: value.to(args.device							) for key, value in inputs.items()}

 lowerCamelCase	:			Dict           =   model.generate(
     **lowerCamelCase,			max_length=args.length + len(inputs["""input_ids"""][0]							),			penalty_alpha=args.penalty_alpha,			top_k=args.k,			)

 lowerCamelCase	:			str           =   []
 for generated_sequence_idx, generated_sequence in enumerate(lowerCamelCase							):
  print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''							)
  lowerCamelCase	:			Optional[Any]           =   generated_sequence.tolist()

  # Decode text
  lowerCamelCase	:			List[str]           =   tokenizer.decode(lowerCamelCase,			clean_up_tokenization_spaces=lowerCamelCase,			add_special_tokens=lowerCamelCase							)

  # Remove all text after the stop token
  lowerCamelCase	:			Union[str, 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	:			List[str]           =   (
      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()






from __future__ import annotations

from math import pi, pow





def 					_a      (		lowerCamelCase							):
 if side_length < 0:
  raise ValueError("""vol_cube() only accepts non-negative values"""							)
 return pow(lowerCamelCase,			3							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or radius < 0:
  raise ValueError("""vol_spherical_cap() only accepts non-negative values"""							)
 # Volume is 1/3 pi * height squared * (3 * radius - height)
 return 1 / 3 * pi * pow(lowerCamelCase,			2							) * (3 * radius - height)





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 if radius_a < 0 or radius_a < 0 or centers_distance < 0:
  raise ValueError("""vol_spheres_intersect() only accepts non-negative values"""							)
 if centers_distance == 0:
  return vol_sphere(min(lowerCamelCase,			lowerCamelCase							)							)

 lowerCamelCase	:			int           =   (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 lowerCamelCase	:			Any           =   (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )

 return vol_spherical_cap(lowerCamelCase,			lowerCamelCase							) + vol_spherical_cap(lowerCamelCase,			lowerCamelCase							)





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):

 if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
  raise ValueError(
      """vol_spheres_union() only accepts non-negative values, non-zero radius"""							)

 if centers_distance == 0:
  return vol_sphere(max(lowerCamelCase,			lowerCamelCase							)							)

 return (
     vol_sphere(lowerCamelCase							)
     + vol_sphere(lowerCamelCase							)
     - vol_spheres_intersect(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)
 )





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 if width < 0 or height < 0 or length < 0:
  raise ValueError("""vol_cuboid() only accepts non-negative values"""							)
 return float(width * height * length							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_cone() only accepts non-negative values"""							)
 return area_of_base * height / 3.0





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or radius < 0:
  raise ValueError("""vol_right_circ_cone() only accepts non-negative values"""							)
 return pi * pow(lowerCamelCase,			2							) * height / 3.0





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_prism() only accepts non-negative values"""							)
 return float(area_of_base * height							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_pyramid() only accepts non-negative values"""							)
 return area_of_base * height / 3.0





def 					_a      (		lowerCamelCase							):
 if radius < 0:
  raise ValueError("""vol_sphere() only accepts non-negative values"""							)
 # Volume is 4/3 * pi * radius cubed
 return 4 / 3 * pi * pow(lowerCamelCase,			3							)





def 					_a      (		lowerCamelCase							):
 if radius < 0:
  raise ValueError("""vol_hemisphere() only accepts non-negative values"""							)
 # Volume is radius cubed * pi * 2/3
 return pow(lowerCamelCase,			3							) * pi * 2 / 3





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or radius < 0:
  raise ValueError("""vol_circular_cylinder() only accepts non-negative values"""							)
 # Volume is radius squared * height * pi
 return pow(lowerCamelCase,			2							) * height * pi





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 # Volume - (outer_radius squared - inner_radius squared) * pi * height
 if inner_radius < 0 or outer_radius < 0 or height < 0:
  raise ValueError(
      """vol_hollow_circular_cylinder() only accepts non-negative values"""							)
 if outer_radius <= inner_radius:
  raise ValueError("""outer_radius must be greater than inner_radius"""							)
 return pi * (pow(lowerCamelCase,			2							) - pow(lowerCamelCase,			2							)) * height





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 # Volume is 1/3 * pi * height *
 #           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError("""vol_conical_frustum() only accepts non-negative values"""							)
 return (
     1
     / 3
     * pi
     * height
     * (pow(lowerCamelCase,			2							) + pow(lowerCamelCase,			2							) + radius_a * radius_a)
 )





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("""vol_torus() only accepts non-negative values"""							)
 return 2 * pow(lowerCamelCase,			2							) * torus_radius * pow(lowerCamelCase,			2							)





def 					_a      (		):
 print("""Volumes:"""							)
 print(F'''Cube: {vol_cube(2							) = }'''							)  # = 8
 print(F'''Cuboid: {vol_cuboid(2,			2,			2							) = }'''							)  # = 8
 print(F'''Cone: {vol_cone(2,			2							) = }'''							)  # ~= 1.33
 print(F'''Right Circular Cone: {vol_right_circ_cone(2,			2							) = }'''							)  # ~= 8.38
 print(F'''Prism: {vol_prism(2,			2							) = }'''							)  # = 4
 print(F'''Pyramid: {vol_pyramid(2,			2							) = }'''							)  # ~= 1.33
 print(F'''Sphere: {vol_sphere(2							) = }'''							)  # ~= 33.5
 print(F'''Hemisphere: {vol_hemisphere(2							) = }'''							)  # ~= 16.75
 print(F'''Circular Cylinder: {vol_circular_cylinder(2,			2							) = }'''							)  # ~= 25.1
 print(F'''Torus: {vol_torus(2,			2							) = }'''							)  # ~= 157.9
 print(F'''Conical Frustum: {vol_conical_frustum(2,			2,			4							) = }'''							)  # ~= 58.6
 print(F'''Spherical cap: {vol_spherical_cap(1,			2							) = }'''							)  # ~= 5.24
 print(F'''Spheres intersetion: {vol_spheres_intersect(2,			2,			1							) = }'''							)  # ~= 21.21
 print(F'''Spheres union: {vol_spheres_union(2,			2,			1							) = }'''							)  # ~= 45.81
 print(
     F'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1,			2,			3							) = }'''							)  # ~= 28.3


if __name__ == "__main__":
      main()




