




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			str      =     {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
  _lowercase:			List[Any]      =     MODELS[model_size]  # set model hyperparameters
  _lowercase:			Union[str, Any]      =     ImageGPTConfig(n_embd=UpperCamelCase       ,  n_layer=UpperCamelCase       ,  n_head=UpperCamelCase				)
  _lowercase:			Optional[int]      =     ImageGPTForCausalLM(UpperCamelCase				)

  # Load weights from numpy
  load_tf_weights_in_imagegpt(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

  # Save pytorch-model
  _lowercase:			Dict      =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _lowercase:			Optional[Any]      =     pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F'Save PyTorch model to {pytorch_weights_dump_path}'				)
  torch.save(model.state_dict()       ,  UpperCamelCase				)
  print(F'Save configuration file to {pytorch_config_dump_path}'				)
  with open(UpperCamelCase       ,  'w'       ,  encoding='utf-8'				) as f:
    f.write(config.to_json_string()				)


if __name__ == "__main__":
   UpperCAmelCase__    :							int       		=     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--imagegpt_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path to the TensorFlow checkpoint path.',
   )
   parser.add_argument(
       '--model_size',
       default=None,
       type=str,
       required=True,
       help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   UpperCAmelCase__    :							Optional[int]       		=     parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )

'''simple docstring'''





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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float  )   ->				float:







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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int  )   ->				float:







     """simple docstring"""
     a					:							List[str]							= (circles_number + 1 - sqrt(2 * circles_number  )) / (
         2 * (circles_number**2 + 1)
     )
     a					:							int							= circles_number * intersection_y

     a					:							List[Any]							= intersection_x * intersection_y / 2
     a					:							List[str]							= circle_bottom_arc_integral(
         1 / 2  ) - circle_bottom_arc_integral(snake_case  )

     return triangle_area + concave_region_area






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float = 1 / 1_000  )   ->				int:







     """simple docstring"""
     a					:							str							= (1 - pi / 4) / 4

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

     return -1


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

