






"""simple docstring"""




import math as m






def 							lowerCAmelCase			(   UpperCamelCase_:				List[str]  ,			UpperCamelCase_:				List[Any]  ,			UpperCamelCase_:				str=0.001       )							->				Tuple:







  '''simple docstring'''







  return (f(a + h       ) - f(a - h       )) / (2 * h)






def 							lowerCAmelCase			(   UpperCamelCase_:				str  ,			UpperCamelCase_:				Any=0  ,			UpperCamelCase_:				List[Any]=100  ,			UpperCamelCase_:				Dict=0.0001  ,			UpperCamelCase_:				Dict=1E-6  ,			UpperCamelCase_:				Optional[Any]=False       )							->				List[str]:







  '''simple docstring'''







  _a					      =						xa  # set the initial guess
  _a					      =						[a]
  _a					      =						abs(f(UpperCamelCase_       )       )
  _a					      =						lambda UpperCamelCase_       : calc_derivative(UpperCamelCase_  ,			UpperCamelCase_  ,			h=UpperCamelCase_       )  # noqa: E731  Derivative of f(x)
  for _ in range(UpperCamelCase_       ):
    if fa(UpperCamelCase_       ) == 0:
      raise ValueError("No converging solution found"       )
    _a					      =						a - f(UpperCamelCase_       ) / fa(UpperCamelCase_       )  # Calculate the next estimate
    if logsteps:
      steps.append(UpperCamelCase_       )
    if error < maxerror:
      break
  else:
    raise ValueError("Iteration limit reached, no converging solution found"       )
  if logsteps:
    # If logstep is true, then log intermediate steps
    return a, error, steps
  return a, error


if __name__ == "__main__":
     from matplotlib import pyplot as plt

     UpperCamelCase											=     lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     UpperCamelCase							,				UpperCamelCase							,				UpperCamelCase											=     newton_raphson(
         f, xa=10, maxiter=1_000, step=1e-6, logsteps=True
     )
     plt.plot([abs(f(x)) for x in steps])
     plt.xlabel("""step""")
     plt.ylabel("""error""")
     plt.show()
     print(f"solution = {{{solution:f}}}, error = {{{error:f}}}")










"""simple docstring"""




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_:				Dict  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Any       )							->				Tuple:







  '''simple docstring'''







  _a					      =						{"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
  _a      ,						_a      ,						_a					      =						MODELS[model_size]  # set model hyperparameters
  _a					      =						ImageGPTConfig(n_embd=UpperCamelCase_  ,			n_layer=UpperCamelCase_  ,			n_head=UpperCamelCase_       )
  _a					      =						ImageGPTForCausalLM(UpperCamelCase_       )

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

  # Save pytorch-model
  _a					      =						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  _a					      =						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											=     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											=     parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )


