





import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	Dict:
						# Initialise PyTorch model
						UpperCAmelCase_			:      List[str]     						=  RemBertConfig.from_json_file(__UpperCamelCase   )
						print('''Building PyTorch model from configuration: {}'''.format(str(__UpperCamelCase   )   )   )
						UpperCAmelCase_			:      int     						=  RemBertModel(__UpperCamelCase   )

						# Load weights from tf checkpoint
						load_tf_weights_in_rembert(__UpperCamelCase,			__UpperCamelCase,			__UpperCamelCase   )

						# Save pytorch-model
						print('''Save PyTorch model to {}'''.format(__UpperCamelCase   )   )
						torch.save(model.state_dict(),			__UpperCamelCase   )


if __name__ == "__main__":
					snake_case_     :       Any						=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
					)
					parser.add_argument(
					    "--rembert_config_file",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        "The config json file corresponding to the pre-trained RemBERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					snake_case_     :       Optional[int]						=  parser.parse_args()
					convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)












'''simple docstring'''







import sys
import turtle





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float]   ) ->	tuple[float, float]:
						return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			tuple[float, float],			SCREAMING_SNAKE_CASE__      :			int,			) ->	None:
						my_pen.up()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.down()
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )
						my_pen.goto(vertexa[0],			vertexa[1]   )

						if depth == 0:
												return

						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )
						triangle(SCREAMING_SNAKE_CASE__,			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			get_mid(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   ),			depth - 1   )


if __name__ == "__main__":
					if len(sys.argv) != 2:
										raise ValueError(
										    "Correct format for using this script: "
										    "python fractals.py <int:depth_for_fractal>"
										)
					snake_case_     :       Any						=  turtle.Turtle()
					my_pen.ht()
					my_pen.speed(5)
					my_pen.pencolor("red")

					snake_case_     :       Tuple						=  [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
					triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))





