





from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
_SCREAMING_SNAKE_CASE      	=		numpy.array([0, 0])
_SCREAMING_SNAKE_CASE      	=		numpy.array([0.5, 0.8_6_6_0_2_5_4])
_SCREAMING_SNAKE_CASE      	=		numpy.array([1, 0])
_SCREAMING_SNAKE_CASE      	=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Any											=	initial_vectors
	for _ in range(__a   ):
		snake_case_		:			List[str]											=	iteration_step(__a   )
	return vectors


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			List[Any]											=	[]
	for i, start_vector in enumerate(vectors[:-1]   ):
		snake_case_		:			Tuple											=	vectors[i + 1]
		new_vectors.append(__a   )
		snake_case_		:			List[Any]											=	end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3   )
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3   , 60   )   )
		new_vectors.append(start_vector + difference_vector * 2 / 3   )
	new_vectors.append(vectors[-1]   )
	return new_vectors


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Union[str, Any]											=	numpy.radians(__a   )
	snake_case_						,snake_case_		:			Dict											=	numpy.cos(__a   ), numpy.sin(__a   )
	snake_case_		:			Union[str, Any]											=	numpy.array(((c, -s), (s, c))   )
	return numpy.dot(__a   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Dict											=	plt.gca()
	axes.set_aspect('equal'   )

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	snake_case_						,snake_case_		:			Optional[int]											=	zip(*__a   )
	plt.plot(__a   , __a   )
	plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_SCREAMING_SNAKE_CASE      	=		iterate(INITIAL_VECTORS, 5)
						plot(processed_vectors)








import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	# Load checkpoint
	snake_case_		:			Union[str, Any]											=	torch.load(__a   , map_location='cpu'   )

	snake_case_		:			Union[str, Any]											=	chkpt['model']

	# We have the base model one level deeper than the original XLM repository
	snake_case_		:			str											=	{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			snake_case_		:			Tuple											=	v
		else:
			snake_case_		:			Dict											=	v

	snake_case_		:			Tuple											=	chkpt['params']
	snake_case_		:			List[Any]											=	{n: v for n, v in config.items() if not isinstance(__a   , (torch.FloatTensor, numpy.ndarray)   )}

	snake_case_		:			Optional[int]											=	chkpt['dico_word2id']
	snake_case_		:			List[str]											=	{s + '</w>' if s.find('@@'   ) == -1 and i > 13 else s.replace('@@'   , ''   ): i for s, i in vocab.items()}

	# Save pytorch-model
	snake_case_		:			List[str]											=	pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	snake_case_		:			Dict											=	pytorch_dump_folder_path + '/' + CONFIG_NAME
	snake_case_		:			Any											=	pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""   )
	torch.save(__a   , __a   )

	print(f"""Save configuration file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )

	print(f"""Save vocab file to {pytorch_config_dump_path}"""   )
	with open(__a   , 'w'   , encoding='utf-8'   ) as f:
		f.write(json.dumps(__a   , indent=2   ) + '\n'   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

