



"""simple docstring"""
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def        lowerCAmelCase_(   lowercase_     :							Optional[int]							)	->       List[Any]:
				if "model" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''model.'''						,   ''''''							)
				if "norm1" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''norm1'''						,   '''attention.output.LayerNorm'''							)
				if "norm2" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''norm2'''						,   '''output.LayerNorm'''							)
				if "norm" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''norm'''						,   '''LayerNorm'''							)
				if "transformer" in orig_key:
								_lowerCamelCase        =				orig_key.split('''.'''							)[0].split('''_'''							)[-1]
								_lowerCamelCase        =				orig_key.replace(F"""transformer_{layer_num}"""						,   F"""encoder.layer.{layer_num}"""							)
				if "mha.attn" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''mha.attn'''						,   '''attention.self'''							)
				if "mha" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''mha'''						,   '''attention'''							)
				if "W_q" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''W_q'''						,   '''self.query'''							)
				if "W_k" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''W_k'''						,   '''self.key'''							)
				if "W_v" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''W_v'''						,   '''self.value'''							)
				if "ff1" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''ff1'''						,   '''intermediate.dense'''							)
				if "ff2" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''ff2'''						,   '''output.dense'''							)
				if "ff" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''ff'''						,   '''output.dense'''							)
				if "mlm_class" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''mlm.mlm_class'''						,   '''cls.predictions.decoder'''							)
				if "mlm" in orig_key:
								_lowerCamelCase        =				orig_key.replace('''mlm'''						,   '''cls.predictions.transform'''							)
				if "cls" not in orig_key:
								_lowerCamelCase        =				'''yoso.''' + orig_key

				return orig_key



def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							Optional[Any]							)	->       Optional[int]:
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase        =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_							)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												_lowerCamelCase        =				val

				_lowerCamelCase        =				orig_state_dict['''cls.predictions.decoder.bias''']
				_lowerCamelCase        =				torch.arange(SCREAMING_SNAKE_CASE_							).expand((1, -1)							) + 2

				return orig_state_dict



def        lowerCAmelCase_(   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							List[Any]							)	->       Union[str, Any]:
				_lowerCamelCase        =				torch.load(SCREAMING_SNAKE_CASE_						,   map_location='''cpu'''							)['''model_state_dict''']
				_lowerCamelCase        =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_							)
				_lowerCamelCase        =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_							)

				_lowerCamelCase        =				convert_checkpoint_helper(config.max_position_embeddings						,   SCREAMING_SNAKE_CASE_							)

				print(model.load_state_dict(SCREAMING_SNAKE_CASE_							)							)
				model.eval()
				model.save_pretrained(SCREAMING_SNAKE_CASE_							)

				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						int    							=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
			)
			parser.add_argument(
			    """--config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help="""The json file for YOSO model config.""",
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			__SCREAMING_SNAKE_CASE  :						int    							=      parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""
from __future__ import annotations

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

# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE  :						List[str]    							=      numpy.array([0, 0])
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      numpy.array([0.5, 0.866_0254])
__SCREAMING_SNAKE_CASE  :						Tuple    							=      numpy.array([1, 0])
__SCREAMING_SNAKE_CASE  :						List[Any]    							=      [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]						,   lowercase_     :							int							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				initial_vectors
				for _ in range(lowercase_							):
								_lowerCamelCase        =				iteration_step(lowercase_							)
				return vectors



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				[]
				for i, start_vector in enumerate(vectors[:-1]							):
								_lowerCamelCase        =				vectors[i + 1]
								new_vectors.append(lowercase_							)
								_lowerCamelCase        =				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        lowerCAmelCase_(   lowercase_     :							numpy.ndarray						,   lowercase_     :							float							)	->       numpy.ndarray:
				_lowerCamelCase        =				numpy.radians(lowercase_							)
				_lowerCamelCase  ,     _lowerCamelCase        =				numpy.cos(lowercase_							), numpy.sin(lowercase_							)
				_lowerCamelCase        =				numpy.array(((c, -s), (s, c))							)
				return numpy.dot(lowercase_						,   lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       None:
				_lowerCamelCase        =				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()
				_lowerCamelCase  ,     _lowerCamelCase        =				zip(*lowercase_							)
				plt.plot(lowercase_						,   lowercase_							)
				plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__SCREAMING_SNAKE_CASE  :						str    							=      iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)
