


'''simple docstring'''



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
			if (
			    any("""norm""" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__  :					Any        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						UpperCAmelCase__  :					Any        =     pt_tensor.transpose(2 ,      3 ,      1 ,      0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__  :					Optional[Any]        =     pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			UpperCAmelCase__  :					str        =     flax_model.init_weights(PRNGKey(UpperCamelCase__					)					)

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     rename_key_and_reshape_tensor(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)



'''simple docstring'''



from __future__ import annotations

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

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     initial_vectors
			for _ in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     iteration_step(UpperCamelCase__					)
			return vectors







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     []
			for i, start_vector in enumerate(vectors[:-1]					):
						UpperCAmelCase__  :					Tuple        =     vectors[i + 1]
						new_vectors.append(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[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 ,      6_0					)					)
						new_vectors.append(start_vector + difference_vector * 2 / 3					)
			new_vectors.append(vectors[-1]					)
			return new_vectors







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     numpy.radians(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     numpy.cos(UpperCamelCase__					), numpy.sin(UpperCamelCase__					)
			UpperCAmelCase__  :					Dict        =     numpy.array(((c, -s), (s, c))					)
			return numpy.dot(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     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()
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     zip(*UpperCamelCase__					)
			plt.plot(UpperCamelCase__ ,      UpperCamelCase__					)
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

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