


from __future__ import annotations

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

# initial triangle of Koch snowflake
UpperCAmelCase_      : Tuple  					=       numpy.array([0, 0])
UpperCAmelCase_      : Any  					=       numpy.array([0.5, 0.8_6_6_0_2_5_4])
UpperCAmelCase_      : Tuple  					=       numpy.array([1, 0])
UpperCAmelCase_      : Optional[int]  					=       [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def        SCREAMING_SNAKE_CASE_						(			__A :			list[numpy.ndarray]  ,    __A :			int				)	->		list[numpy.ndarray]:

       """simple docstring"""

       a_					:		Tuple							=  initial_vectors
       for _ in range(__A				):
              a_					:		Tuple							=  iteration_step(__A				)
       return vectors







def        SCREAMING_SNAKE_CASE_						(			__A :			list[numpy.ndarray]				)	->		list[numpy.ndarray]:

       """simple docstring"""

       a_					:		Optional[int]							=  []
       for i, start_vector in enumerate(vectors[:-1]				):
              a_					:		int							=  vectors[i + 1]
              new_vectors.append(__A				)
              a_					:		List[str]							=  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 :			numpy.ndarray  ,    __A :			float				)	->		numpy.ndarray:

       """simple docstring"""

       a_					:		Tuple							=  numpy.radians(__A				)
       a_							,		a_					:		List[str]							=  numpy.cos(__A				), numpy.sin(__A				)
       a_					:		Optional[int]							=  numpy.array(((c, -s), (s, c))				)
       return numpy.dot(__A  ,    __A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			list[numpy.ndarray]				)	->		None:

       """simple docstring"""

       a_					:		int							=  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()
       a_							,		a_					:		Any							=  zip(*__A				)
       plt.plot(__A  ,    __A				)
       plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_      : List[str]  					=       iterate(INITIAL_VECTORS, 5)
   plot(processed_vectors)






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


UpperCAmelCase_      : Optional[Any]  					=       logging.get_logger(__name__)

UpperCAmelCase_      : str  					=       'T5Config'







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.array  ,    __A :			int  ,    __A :			int				)	->		jnp.ndarray:

       """simple docstring"""

       a_					:		Dict							=  jnp.zeros_like(__A				)
       a_					:		Dict							=  shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]				)
       a_					:		str							=  shifted_input_ids.at[:, 0].set(__A				)

       a_					:		int							=  jnp.where(shifted_input_ids == -1_00  ,    __A  ,    __A				)
       return shifted_input_ids
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     str        =						'''mt5'''
      snake_case__				:     List[Any]        =						MTaConfig
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     str        =						'''mt5'''
      snake_case__				:     List[str]        =						MTaConfig
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Any        =						'''mt5'''
      snake_case__				:     Union[str, Any]        =						MTaConfig


