






"""simple docstring"""





from __future__ import annotations

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

# initial triangle of Koch snowflake
UpperCamelCase_	:				Any										= numpy.array([0, 0])
UpperCamelCase_	:				List[Any]										= numpy.array([0.5, 0.8_66_02_54])
UpperCamelCase_	:				int										= numpy.array([1, 0])
UpperCamelCase_	:				List[str]										= [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def     __lowercase					(    a :				list[numpy.ndarray]       ,		a :				int )					->					list[numpy.ndarray]:
      __snake_case      :    str  			=initial_vectors
      for _ in range(a ):
            __snake_case      :    List[str]  			=iteration_step(a )
      return vectors



def     __lowercase					(    a :				list[numpy.ndarray] )					->					list[numpy.ndarray]:
      __snake_case      :    Tuple  			=[]
      for i, start_vector in enumerate(vectors[:-1] ):
            __snake_case      :    int  			=vectors[i + 1]
            new_vectors.append(a )
            __snake_case      :    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     __lowercase					(    a :				numpy.ndarray       ,		a :				float )					->					numpy.ndarray:
      __snake_case      :    Union[str, Any]  			=numpy.radians(a )
      __snake_case			,				__snake_case      :    str  			=numpy.cos(a ), numpy.sin(a )
      __snake_case      :    List[Any]  			=numpy.array(((c, -s), (s, c)) )
      return numpy.dot(a       ,		a )



def     __lowercase					(    a :				list[numpy.ndarray] )					->					None:
      __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      :    Dict  			=zip(*a )
      plt.plot(a       ,		a )
      plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase_	:				Tuple										= iterate(INITIAL_VECTORS, 5)
    plot(processed_vectors)










"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_	:				Dict										= logging.get_logger(__name__)

UpperCamelCase_	:				Dict										= {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}


class      _lowercase (     lowerCAmelCase						):
      _a   :     Union[str, Any]								=   '''xmod'''


      def __init__(    self				:	Optional[int]							,   a				:	Dict=3_0_5_2_2							,   a				:	Any=7_6_8							,   a				:	str=1_2							,   a				:	Optional[int]=1_2							,   a				:	Any=3_0_7_2							,   a				:	int="gelu"							,   a				:	List[Any]=0.1							,   a				:	Union[str, Any]=0.1							,   a				:	Tuple=5_1_2							,   a				:	str=2							,   a				:	Any=0.0_2							,   a				:	Tuple=1e-12							,   a				:	Optional[int]=1							,   a				:	Any=0							,   a				:	str=2							,   a				:	str="absolute"							,   a				:	List[Any]=True							,   a				:	Optional[Any]=None							,   a				:	Tuple=False							,   a				:	Union[str, Any]=2							,   a				:	Any=False							,   a				:	Dict=True							,   a				:	int=True							,   a				:	str=("en_XX",)							,   a				:	Dict=None							,   **a				:	Tuple							,   ):





            """simple docstring"""







            super().__init__(pad_token_id=a							,   bos_token_id=a							,   eos_token_id=a							,   **a						)

            __snake_case      :    Union[str, Any]  			=vocab_size
            __snake_case      :    Union[str, Any]  			=hidden_size
            __snake_case      :    int  			=num_hidden_layers
            __snake_case      :    Union[str, Any]  			=num_attention_heads
            __snake_case      :    Any  			=hidden_act
            __snake_case      :    Tuple  			=intermediate_size
            __snake_case      :    int  			=hidden_dropout_prob
            __snake_case      :    str  			=attention_probs_dropout_prob
            __snake_case      :    int  			=max_position_embeddings
            __snake_case      :    Tuple  			=type_vocab_size
            __snake_case      :    Union[str, Any]  			=initializer_range
            __snake_case      :    Dict  			=layer_norm_eps
            __snake_case      :    Optional[int]  			=position_embedding_type
            __snake_case      :    int  			=use_cache
            __snake_case      :    int  			=classifier_dropout
            __snake_case      :    int  			=pre_norm
            __snake_case      :    Any  			=adapter_reduction_factor
            __snake_case      :    str  			=adapter_layer_norm
            __snake_case      :    Union[str, Any]  			=adapter_reuse_layer_norm
            __snake_case      :    List[str]  			=ln_before_adapter
            __snake_case      :    Optional[int]  			=list(a						)
            __snake_case      :    int  			=default_language



class      _lowercase (     lowerCAmelCase						):


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            if self.task == "multiple-choice":
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
            else:
                  __snake_case      :    Tuple  			={0: '''batch''', 1: '''sequence'''}
            return OrderedDict(
                [
                    ('''input_ids''', dynamic_axis),
                    ('''attention_mask''', dynamic_axis),
                ]						)


