

from __future__ import annotations

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

# initial triangle of Koch snowflake
lowercase_   							=  numpy.array([0, 0])
lowercase_   							=  numpy.array([0.5, 0.8_6_6_0_2_5_4])
lowercase_   							=  numpy.array([1, 0])
lowercase_   							=  [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def 	a (     A__      :  list[numpy.ndarray] ,  A__      :  int	)       -> list[numpy.ndarray]:





      """simple docstring"""



      _lowercase      			=initial_vectors
      for _ in range(A__	):
            _lowercase      			=iteration_step(A__	)
      return vectors
def 	a (     A__      :  list[numpy.ndarray]	)       -> list[numpy.ndarray]:





      """simple docstring"""



      _lowercase      			=[]
      for i, start_vector in enumerate(vectors[:-1]	):
            _lowercase      			=vectors[i + 1]
            new_vectors.append(A__	)
            _lowercase      			=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 	a (     A__      :  numpy.ndarray ,  A__      :  float	)       -> numpy.ndarray:





      """simple docstring"""



      _lowercase      			=numpy.radians(A__	)
      _lowercase			,			_lowercase      			=numpy.cos(A__	), numpy.sin(A__	)
      _lowercase      			=numpy.array(((c, -s), (s, c))	)
      return numpy.dot(A__ ,  A__	)
def 	a (     A__      :  list[numpy.ndarray]	)       -> None:





      """simple docstring"""



      _lowercase      			=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()
      _lowercase			,			_lowercase      			=zip(*A__	)
      plt.plot(A__ ,  A__	)
      plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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





from collections import OrderedDict
from typing import Mapping

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


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """distilbert"""
  _a		     =    {
      """hidden_size""": """dim""",
      """num_attention_heads""": """n_heads""",
      """num_hidden_layers""": """n_layers""",
  }




  def __init__(							self				,  lowerCAmelCase=30_522				,  lowerCAmelCase=512				,  lowerCAmelCase=False				,  lowerCAmelCase=6				,  lowerCAmelCase=12				,  lowerCAmelCase=768				,  lowerCAmelCase=4 * 768				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.1				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.02				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.2				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=vocab_size
        _lowercase      			=max_position_embeddings
        _lowercase      			=sinusoidal_pos_embds
        _lowercase      			=n_layers
        _lowercase      			=n_heads
        _lowercase      			=dim
        _lowercase      			=hidden_dim
        _lowercase      			=dropout
        _lowercase      			=attention_dropout
        _lowercase      			=activation
        _lowercase      			=initializer_range
        _lowercase      			=qa_dropout
        _lowercase      			=seq_classif_dropout
        super().__init__(**lowerCAmelCase				,  pad_token_id=lowerCAmelCase   )





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






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


