'''simple docstring'''






from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers.models.layoutlm.modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMModel,
    )




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=13   ,    snake_case_=7   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=99   ,    snake_case_=32   ,    snake_case_=2   ,    snake_case_=4   ,    snake_case_=37   ,    snake_case_="gelu"   ,    snake_case_=0.1   ,    snake_case_=0.1   ,    snake_case_=512   ,    snake_case_=16   ,    snake_case_=2   ,    snake_case_=0.0_2   ,    snake_case_=3   ,    snake_case_=4   ,    snake_case_=None   ,    snake_case_=1000   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= parent
    __UpperCAmelCase:					Optional[int]		= batch_size
    __UpperCAmelCase:					List[Any]		= seq_length
    __UpperCAmelCase:					Dict		= is_training
    __UpperCAmelCase:					Optional[int]		= use_input_mask
    __UpperCAmelCase:					Optional[int]		= use_token_type_ids
    __UpperCAmelCase:					Tuple		= use_labels
    __UpperCAmelCase:					Dict		= vocab_size
    __UpperCAmelCase:					List[Any]		= hidden_size
    __UpperCAmelCase:					Optional[Any]		= num_hidden_layers
    __UpperCAmelCase:					Optional[Any]		= num_attention_heads
    __UpperCAmelCase:					str		= intermediate_size
    __UpperCAmelCase:					str		= hidden_act
    __UpperCAmelCase:					Tuple		= hidden_dropout_prob
    __UpperCAmelCase:					Optional[int]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Optional[int]		= max_position_embeddings
    __UpperCAmelCase:					Tuple		= type_vocab_size
    __UpperCAmelCase:					str		= type_sequence_label_size
    __UpperCAmelCase:					int		= initializer_range
    __UpperCAmelCase:					int		= num_labels
    __UpperCAmelCase:					Union[str, Any]		= num_choices
    __UpperCAmelCase:					Union[str, Any]		= scope
    __UpperCAmelCase:					Union[str, Any]		= range_bbox







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size					)

    # convert bbox to numpy since TF does not support item assignment
    __UpperCAmelCase:					Optional[int]		= ids_tensor([self.batch_size, self.seq_length, 4]   ,    self.range_bbox					).numpy()
    # Ensure that bbox is legal
    for i in range(bbox.shape[0]					):
      for j in range(bbox.shape[1]					):
        if bbox[i, j, 3] < bbox[i, j, 1]:
          __UpperCAmelCase:					int		= bbox[i, j, 3]
          __UpperCAmelCase:					Optional[Any]		= bbox[i, j, 1]
          __UpperCAmelCase:					List[Any]		= t
        if bbox[i, j, 2] < bbox[i, j, 0]:
          __UpperCAmelCase:					int		= bbox[i, j, 2]
          __UpperCAmelCase:					Union[str, Any]		= bbox[i, j, 0]
          __UpperCAmelCase:					Optional[Any]		= t
    __UpperCAmelCase:					Optional[int]		= tf.convert_to_tensor(snake_case_					)

    __UpperCAmelCase:					Any		= None
    if self.use_input_mask:
      __UpperCAmelCase:					List[str]		= random_attention_mask([self.batch_size, self.seq_length]					)

    __UpperCAmelCase:					Any		= None
    if self.use_token_type_ids:
      __UpperCAmelCase:					Optional[int]		= ids_tensor([self.batch_size, self.seq_length]   ,    self.type_vocab_size					)

    __UpperCAmelCase:					Any		= None
    __UpperCAmelCase:					Dict		= None
    __UpperCAmelCase:					str		= None
    if self.use_labels:
      __UpperCAmelCase:					str		= ids_tensor([self.batch_size]   ,    self.type_sequence_label_size					)
      __UpperCAmelCase:					Optional[int]		= ids_tensor([self.batch_size, self.seq_length]   ,    self.num_labels					)
      __UpperCAmelCase:					Optional[int]		= ids_tensor([self.batch_size]   ,    self.num_choices					)

    __UpperCAmelCase:					List[Any]		= LayoutLMConfig(
        vocab_size=self.vocab_size   ,    hidden_size=self.hidden_size   ,    num_hidden_layers=self.num_hidden_layers   ,    num_attention_heads=self.num_attention_heads   ,    intermediate_size=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    hidden_dropout_prob=self.hidden_dropout_prob   ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    type_vocab_size=self.type_vocab_size   ,    initializer_range=self.initializer_range   ,    )

    return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= TFLayoutLMModel(config=snake_case_					)

    __UpperCAmelCase:					List[Any]		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)
    __UpperCAmelCase:					Tuple		= model(snake_case_   ,    snake_case_   ,    token_type_ids=snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= model(snake_case_   ,    snake_case_					)
    self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)					)
    self.parent.assertEqual(result.pooler_output.shape   ,    (self.batch_size, self.hidden_size)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= TFLayoutLMForMaskedLM(config=snake_case_					)

    __UpperCAmelCase:					List[str]		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_   ,    labels=snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.vocab_size)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.num_labels
    __UpperCAmelCase:					Tuple		= TFLayoutLMForSequenceClassification(config=snake_case_					)

    __UpperCAmelCase:					int		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.num_labels
    __UpperCAmelCase:					int		= TFLayoutLMForTokenClassification(config=snake_case_					)

    __UpperCAmelCase:					Any		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_   ,    labels=snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.num_labels)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= TFLayoutLMForQuestionAnswering(config=snake_case_					)

    __UpperCAmelCase:					Optional[Any]		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)
    self.parent.assertEqual(result.start_logits.shape   ,    (self.batch_size, self.seq_length)					)
    self.parent.assertEqual(result.end_logits.shape   ,    (self.batch_size, self.seq_length)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.prepare_config_and_inputs()
    (
        (
        __UpperCAmelCase
    ), (
        __UpperCAmelCase
    ), (
        __UpperCAmelCase
    ), (
        __UpperCAmelCase
    ), (
        __UpperCAmelCase
    ), (
        __UpperCAmelCase
    ), (
        __UpperCAmelCase
    ), (
        __UpperCAmelCase
    ), 
    ):					Union[str, Any]		= config_and_inputs
    __UpperCAmelCase:					Dict		= {
        """input_ids""": input_ids,
        """bbox""": bbox,
        """token_type_ids""": token_type_ids,
        """attention_mask""": input_mask,
    }
    return config, inputs_dict




@require_tf
class    a (       __lowerCAmelCase						,  __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					(
      (
          TFLayoutLMModel,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForTokenClassification,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForQuestionAnswering,
      )
      if is_tf_available()
      else ()
  )
  __lowerCAmelCase				      =					(
      {
          """feature-extraction""": TFLayoutLMModel,
          """fill-mask""": TFLayoutLMForMaskedLM,
          """text-classification""": TFLayoutLMForSequenceClassification,
          """token-classification""": TFLayoutLMForTokenClassification,
          """zero-shot""": TFLayoutLMForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					1_0







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= TFLayoutLMModelTester(self					)
    __UpperCAmelCase:					Optional[int]		= ConfigTester(self   ,    config_class=snake_case_   ,    hidden_size=37					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    self.config_tester.run_common_tests()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*snake_case_					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __UpperCAmelCase:					Tuple		= TFLayoutLMModel.from_pretrained(snake_case_					)
      self.assertIsNotNone(snake_case_					)







  @unittest.skip("""Onnx compliancy broke with TF 2.10"""					)
  def    lowercase_  (							self					):

    '''simple docstring'''







    pass






def  UpperCamelCase__					(					)							->	Tuple:
  # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  # fmt: off
  __UpperCAmelCase:					Union[str, Any]		= tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]     )  # noqa: E231
  __UpperCAmelCase:					Any		= tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]     )  # noqa: E231
  __UpperCAmelCase:					Optional[Any]		= tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]     )  # noqa: E231
  __UpperCAmelCase:					List[Any]		= tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]     )  # noqa: E231
  # these are sequence labels (i.e. at the token level)
  __UpperCAmelCase:					List[Any]		= tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]     )  # noqa: E231
  # fmt: on

  return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""					)

    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					List[str]		= prepare_layoutlm_batch_inputs()

    # forward pass
    __UpperCAmelCase:					Dict		= model(input_ids=snake_case_   ,    bbox=snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)

    # test the sequence output on [0, :3, :3]
    __UpperCAmelCase:					Tuple		= tf.convert_to_tensor(
        [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]   ,    )

    self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]   ,    snake_case_   ,    atol=1e-3					)					)

    # test the pooled output on [1, :3]
    __UpperCAmelCase:					Union[str, Any]		= tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]					)

    self.assertTrue(np.allclose(outputs.pooler_output[1, :3]   ,    snake_case_   ,    atol=1e-3					)					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""   ,    num_labels=2					)

    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= prepare_layoutlm_batch_inputs()

    # forward pass
    __UpperCAmelCase:					Any		= model(
        input_ids=snake_case_   ,    bbox=snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_   ,    labels=tf.convert_to_tensor([1, 1]					)   ,    )

    # test whether we get a loss as a scalar
    __UpperCAmelCase:					int		= outputs.loss
    __UpperCAmelCase:					Dict		= (2,)
    self.assertEqual(loss.shape   ,    snake_case_					)

    # test the shape of the logits
    __UpperCAmelCase:					Tuple		= outputs.logits
    __UpperCAmelCase:					Tuple		= (2, 2)
    self.assertEqual(logits.shape   ,    snake_case_					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""   ,    num_labels=13					)

    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					List[str]		= prepare_layoutlm_batch_inputs()

    # forward pass
    __UpperCAmelCase:					int		= model(
        input_ids=snake_case_   ,    bbox=snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_   ,    labels=snake_case_					)

    # test the shape of the logits
    __UpperCAmelCase:					Optional[int]		= outputs.logits
    __UpperCAmelCase:					Optional[int]		= tf.convert_to_tensor((2, 25, 13)					)
    self.assertEqual(logits.shape   ,    snake_case_					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""					)

    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					Any		= prepare_layoutlm_batch_inputs()

    # forward pass
    __UpperCAmelCase:					List[str]		= model(input_ids=snake_case_   ,    bbox=snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)

    # test the shape of the logits
    __UpperCAmelCase:					Dict		= tf.convert_to_tensor((2, 25)					)
    self.assertEqual(outputs.start_logits.shape   ,    snake_case_					)
    self.assertEqual(outputs.end_logits.shape   ,    snake_case_					)
'''simple docstring'''






from __future__ import annotations

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

# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE_	   =			numpy.array([0, 0])
SCREAMING_SNAKE_CASE_	   =			numpy.array([0.5, 0.866_0254])
SCREAMING_SNAKE_CASE_	   =			numpy.array([1, 0])
SCREAMING_SNAKE_CASE_	   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]   ,    _lowercase    :				int     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					int		= initial_vectors
  for _ in range(_lowercase     ):
    __UpperCAmelCase:					Any		= iteration_step(_lowercase     )
  return vectors






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






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	None:
  __UpperCAmelCase:					Union[str, 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:					Dict		= zip(*_lowercase     )
  plt.plot(_lowercase   ,    _lowercase     )
  plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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