






"""simple docstring"""





from __future__ import annotations

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

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	list[numpy.ndarray]:
    """simple docstring"""

    UpperCamelCase__         =    initial_vectors
    for _ in range(SCREAMING_SNAKE_CASE_					):
        UpperCamelCase__         =    iteration_step(SCREAMING_SNAKE_CASE_					)
    return vectors







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[numpy.ndarray]:
    """simple docstring"""

    UpperCamelCase__         =    []
    for i, start_vector in enumerate(vectors[:-1]					):
        UpperCamelCase__         =    vectors[i + 1]
        new_vectors.append(SCREAMING_SNAKE_CASE_					)
        UpperCamelCase__         =    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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	numpy.ndarray:
    """simple docstring"""

    UpperCamelCase__         =    numpy.radians(SCREAMING_SNAKE_CASE_					)
    UpperCamelCase__         =    numpy.cos(SCREAMING_SNAKE_CASE_					), numpy.sin(SCREAMING_SNAKE_CASE_					)
    UpperCamelCase__         =    numpy.array(((c, -s), (s, c))					)
    return numpy.dot(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	None:
    """simple docstring"""

    UpperCamelCase__         =    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__         =    zip(*SCREAMING_SNAKE_CASE_					)
    plt.plot(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_					)
    plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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








"""simple docstring"""





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


A__					: List[Any]=	["""bert-base-uncased""", """bert-base-cased"""]
A__					: Optional[int]=	"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():
  class    __lowerCamelCase    (  tf.keras.Model  ):

     def __init__(					self ,				snake_case_      )		->       Optional[int]:
         super().__init__()
         UpperCamelCase__         =    tokenizer
         UpperCamelCase__         =    AutoConfig.from_pretrained(snake_case_      )
         UpperCamelCase__         =    TFAutoModel.from_config(snake_case_      )







     def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
         UpperCamelCase__         =    self.tokenizer(snake_case_      )
         UpperCamelCase__         =    self.bert(**snake_case_      )
         return out["pooler_output"]
@require_tf
@require_tensorflow_text
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       super().setUp()

       UpperCamelCase__         =    [
           BertTokenizer.from_pretrained(snake_case_      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
       ]  # repeat for when fast_bert_tokenizer=false
       UpperCamelCase__         =    [TFBertTokenizer.from_pretrained(snake_case_      ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
           TFBertTokenizer.from_pretrained(snake_case_ ,				use_fast_bert_tokenizer=snake_case_      )
           for checkpoint in TOKENIZER_CHECKPOINTS
       ]
       assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

       UpperCamelCase__         =    [
           'This is a straightforward English test sentence.',
           'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
           'Now we\'re going to add some Chinese: 一 二 三 一二三',
           'And some much more rare Chinese: 齉 堃 齉堃',
           'Je vais aussi écrire en français pour tester les accents',
           'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
       ]
       UpperCamelCase__         =    list(zip(self.test_sentences ,				self.test_sentences[::-1]      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       for tokenizer, tf_tokenizer in zip(self.tokenizers ,				self.tf_tokenizers      ):
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tokenizer(snake_case_ ,				return_tensors='tf' ,				padding='longest'      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in python_outputs.keys():
                   self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape      )      )
                   self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,				tf.intaa      ) == tf_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf_tokenizer(self.paired_sentences      )
           UpperCamelCase__         =    tf_tokenizer(
               text=[sentence[0] for sentence in self.paired_sentences] ,				text_pair=[sentence[1] for sentence in self.paired_sentences] ,				)
           for key in merged_outputs.keys():
               self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,				tf.intaa      ) == separated_outputs[key]      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    tf.function(snake_case_      )
           for test_inputs in (self.test_sentences, self.paired_sentences):
               UpperCamelCase__         =    tf.constant(snake_case_      )
               UpperCamelCase__         =    compiled_tokenizer(snake_case_      )
               UpperCamelCase__         =    tf_tokenizer(snake_case_      )

               for key in eager_outputs.keys():
                   self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       for tf_tokenizer in self.tf_tokenizers:
           UpperCamelCase__         =    ModelToSave(tokenizer=snake_case_      )
           UpperCamelCase__         =    tf.convert_to_tensor(self.test_sentences      )
           UpperCamelCase__         =    model(snake_case_      )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
               UpperCamelCase__         =    Path(snake_case_      ) / 'saved.model'
               model.save(snake_case_      )
               UpperCamelCase__         =    tf.keras.models.load_model(snake_case_      )
           UpperCamelCase__         =    loaded_model(snake_case_      )
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output      )      ) ,				1E-5      )




