'''simple docstring'''






import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
    from transformers.models.rag.configuration_rag import RagConfig
    from transformers.models.rag.tokenization_rag import RagTokenizer




@require_faiss
@require_torch
class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= tempfile.mkdtemp()
    __UpperCAmelCase:					List[str]		= 8

    # DPR tok
    __UpperCAmelCase:					List[Any]		= [
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    __UpperCAmelCase:					int		= os.path.join(self.tmpdirname   ,    """dpr_tokenizer"""					)
    os.makedirs(snake_case_   ,    exist_ok=snake_case_					)
    __UpperCAmelCase:					Optional[int]		= os.path.join(snake_case_   ,    DPR_VOCAB_FILES_NAMES["""vocab_file"""]					)
    with open(self.vocab_file   ,    """w"""   ,    encoding="""utf-8"""					) as vocab_writer:
      vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

    # BART tok
    __UpperCAmelCase:					Optional[int]		= [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
    ]
    __UpperCAmelCase:					Any		= dict(zip(snake_case_   ,    range(len(snake_case_					)					)					)					)
    __UpperCAmelCase:					List[Any]		= ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    __UpperCAmelCase:					List[Any]		= {"""unk_token""": """<unk>"""}

    __UpperCAmelCase:					Tuple		= os.path.join(self.tmpdirname   ,    """bart_tokenizer"""					)
    os.makedirs(snake_case_   ,    exist_ok=snake_case_					)
    __UpperCAmelCase:					List[str]		= os.path.join(snake_case_   ,    BART_VOCAB_FILES_NAMES["""vocab_file"""]					)
    __UpperCAmelCase:					List[Any]		= os.path.join(snake_case_   ,    BART_VOCAB_FILES_NAMES["""merges_file"""]					)
    with open(self.vocab_file   ,    """w"""   ,    encoding="""utf-8"""					) as fp:
      fp.write(json.dumps(snake_case_					) + """\n"""					)
    with open(self.merges_file   ,    """w"""   ,    encoding="""utf-8"""					) as fp:
      fp.write("""\n""".join(snake_case_					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,    """dpr_tokenizer"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,    """bart_tokenizer"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    shutil.rmtree(self.tmpdirname					)







  @require_tokenizers
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= os.path.join(self.tmpdirname   ,    """rag_tokenizer"""					)
    __UpperCAmelCase:					Optional[Any]		= RagConfig(question_encoder=DPRConfig().to_dict()   ,    generator=BartConfig().to_dict()					)
    __UpperCAmelCase:					Tuple		= RagTokenizer(question_encoder=self.get_dpr_tokenizer()   ,    generator=self.get_bart_tokenizer()					)
    rag_config.save_pretrained(snake_case_					)
    rag_tokenizer.save_pretrained(snake_case_					)
    __UpperCAmelCase:					List[str]		= RagTokenizer.from_pretrained(snake_case_   ,    config=snake_case_					)
    self.assertIsInstance(new_rag_tokenizer.question_encoder   ,    snake_case_					)
    self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()   ,    rag_tokenizer.question_encoder.get_vocab()					)
    self.assertIsInstance(new_rag_tokenizer.generator   ,    snake_case_					)
    self.assertEqual(new_rag_tokenizer.generator.get_vocab()   ,    rag_tokenizer.generator.get_vocab()					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= RagTokenizer.from_pretrained("""facebook/rag-token-nq"""					)
    __UpperCAmelCase:					str		= [
        """who got the first nobel prize in physics""",
        """when is the next deadpool movie being released""",
        """which mode is used for short wave broadcast service""",
        """who is the owner of reading football club""",
        """when is the next scandal episode coming out""",
        """when is the last time the philadelphia won the superbowl""",
        """what is the most current adobe flash player version""",
        """how many episodes are there in dragon ball z""",
        """what is the first step in the evolution of the eye""",
        """where is gall bladder situated in human body""",
        """what is the main mineral in lithium batteries""",
        """who is the president of usa right now""",
        """where do the greasers live in the outsiders""",
        """panda is a national animal of which country""",
        """what is the name of manchester united stadium""",
    ]
    __UpperCAmelCase:					List[str]		= tokenizer(snake_case_					)
    self.assertIsNotNone(snake_case_					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""					)
    __UpperCAmelCase:					List[str]		= [
        """who got the first nobel prize in physics""",
        """when is the next deadpool movie being released""",
        """which mode is used for short wave broadcast service""",
        """who is the owner of reading football club""",
        """when is the next scandal episode coming out""",
        """when is the last time the philadelphia won the superbowl""",
        """what is the most current adobe flash player version""",
        """how many episodes are there in dragon ball z""",
        """what is the first step in the evolution of the eye""",
        """where is gall bladder situated in human body""",
        """what is the main mineral in lithium batteries""",
        """who is the president of usa right now""",
        """where do the greasers live in the outsiders""",
        """panda is a national animal of which country""",
        """what is the name of manchester united stadium""",
    ]
    __UpperCAmelCase:					Dict		= tokenizer(snake_case_					)
    self.assertIsNotNone(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)