



'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCamelCase__:   Optional[Any]								=			logging.get_logger(__name__)

UpperCamelCase__:   int								=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__:   Tuple								=			{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCamelCase__:   List[Any]								=			{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


UpperCamelCase__:   List[Any]								=			{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       PRETRAINED_INIT_CONFIGURATION
	lowerCamelCase__			 =       RoFormerTokenizer







	def __init__(						self			:     Optional[Any]		,			__snake_case			:     Optional[int]=None		,			__snake_case			:     Optional[int]=None		,			__snake_case			:     List[Any]=True		,			__snake_case			:     Optional[Any]="[UNK]"		,			__snake_case			:     List[str]="[SEP]"		,			__snake_case			:     str="[PAD]"		,			__snake_case			:     str="[CLS]"		,			__snake_case			:     List[str]="[MASK]"		,			__snake_case			:     Optional[Any]=True		,			__snake_case			:     List[Any]=None		,			**__snake_case			:     Union[str, Any]		,			)			->		Union[str, Any]:
								super().__init__(
								    __snake_case		,			tokenizer_file=__snake_case		,			do_lower_case=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			pad_token=__snake_case		,			cls_token=__snake_case		,			mask_token=__snake_case		,			tokenize_chinese_chars=__snake_case		,			strip_accents=__snake_case		,			**__snake_case		,			)

								UpperCAmelCase :       List[str] 							=		json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
								if (
								    pre_tok_state.get('''lowercase'''		,			__snake_case		) != do_lower_case
								    or pre_tok_state.get('''strip_accents'''		,			__snake_case		) != strip_accents
								):
															UpperCAmelCase :       List[str] 							=		getattr(__snake_case		,			pre_tok_state.pop('''type'''		)		)
															UpperCAmelCase :       Union[str, Any] 							=		do_lower_case
															UpperCAmelCase :       int 							=		strip_accents
															UpperCAmelCase :       Optional[int] 							=		pre_tok_class(**__snake_case		)

								UpperCAmelCase :       int 							=		do_lower_case







	def __getstate__(						self			:     Optional[int]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		self.__dict__.copy()
								UpperCAmelCase :       List[Any] 							=		BertPreTokenizer()
								return state







	def __setstate__(						self			:     Tuple		,			__snake_case			:     int		)			->		Optional[int]:
								UpperCAmelCase :       Tuple 							=		d
								UpperCAmelCase :       List[Any] 							=		self.__dict__['''_tokenizer'''].get_vocab()
								UpperCAmelCase :       Tuple 							=		PreTokenizer.custom(JiebaPreTokenizer(__snake_case		)		)







	def      A	(						self			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     str=None		)			->		Optional[int]:
								UpperCAmelCase :       int 							=		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       List[Any] 							=		[self.sep_token_id]
								UpperCAmelCase :       str 							=		[self.cls_token_id]
								if token_ids_a is None:
															return len(cls + token_ids_a + sep		) * [0]
								return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







	def      A	(						self			:     Tuple		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								UpperCAmelCase :       Optional[Any] 							=		self._tokenizer.model.save(__snake_case		,			name=__snake_case		)
								return tuple(__snake_case		)







	def      A	(						self			:     str		,			__snake_case			:     List[str]		,			__snake_case			:     int=None		,			__snake_case			:     List[str]=None		,			__snake_case			:     Union[str, Any]=False		,			**__snake_case			:     List[Any]		,			)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		BertPreTokenizer()
								return super().save_pretrained(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			**__snake_case		)










'''simple docstring'''






from __future__ import annotations

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

# initial triangle of Koch snowflake
UpperCamelCase__:   Tuple								=			numpy.array([0, 0])
UpperCamelCase__:   Union[str, Any]								=			numpy.array([0.5, 0.8660254])
UpperCamelCase__:   Dict								=			numpy.array([1, 0])
UpperCamelCase__:   int								=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]   ,      _lowerCAmelCase  :						int  )      ->   list[numpy.ndarray]:
							UpperCAmelCase :       Union[str, Any] 							=		initial_vectors
							for _ in range(_lowerCAmelCase  ):
														UpperCAmelCase :       Union[str, Any] 							=		iteration_step(_lowerCAmelCase  )
							return vectors


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


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]  )      ->   None:
							UpperCAmelCase :       List[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 :       str 							=		zip(*_lowerCAmelCase  )
							plt.plot(_lowerCAmelCase   ,      _lowerCAmelCase  )
							plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						UpperCamelCase__:   List[Any]								=			iterate(INITIAL_VECTORS, 5)
						plot(processed_vectors)





