
"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	"""▁"""

lowerCamelCase__           =	{"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCamelCase__           =	{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCamelCase__           =	{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}




class 				A__ (			_lowerCamelCase):
		A_		: List[Any]            =			VOCAB_FILES_NAMES
		A_		: List[str]            =			PRETRAINED_VOCAB_FILES_MAP
		A_		: Optional[Any]            =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_		: Tuple            =			['input_ids', 'attention_mask']

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="<unk>" ,	_SCREAMING_SNAKE_CASE="<pad>" ,	_SCREAMING_SNAKE_CASE="<mask>" ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			# Mask token behave like a normal word, i.e. include the space before it
			__lowerCAmelCase    :		Dict		    =   AddedToken(_SCREAMING_SNAKE_CASE ,	lstrip=_SCREAMING_SNAKE_CASE ,	rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) else mask_token

			__lowerCAmelCase    :		Tuple		    =   {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=_SCREAMING_SNAKE_CASE ,	eos_token=_SCREAMING_SNAKE_CASE ,	unk_token=_SCREAMING_SNAKE_CASE ,	sep_token=_SCREAMING_SNAKE_CASE ,	cls_token=_SCREAMING_SNAKE_CASE ,	pad_token=_SCREAMING_SNAKE_CASE ,	mask_token=_SCREAMING_SNAKE_CASE ,	sp_model_kwargs=self.sp_model_kwargs ,	**_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Tuple		    =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
			self.sp_model.Load(str(_SCREAMING_SNAKE_CASE				)				)
			__lowerCAmelCase    :		List[str]		    =   vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# Mimic fairseq token-to-id alignment for the first 4 token
			__lowerCAmelCase    :		Any		    =   {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			__lowerCAmelCase    :		Union[str, Any]		    =   1

			__lowerCAmelCase    :		List[str]		    =   len(self.sp_model				) + self.fairseq_offset
			__lowerCAmelCase    :		Union[str, Any]		    =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

		def __getstate__(   self				):
			__lowerCAmelCase    :		Dict		    =   self.__dict__.copy()
			__lowerCAmelCase    :		Any		    =   None
			__lowerCAmelCase    :		List[str]		    =   self.sp_model.serialized_model_proto()
			return state

		def __setstate__(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   d

			# for backward compatibility
			if not hasattr(self ,	'sp_model_kwargs'				):
				__lowerCAmelCase    :		Optional[int]		    =   {}

			__lowerCAmelCase    :		Any		    =   spm.SentencePieceProcessor(**self.sp_model_kwargs				)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):

			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			__lowerCAmelCase    :		str		    =   [self.cls_token_id]
			__lowerCAmelCase    :		Union[str, Any]		    =   [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False				):

			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=_SCREAMING_SNAKE_CASE ,	token_ids_a=_SCREAMING_SNAKE_CASE ,	already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

			if token_ids_a is None:
				return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
			return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		Tuple		    =   [self.sep_token_id]
			__lowerCAmelCase    :		Optional[Any]		    =   [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 + sep + token_ids_a + sep				) * [0]

		@property
		def 							__lowerCamelCase							(   self				):
			return len(self.sp_model				) + self.fairseq_offset + 1  # Add the <mask> token

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
			vocab.update(self.added_tokens_encoder				)
			return vocab

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,	out_type=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if token in self.fairseq_tokens_to_ids:
				return self.fairseq_tokens_to_ids[token]
			__lowerCAmelCase    :		Dict		    =   self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE				)

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if index in self.fairseq_ids_to_tokens:
				return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   ''.join(_SCREAMING_SNAKE_CASE				).replace(_SCREAMING_SNAKE_CASE ,	' '				).strip()
			return out_string







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
				logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
				return
			__lowerCAmelCase    :		Optional[int]		    =   os.path.join(
			    _SCREAMING_SNAKE_CASE ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

			if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
				copyfile(self.vocab_file ,	_SCREAMING_SNAKE_CASE				)
			elif not os.path.isfile(self.vocab_file				):
				with open(_SCREAMING_SNAKE_CASE ,	'wb'				) as fi:
					__lowerCAmelCase    :		Tuple		    =   self.sp_model.serialized_model_proto()
					fi.write(_SCREAMING_SNAKE_CASE				)

			return (out_vocab_file,)

"""simple docstring"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

			for dest in self.connections[node]:
				current_probability += self.connections[node][dest]
				if current_probability > random_value:
					return dest
			return ""



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   graph.transition(_UpperCamelCase	)
		visited[node] += 1

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()