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     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     VOCAB_FILES_NAMES
							UpperCAmelCase				   =     PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase				   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase				   =     ["input_ids", "attention_mask"]







							def __init__(   self      :     int ,			_A      :     Optional[Any] ,			_A      :     Union[str, Any]="<s>" ,			_A      :     List[Any]="</s>" ,			_A      :     Tuple="</s>" ,			_A      :     List[str]="<s>" ,			_A      :     Optional[int]="<unk>" ,			_A      :     Optional[Any]="<pad>" ,			_A      :     Optional[Any]="<mask>" ,			_A      :     Optional[Dict[str, Any]] = None ,			**_A      :     List[str] ,			):
												# Mask token behave like a normal word, i.e. include the space before it
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else mask_token

												_UpperCamelCase			     =     {} if sp_model_kwargs is None else sp_model_kwargs

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

												_UpperCamelCase			     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(str(_A		)		)
												_UpperCamelCase			     =     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
												_UpperCamelCase			     =     {'''<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
												_UpperCamelCase			     =     1

												_UpperCamelCase			     =     len(self.sp_model		) + self.fairseq_offset
												_UpperCamelCase			     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







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







							def __setstate__(   self      :     Tuple ,			_A      :     Optional[int]		):
												_UpperCamelCase			     =     d

												# for backward compatibility
												if not hasattr(self ,			'''sp_model_kwargs'''		):
																	_UpperCamelCase			     =     {}

												_UpperCamelCase			     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):

												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												_UpperCamelCase			     =     [self.cls_token_id]
												_UpperCamelCase			     =     [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None ,			_A      :     bool = False		):

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

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







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):
												_UpperCamelCase			     =     [self.sep_token_id]
												_UpperCamelCase			     =     [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  UpperCamelCase_						(   self      :     int		):
												return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     {self.convert_ids_to_tokens(_A		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     str		):
												return self.sp_model.encode(_A ,			out_type=_A		)







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     Optional[Any]		):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												_UpperCamelCase			     =     self.sp_model.PieceToId(_A		)

												# 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  UpperCamelCase_						(   self      :     Dict ,			_A      :     str		):
												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  UpperCamelCase_						(   self      :     Any ,			_A      :     int		):
												_UpperCamelCase			     =     ''''''.join(_A		).replace(_A ,			''' '''		).strip()
												return out_string







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

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

												return (out_vocab_file,)

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

					try:
										if dataset.shape[1] != value_array.shape[1]:
															_UpperCamelCase			     =     (
															    '''Wrong input data\'s shape... '''
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(__snake_case						)
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError('''Wrong shape'''						)

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

					return answer
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return np.dot(__snake_case  ,     __snake_case						) / (norm(__snake_case						) * norm(__snake_case						))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
