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

import sentencepiece as spm

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


lowercase__		:List[str]			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   "▁"

lowercase__		:Tuple			  =   {"vocab_file": "sentencepiece.bpe.model"}

lowercase__		:List[str]			  =   {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowercase__		:Any			  =   {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
lowercase__		:List[str]			  =   ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[str]	 =VOCAB_FILES_NAMES
							lowercase_	:	Optional[int]	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	List[str]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	int	 =['''input_ids''', '''attention_mask''']

							lowercase_	:	List[int]	 =[]
							lowercase_	:	List[int]	 =[]




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

												lowercase						     =						{} 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__       ,tokenizer_file=A__       ,src_lang=A__       ,tgt_lang=A__       ,additional_special_tokens=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

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

												lowercase						     =						len(self.sp_model)
												lowercase						     =						{
												    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A__)
												}
												lowercase						     =						{v: k for k, v in self.lang_code_to_id.items()}
												lowercase						     =						len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

												self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												lowercase						     =						list(self.lang_code_to_id.keys())

												if additional_special_tokens is not None:
																	# Only add those special tokens if they are not already there.
																	self._additional_special_tokens.extend(
																	    [t for t in additional_special_tokens if t not in self._additional_special_tokens])

												lowercase						     =						src_lang if src_lang is not None else '''en_XX'''
												lowercase						     =						self.lang_code_to_id[self._src_lang]
												lowercase						     =						tgt_lang
												self.set_src_lang_special_tokens(self._src_lang)




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												lowercase						     =						self.sp_model.serialized_model_proto()
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

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

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




							@property
							def 			A__				(    self):
												return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token




							@property
							def 			A__				(    self):
												return self._src_lang




							@src_lang.setter
							def 			A__				(    self       ,A__):
												lowercase						     =						new_src_lang
												self.set_src_lang_special_tokens(self._src_lang)




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = 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__)

												lowercase						     =						[1] * len(self.prefix_tokens)
												lowercase						     =						[1] * len(self.suffix_tokens)
												if token_ids_a is None:
																	return prefix_ones + ([0] * len(A__)) + suffix_ones
												return prefix_ones + ([0] * len(A__)) + ([0] * len(A__)) + suffix_ones




							def 			A__				(    self       ,A__       ,A__ = None):
												if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[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]




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,**A__):
												if src_lang is None or tgt_lang is None:
																	raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
												lowercase						     =						src_lang
												lowercase						     =						self(A__       ,add_special_tokens=A__       ,return_tensors=A__       ,**A__)
												lowercase						     =						self.convert_tokens_to_ids(A__)
												lowercase						     =						tgt_lang_id
												return inputs




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




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




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						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 			A__				(    self       ,A__):
												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 			A__				(    self       ,A__):
												lowercase						     =						''''''.join(A__).replace(A__       ,''' ''').strip()
												return out_string




							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						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:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)




							def 			A__				(    self       ,A__       ,A__ = "en_XX"       ,A__ = None       ,A__ = "ro_RO"       ,**A__       ,):
												lowercase						     =						src_lang
												lowercase						     =						tgt_lang
												return super().prepare_seqaseq_batch(A__       ,A__       ,**A__)




							def 			A__				(    self):
												return self.set_src_lang_special_tokens(self.src_lang)




							def 			A__				(    self):
												return self.set_tgt_lang_special_tokens(self.tgt_lang)




							def 			A__				(    self       ,A__):
												lowercase						     =						self.lang_code_to_id[src_lang]
												lowercase						     =						[]
												lowercase						     =						[self.eos_token_id, self.cur_lang_code]





							def 			A__				(    self       ,A__):
												lowercase						     =						self.lang_code_to_id[lang]
												lowercase						     =						[]
												lowercase						     =						[self.eos_token_id, self.cur_lang_code]







from __future__ import annotations

from decimal import Decimal

from numpy import array



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(lowerCAmelCase__		) == 2 and len(matrix[0]		) == 2 and len(matrix[1]		) == 2:
										# Calculate the determinant of the matrix
										lowercase						     =						float(
										    d(matrix[0][0]		) * d(matrix[1][1]		) - d(matrix[1][0]		) * d(matrix[0][1]		)		)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''		)

										# Creates a copy of the matrix with swapped positions of the elements
										lowercase						     =						[[0.0, 0.0], [0.0, 0.0]]
										lowercase     ,						lowercase						     =						matrix[1][1], matrix[0][0]
										lowercase     ,						lowercase						     =						-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(lowerCAmelCase__		)		) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(lowerCAmelCase__		) == 3
					    and len(matrix[0]		) == 3
					    and len(matrix[1]		) == 3
					    and len(matrix[2]		) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										lowercase						     =						float(
										    (
										        (d(matrix[0][0]		) * d(matrix[1][1]		) * d(matrix[2][2]		))
										        + (d(matrix[0][1]		) * d(matrix[1][2]		) * d(matrix[2][0]		))
										        + (d(matrix[0][2]		) * d(matrix[1][0]		) * d(matrix[2][1]		))
										    )
										    - (
										        (d(matrix[0][2]		) * d(matrix[1][1]		) * d(matrix[2][0]		))
										        + (d(matrix[0][1]		) * d(matrix[1][0]		) * d(matrix[2][2]		))
										        + (d(matrix[0][0]		) * d(matrix[1][2]		) * d(matrix[2][1]		))
										    )		)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''		)

										# Creating cofactor matrix
										lowercase						     =						[
										    [d(0.0		), d(0.0		), d(0.0		)],
										    [d(0.0		), d(0.0		), d(0.0		)],
										    [d(0.0		), d(0.0		), d(0.0		)],
										]
										lowercase						     =						(d(matrix[1][1]		) * d(matrix[2][2]		)) - (
										    d(matrix[1][2]		) * d(matrix[2][1]		)
										)
										lowercase						     =						-(
										    (d(matrix[1][0]		) * d(matrix[2][2]		)) - (d(matrix[1][2]		) * d(matrix[2][0]		))
										)
										lowercase						     =						(d(matrix[1][0]		) * d(matrix[2][1]		)) - (
										    d(matrix[1][1]		) * d(matrix[2][0]		)
										)
										lowercase						     =						-(
										    (d(matrix[0][1]		) * d(matrix[2][2]		)) - (d(matrix[0][2]		) * d(matrix[2][1]		))
										)
										lowercase						     =						(d(matrix[0][0]		) * d(matrix[2][2]		)) - (
										    d(matrix[0][2]		) * d(matrix[2][0]		)
										)
										lowercase						     =						-(
										    (d(matrix[0][0]		) * d(matrix[2][1]		)) - (d(matrix[0][1]		) * d(matrix[2][0]		))
										)
										lowercase						     =						(d(matrix[0][1]		) * d(matrix[1][2]		)) - (
										    d(matrix[0][2]		) * d(matrix[1][1]		)
										)
										lowercase						     =						-(
										    (d(matrix[0][0]		) * d(matrix[1][2]		)) - (d(matrix[0][2]		) * d(matrix[1][0]		))
										)
										lowercase						     =						(d(matrix[0][0]		) * d(matrix[1][1]		)) - (
										    d(matrix[0][1]		) * d(matrix[1][0]		)
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										lowercase						     =						array(lowerCAmelCase__		)
										for i in range(3		):
															for j in range(3		):
																				lowercase						     =						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										lowercase						     =						array(lowerCAmelCase__		)
										for i in range(3		):
															for j in range(3		):
																				inverse_matrix[i][j] /= d(lowerCAmelCase__		)

        # Calculate the inverse of the matrix
										return [[float(d(lowerCAmelCase__		)		) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''		)




