

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


snake_case    :						Tuple  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						'▁'

snake_case    :						Union[str, Any]  =						{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

snake_case    :						Optional[int]  =						{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

snake_case    :						Any  =						{
    'facebook/m2m100_418M': 1_0_2_4,
}

# fmt: off
snake_case    :						Tuple  =						{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    List[str]           =						VOCAB_FILES_NAMES
					__A     :    Tuple           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A     :    str           =						PRETRAINED_VOCAB_FILES_MAP
					__A     :    Any           =						['input_ids', 'attention_mask']

					__A     :    List[int]           =						[]
					__A     :    List[int]           =						[]







					def __init__(       self      :     Tuple			,		_A      :     List[str]			,		_A      :     Dict			,		_A      :     int=None			,		_A      :     int=None			,		_A      :     Any="<s>"			,		_A      :     Optional[Any]="</s>"			,		_A      :     Union[str, Any]="</s>"			,		_A      :     Dict="<pad>"			,		_A      :     Union[str, Any]="<unk>"			,		_A      :     Optional[Any]="m2m100"			,		_A      :     Optional[Dict[str, Any]] = None			,		_A      :     int=8			,		**_A      :     Tuple			,		):
											A__	:							Union[str, Any]						    = {} if sp_model_kwargs is None else sp_model_kwargs

											A__	:							int						    = language_codes
											A__	:							Optional[Any]						    = FAIRSEQ_LANGUAGE_CODES[language_codes]
											A__	:							str						    = {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

											A__	:							Any						    = kwargs.get("additional_special_tokens"			,		[])
											kwargs["additional_special_tokens"] += [
											    self.get_lang_token(_A)
											    for lang_code in fairseq_language_code
											    if self.get_lang_token(_A) not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    src_lang=_A			,		tgt_lang=_A			,		bos_token=_A			,		eos_token=_A			,		sep_token=_A			,		unk_token=_A			,		pad_token=_A			,		language_codes=_A			,		sp_model_kwargs=self.sp_model_kwargs			,		num_madeup_words=_A			,		**_A			,		)

											A__	:							Dict						    = vocab_file
											A__	:							Any						    = load_json(_A)
											A__	:							List[Any]						    = {v: k for k, v in self.encoder.items()}
											A__	:							Dict						    = spm_file
											A__	:							str						    = load_spm(_A			,		self.sp_model_kwargs)

											A__	:							Any						    = len(self.encoder)

											A__	:							Optional[int]						    = {
											    self.get_lang_token(_A): self.encoder_size + i for i, lang_code in enumerate(_A)
											}
											A__	:							List[Any]						    = {lang_code: self.encoder_size + i for i, lang_code in enumerate(_A)}
											A__	:							Union[str, Any]						    = {v: k for k, v in self.lang_token_to_id.items()}

											A__	:							Optional[int]						    = src_lang if src_lang is not None else "en"
											A__	:							Union[str, Any]						    = tgt_lang
											A__	:							Optional[int]						    = self.get_lang_id(self._src_lang)
											self.set_src_lang_special_tokens(self._src_lang)

											A__	:							Union[str, Any]						    = num_madeup_words







					@property
					def  _lowercase			(       self      :     Dict):
											return len(self.encoder) + len(self.lang_token_to_id)







					@property
					def  _lowercase			(       self      :     str):
											return self._src_lang







					@src_lang.setter
					def  _lowercase			(       self      :     Any			,		_A      :     str):
											A__	:							str						    = new_src_lang
											self.set_src_lang_special_tokens(self._src_lang)







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







					def  _lowercase			(       self      :     Any			,		_A      :     List[str]):
											if token in self.lang_token_to_id:
																	return self.lang_token_to_id[token]
											return self.encoder.get(_A			,		self.encoder[self.unk_token])







					def  _lowercase			(       self      :     int			,		_A      :     int):
											if index in self.id_to_lang_token:
																	return self.id_to_lang_token[index]
											return self.decoder.get(_A			,		self.unk_token)







					def  _lowercase			(       self      :     List[str]			,		_A      :     Optional[Any]):
											A__	:							Any						    = []
											A__	:							Any						    = ""
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							out_string += self.sp_model.decode(_A) + token
																							A__	:							List[Any]						    = []
																	else:
																							current_sub_tokens.append(_A)
											out_string += self.sp_model.decode(_A)
											return out_string.strip()







					def  _lowercase			(       self      :     Union[str, Any]			,		_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)

											A__	:							Optional[int]						    = [1] * len(self.prefix_tokens)
											A__	:							Tuple						    = [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  _lowercase			(       self      :     Dict			,		_A      :     List[int]			,		_A      :     Optional[List[int]] = 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  _lowercase			(       self      :     List[Any]):
											A__	:							List[Any]						    = {self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab







					def __getstate__(       self      :     int):
											A__	:							Optional[Any]						    = self.__dict__.copy()
											A__	:							int						    = None
											return state







					def __setstate__(       self      :     Any			,		_A      :     Dict):
											A__	:							Optional[Any]						    = d

											# for backward compatibility
											if not hasattr(self			,		"sp_model_kwargs"):
																	A__	:							int						    = {}

											A__	:							List[Any]						    = load_spm(self.spm_file			,		self.sp_model_kwargs)







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     str			,		_A      :     Optional[str] = None):
											A__	:							Union[str, Any]						    = Path(_A)
											if not save_dir.is_dir():
																	raise OSError(F'{save_directory} should be a directory')
											A__	:							int						    = save_dir / (
											    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
											)
											A__	:							Union[str, Any]						    = save_dir / (
											    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
											)

											save_json(self.encoder			,		_A)

											if os.path.abspath(self.spm_file) != os.path.abspath(_A) and os.path.isfile(self.spm_file):
																	copyfile(self.spm_file			,		_A)
											elif not os.path.isfile(self.spm_file):
																	with open(_A			,		"wb") as fi:
																							A__	:							Any						    = self.sp_model.serialized_model_proto()
																							fi.write(_A)

											return (str(_A), str(_A))







					def  _lowercase			(       self      :     str			,		_A      :     List[str]			,		_A      :     str = "en"			,		_A      :     Optional[List[str]] = None			,		_A      :     str = "ro"			,		**_A      :     Optional[Any]			,		):
											A__	:							Optional[Any]						    = src_lang
											A__	:							Optional[int]						    = tgt_lang
											self.set_src_lang_special_tokens(self.src_lang)
											return super().prepare_seqaseq_batch(_A			,		_A			,		**_A)







					def  _lowercase			(       self      :     Dict			,		_A      :     List[Any]			,		_A      :     Optional[str]			,		_A      :     Optional[str]			,		**_A      :     List[str]):
											if src_lang is None or tgt_lang is None:
																	raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
											A__	:							str						    = src_lang
											A__	:							Tuple						    = self(_A			,		add_special_tokens=_A			,		**_A)
											A__	:							Union[str, Any]						    = self.get_lang_id(_A)
											A__	:							Any						    = tgt_lang_id
											return inputs







					def  _lowercase			(       self      :     List[str]):
											self.set_src_lang_special_tokens(self.src_lang)







					def  _lowercase			(       self      :     Dict):
											self.set_tgt_lang_special_tokens(self.tgt_lang)







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     str):
											A__	:							Dict						    = self.get_lang_token(_A)
											A__	:							str						    = self.lang_token_to_id[lang_token]
											A__	:							str						    = [self.cur_lang_id]
											A__	:							Union[str, Any]						    = [self.eos_token_id]







					def  _lowercase			(       self      :     List[Any]			,		_A      :     str):
											A__	:							Union[str, Any]						    = self.get_lang_token(_A)
											A__	:							str						    = self.lang_token_to_id[lang_token]
											A__	:							Optional[Any]						    = [self.cur_lang_id]
											A__	:							Dict						    = [self.eos_token_id]







					def  _lowercase			(       self      :     str			,		_A      :     str):
											return self.lang_code_to_token[lang]







					def  _lowercase			(       self      :     int			,		_A      :     str):
											A__	:							str						    = self.get_lang_token(_A)
											return self.lang_token_to_id[lang_token]




def 	snake_case__       (					__lowercase							,     __lowercase       )		->		sentencepiece.SentencePieceProcessor:







						"""simple docstring"""

						A__	:							int						    = sentencepiece.SentencePieceProcessor(**__lowercase       )
						spm.Load(str(__lowercase       )       )
						return spm
def 	snake_case__       (					__lowercase       )		->		Union[Dict, List]:







						"""simple docstring"""

						with open(__lowercase							,     "r"       ) as f:
												return json.load(__lowercase       )




def 	snake_case__       (					__lowercase							,     __lowercase       )		->		None:







						"""simple docstring"""

						with open(__lowercase							,     "w"       ) as f:
												json.dump(__lowercase							,     __lowercase							,     indent=2       )




import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Dict			,		_A      :     NestedDataStructureLike[PathLike]			,		_A      :     Optional[NamedSplit] = None			,		_A      :     Optional[Features] = None			,		_A      :     str = None			,		_A      :     bool = False			,		_A      :     bool = False			,		_A      :     Optional[str] = None			,		_A      :     Optional[int] = None			,		**_A      :     Dict			,		):
											super().__init__(
											    _A			,		split=_A			,		features=_A			,		cache_dir=_A			,		keep_in_memory=_A			,		streaming=_A			,		num_proc=_A			,		**_A			,		)
											A__	:							Optional[int]						    = field
											A__	:							Dict						    = path_or_paths if isinstance(_A			,		_A) else {self.split: path_or_paths}
											A__	:							int						    = Json(
											    cache_dir=_A			,		data_files=_A			,		features=_A			,		field=_A			,		**_A			,		)







					def  _lowercase			(       self      :     Dict):
											# Build iterable dataset
											if self.streaming:
																	A__	:							Union[str, Any]						    = self.builder.as_streaming_dataset(split=self.split)
											# Build regular (map-style) dataset
											else:
																	A__	:							Optional[Any]						    = None
																	A__	:							Any						    = None
																	A__	:							Tuple						    = None
																	A__	:							Tuple						    = None

																	self.builder.download_and_prepare(
																	    download_config=_A			,		download_mode=_A			,		verification_mode=_A			,		base_path=_A			,		num_proc=self.num_proc			,		)
																	A__	:							Any						    = self.builder.as_dataset(
																	    split=self.split			,		verification_mode=_A			,		in_memory=self.keep_in_memory)
											return dataset




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]			,		_A      :     Dataset			,		_A      :     Union[PathLike, BinaryIO]			,		_A      :     Optional[int] = None			,		_A      :     Optional[int] = None			,		**_A      :     Any			,		):
											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

											A__	:							Tuple						    = dataset
											A__	:							Tuple						    = path_or_buf
											A__	:							int						    = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											A__	:							Tuple						    = num_proc
											A__	:							Any						    = "utf-8"
											A__	:							Optional[int]						    = to_json_kwargs







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Any						    = self.to_json_kwargs.pop("path_or_buf"			,		_A)
											A__	:							Union[str, Any]						    = self.to_json_kwargs.pop("orient"			,		"records")
											A__	:							List[Any]						    = self.to_json_kwargs.pop("lines"			,		True if orient == "records" else False)
											A__	:							List[Any]						    = self.to_json_kwargs.pop("index"			,		False if orient in ["split", "table"] else True)
											A__	:							int						    = self.to_json_kwargs.pop("compression"			,		_A)

											if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																	raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

											if isinstance(self.path_or_buf			,		(str, bytes, os.PathLike)):
																	with fsspec.open(self.path_or_buf			,		"wb"			,		compression=_A) as buffer:
																							A__	:							int						    = self._write(file_obj=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											else:
																	if compression:
																							raise NotImplementedError(
																							    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																							    " was passed. Please provide a local path instead.")
																	A__	:							Optional[Any]						    = self._write(
																	    file_obj=self.path_or_buf			,		orient=_A			,		lines=_A			,		index=_A			,		**self.to_json_kwargs)
											return written







					def  _lowercase			(       self      :     List[Any]			,		_A      :     int):
											A__    ,       A__    ,       A__    ,       A__    ,       A__	:							Union[str, Any]						    = args

											A__	:							Dict						    = query_table(
											    table=self.dataset.data			,		key=slice(_A			,		offset + self.batch_size)			,		indices=self.dataset._indices			,		)
											A__	:							Dict						    = batch.to_pandas().to_json(
											    path_or_buf=_A			,		orient=_A			,		lines=_A			,		index=_A			,		**_A)
											if not json_str.endswith("\n"):
																	json_str += "\n"
											return json_str.encode(self.encoding)







					def  _lowercase			(       self      :     List[str]			,		_A      :     BinaryIO			,		_A      :     str			,		_A      :     List[Any]			,		_A      :     Optional[Any]			,		**_A      :     Optional[Any]			,		):
											A__	:							Any						    = 0

											if self.num_proc is None or self.num_proc == 1:
																	for offset in logging.tqdm(
																	    range(0			,		len(self.dataset)			,		self.batch_size)			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																							A__	:							List[str]						    = self._batch_json((offset, orient, lines, index, to_json_kwargs))
																							written += file_obj.write(_A)
											else:
																	A__    ,       A__	:							Union[str, Any]						    = len(self.dataset), self.batch_size
																	with multiprocessing.Pool(self.num_proc) as pool:
																							for json_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_json			,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,		_A			,		_A)]			,		)			,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,		unit="ba"			,		disable=not logging.is_progress_bar_enabled()			,		desc="Creating json from Arrow format"			,		):
																													written += file_obj.write(_A)

											return written