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

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

__a  :					int          =  {
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__a  :					int          =  {
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

__a  :					Dict          =  {"""Helsinki-NLP/opus-mt-en-de""": 5_1_2}
__a  :					Optional[int]          =  {}




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			Any      			=						VOCAB_FILES_NAMES
			__a  :			Optional[Any]      			=						PRETRAINED_VOCAB_FILES_MAP
			__a  :			List[Any]      			=						PRETRAINED_INIT_CONFIGURATION
			__a  :			List[str]      			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a  :			Union[str, Any]      			=						['''input_ids''', '''attention_mask''']
			__a  :			List[str]      			=						re.compile('''>>.+<<'''			)  # type: re.Pattern





			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__="<unk>"				,       lowerCAmelCase__="</s>"				,       lowerCAmelCase__="<pad>"				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__ = None				,       lowerCAmelCase__=False				,       **lowerCAmelCase__				,       )					-> str:

							'''simple docstring'''


							__lowercase			      =							{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
							    source_lang=lowercase__				,       target_lang=lowercase__				,       unk_token=lowercase__				,       eos_token=lowercase__				,       pad_token=lowercase__				,       model_max_length=lowercase__				,       sp_model_kwargs=self.sp_model_kwargs				,       target_vocab_file=lowercase__				,       separate_vocabs=lowercase__				,       **lowercase__				,       )
							assert Path(lowercase__						).exists(), F"cannot find spm source {source_spm}"

							__lowercase			      =							separate_vocabs
							__lowercase			      =							load_json(lowercase__						)
							if self.unk_token not in self.encoder:
											raise KeyError('''<unk> token must be in vocab'''						)
							assert self.pad_token in self.encoder

							if separate_vocabs:
											__lowercase			      =							load_json(lowercase__						)
											__lowercase			      =							{v: k for k, v in self.target_encoder.items()}
											__lowercase			      =							[]
							else:
											__lowercase			      =							{v: k for k, v in self.encoder.items()}
											__lowercase			      =							[k for k in self.encoder if k.startswith('''>>'''						) and k.endswith('''<<'''						)]

							__lowercase			      =							source_lang
							__lowercase			      =							target_lang
							__lowercase			      =							[source_spm, target_spm]

							# load SentencePiece model for pre-processing
							__lowercase			      =							load_spm(lowercase__				,       self.sp_model_kwargs						)
							__lowercase			      =							load_spm(lowercase__				,       self.sp_model_kwargs						)
							__lowercase			      =							self.spm_source
							__lowercase			      =							self.encoder

							# Multilingual target side: default to using first supported language code.

							self._setup_normalizer()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							try:
											from sacremoses import MosesPunctNormalizer

											__lowercase			      =							MosesPunctNormalizer(self.source_lang						).normalize
							except (ImportError, FileNotFoundError):
											warnings.warn('''Recommended: pip install sacremoses.'''						)
											__lowercase			      =							lambda lowerCAmelCase__						: x





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							return self.punc_normalizer(lowercase__						) if x else ""





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							return self.current_encoder.get(lowercase__				,       self.current_encoder[self.unk_token]						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							self.language_code_re.match(lowercase__						)
							__lowercase			      =							[match.group(0						)] if match else []
							return code, self.language_code_re.sub(''''''				,       lowercase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							self.remove_language_code(lowercase__						)
							__lowercase			      =							self.current_spm.encode(lowercase__				,       out_type=lowercase__						)
							return code + pieces





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							return self.decoder.get(lowercase__				,       self.unk_token						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       **lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							return super().batch_decode(lowercase__				,       **lowercase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       **lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							return super().decode(lowercase__				,       **lowercase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							self.spm_source if self._decode_use_source_tokenizer else self.spm_target
							__lowercase			      =							[]
							__lowercase			      =							""
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += sp_model.decode_pieces(lowercase__						) + token + " "
															__lowercase			      =							[]
											else:
															current_sub_tokens.append(lowercase__						)
							out_string += sp_model.decode_pieces(lowercase__						)
							return out_string.strip()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> int:

							'''simple docstring'''


							if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							self.spm_source
							__lowercase			      =							self.encoder





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							self.spm_target
							if self.separate_vocabs:
											__lowercase			      =							self.target_encoder





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							return len(self.encoder						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = None						)					-> List[Any]:

							'''simple docstring'''


							if not os.path.isdir(lowercase__						):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
											return
							__lowercase			      =							[]

							if self.separate_vocabs:
											__lowercase			      =							os.path.join(
											    lowercase__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']				,       )
											__lowercase			      =							os.path.join(
											    lowercase__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']				,       )
											save_json(self.encoder				,       lowercase__						)
											save_json(self.target_encoder				,       lowercase__						)
											saved_files.append(lowercase__						)
											saved_files.append(lowercase__						)
							else:
											__lowercase			      =							os.path.join(
											    lowercase__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']						)
											save_json(self.encoder				,       lowercase__						)
											saved_files.append(lowercase__						)

							for spm_save_filename, spm_orig_path, spm_model in zip(
							    [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]				,       self.spm_files				,       [self.spm_source, self.spm_target]				,       ):
											__lowercase			      =							os.path.join(
											    lowercase__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename						)
											if os.path.abspath(lowercase__						) != os.path.abspath(lowercase__						) and os.path.isfile(lowercase__						):
															copyfile(lowercase__				,       lowercase__						)
															saved_files.append(lowercase__						)
											elif not os.path.isfile(lowercase__						):
															with open(lowercase__				,       '''wb'''						) as fi:
																			__lowercase			      =							spm_model.serialized_model_proto()
																			fi.write(lowercase__						)
															saved_files.append(lowercase__						)

							return tuple(lowercase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							return self.get_src_vocab()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							return dict(self.encoder				,       **self.added_tokens_encoder						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							return dict(self.target_encoder				,       **self.added_tokens_decoder						)





			def __getstate__(       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							self.__dict__.copy()
							state.update(
							    {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}						)
							return state





			def __setstate__(       self				,       lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							__lowercase			      =							d

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

							__lowercase			      =							(load_spm(lowercase__				,       self.sp_model_kwargs						) for f in self.spm_files)
							__lowercase			      =							self.spm_source
							self._setup_normalizer()





			def 	_SCREAMING_SNAKE_CASE      (       self				,       *lowerCAmelCase__				,       **lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							return 1





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							set(self.all_special_ids						)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
							return [1 if x in all_special_ids else 0 for x in seq]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = False						)					-> Dict:

							'''simple docstring'''


							if already_has_special_tokens:
											return self._special_token_mask(lowercase__						)
							elif token_ids_a is None:
											return self._special_token_mask(lowercase__						) + [1]
							else:
											return self._special_token_mask(token_ids_a + token_ids_a						) + [1]





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE_							)
				spm.Load(SCREAMING_SNAKE_CASE_							)
				return spm





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				with open(SCREAMING_SNAKE_CASE_							,      '''w'''							) as f:
								json.dump(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      indent=2							)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				with open(SCREAMING_SNAKE_CASE_							,      '''r'''							) as f:
								return json.load(SCREAMING_SNAKE_CASE_							)
from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


__a  :					Union[str, Any]          =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,      lowercase							):
								return _split_by_node_map_style_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)
				else:
								return _split_by_node_iterable_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)