
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


__SCREAMING_SNAKE_CASE     :				str         =       logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :				List[Any]         =       {
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__SCREAMING_SNAKE_CASE     :				Dict         =       {
    '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'
        )
    },
}

__SCREAMING_SNAKE_CASE     :				Union[str, Any]         =       {'Helsinki-NLP/opus-mt-en-de': 5_12}
__SCREAMING_SNAKE_CASE     :				str         =       {}


class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		VOCAB_FILES_NAMES
		_lowerCAmelCase						=		PRETRAINED_VOCAB_FILES_MAP
		_lowerCAmelCase						=		PRETRAINED_INIT_CONFIGURATION
		_lowerCAmelCase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_lowerCAmelCase						=		["input_ids", "attention_mask"]
		_lowerCAmelCase						=		re.compile(">>.+<<"      )  # type: re.Pattern





		def __init__(							self      :		str  ,					lowerCamelCase__      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Dict=None  ,					lowerCamelCase__      :		List[Any]=None  ,					lowerCamelCase__      :		Dict=None  ,					lowerCamelCase__      :		Tuple="<unk>"  ,					lowerCamelCase__      :		Optional[Any]="</s>"  ,					lowerCamelCase__      :		Union[str, Any]="<pad>"  ,					lowerCamelCase__      :		Any=5_1_2  ,					lowerCamelCase__      :		Optional[Dict[str, Any]] = None  ,					lowerCamelCase__      :		List[str]=False  ,					**lowerCamelCase__      :		Any  ,					):
							lowerCAmelCase						:   Optional[Any]							=       {} 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=lowerCamelCase__  ,					target_lang=lowerCamelCase__  ,					unk_token=lowerCamelCase__  ,					eos_token=lowerCamelCase__  ,					pad_token=lowerCamelCase__  ,					model_max_length=lowerCamelCase__  ,					sp_model_kwargs=self.sp_model_kwargs  ,					target_vocab_file=lowerCamelCase__  ,					separate_vocabs=lowerCamelCase__  ,					**lowerCamelCase__  ,					)
							assert Path(lowerCamelCase__			).exists(), f'''cannot find spm source {source_spm}'''

							lowerCAmelCase						:   List[Any]							=       separate_vocabs
							lowerCAmelCase						:   int							=       load_json(lowerCamelCase__			)
							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:
												lowerCAmelCase						:   int							=       load_json(lowerCamelCase__			)
												lowerCAmelCase						:   int							=       {v: k for k, v in self.target_encoder.items()}
												lowerCAmelCase						:   List[str]							=       []
							else:
												lowerCAmelCase						:   Optional[int]							=       {v: k for k, v in self.encoder.items()}
												lowerCAmelCase						:   list							=       [k for k in self.encoder if k.startswith('''>>'''			) and k.endswith('''<<'''			)]

							lowerCAmelCase						:   Optional[int]							=       source_lang
							lowerCAmelCase						:   Any							=       target_lang
							lowerCAmelCase						:   List[str]							=       [source_spm, target_spm]

							# load SentencePiece model for pre-processing
							lowerCAmelCase						:   str							=       load_spm(lowerCamelCase__  ,					self.sp_model_kwargs			)
							lowerCAmelCase						:   Dict							=       load_spm(lowerCamelCase__  ,					self.sp_model_kwargs			)
							lowerCAmelCase						:   Optional[int]							=       self.spm_source
							lowerCAmelCase						:   Tuple							=       self.encoder

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

							self._setup_normalizer()





		def  _A							(							self      :		Dict			):
							try:
												from sacremoses import MosesPunctNormalizer

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





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		str			):
							return self.punc_normalizer(lowerCamelCase__			) if x else ""





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Optional[Any]			):
							return self.current_encoder.get(lowerCamelCase__  ,					self.current_encoder[self.unk_token]			)





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		str			):
							lowerCAmelCase						:   int							=       self.language_code_re.match(lowerCamelCase__			)
							lowerCAmelCase						:   list							=       [match.group(0			)] if match else []
							return code, self.language_code_re.sub(''''''  ,					lowerCamelCase__			)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		str			):
							lowerCAmelCase						:   Dict							=       self.remove_language_code(lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       self.current_spm.encode(lowerCamelCase__  ,					out_type=lowerCamelCase__			)
							return code + pieces





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		int			):
							return self.decoder.get(lowerCamelCase__  ,					self.unk_token			)





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		int  ,					**lowerCamelCase__      :		Dict			):
							return super().batch_decode(lowerCamelCase__  ,					**lowerCamelCase__			)





		def  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		Optional[int]  ,					**lowerCamelCase__      :		str			):
							return super().decode(lowerCamelCase__  ,					**lowerCamelCase__			)





		def  _A							(							self      :		int  ,					lowerCamelCase__      :		List[str]			):
							lowerCAmelCase						:   Optional[int]							=       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
							lowerCAmelCase						:   Optional[Any]							=       []
							lowerCAmelCase						:   List[str]							=       ''''''
							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(lowerCamelCase__			) + token + " "
																	lowerCAmelCase						:   Tuple							=       []
												else:
																	current_sub_tokens.append(lowerCamelCase__			)
							out_string += sp_model.decode_pieces(lowerCamelCase__			)
							return out_string.strip()





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		List[Any]=None			):
							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  _A							(							self      :		Union[str, Any]			):
							lowerCAmelCase						:   Optional[Any]							=       self.spm_source
							lowerCAmelCase						:   Union[str, Any]							=       self.encoder





		def  _A							(							self      :		Tuple			):
							lowerCAmelCase						:   List[Any]							=       self.spm_target
							if self.separate_vocabs:
												lowerCAmelCase						:   Optional[Any]							=       self.target_encoder





		@property
		def  _A							(							self      :		Optional[int]			):
							return len(self.encoder			)





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[str] = None			):
							if not os.path.isdir(lowerCamelCase__			):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
												return
							lowerCAmelCase						:   List[Any]							=       []

							if self.separate_vocabs:
												lowerCAmelCase						:   List[str]							=       os.path.join(
												    lowerCamelCase__  ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']  ,					)
												lowerCAmelCase						:   Optional[Any]							=       os.path.join(
												    lowerCamelCase__  ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']  ,					)
												save_json(self.encoder  ,					lowerCamelCase__			)
												save_json(self.target_encoder  ,					lowerCamelCase__			)
												saved_files.append(lowerCamelCase__			)
												saved_files.append(lowerCamelCase__			)
							else:
												lowerCAmelCase						:   List[str]							=       os.path.join(
												    lowerCamelCase__  ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']			)
												save_json(self.encoder  ,					lowerCamelCase__			)
												saved_files.append(lowerCamelCase__			)

							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]  ,					):
												lowerCAmelCase						:   Dict							=       os.path.join(
												    lowerCamelCase__  ,					(filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename			)
												if os.path.abspath(lowerCamelCase__			) != os.path.abspath(lowerCamelCase__			) and os.path.isfile(lowerCamelCase__			):
																	copyfile(lowerCamelCase__  ,					lowerCamelCase__			)
																	saved_files.append(lowerCamelCase__			)
												elif not os.path.isfile(lowerCamelCase__			):
																	with open(lowerCamelCase__  ,					'''wb'''			) as fi:
																						lowerCAmelCase						:   Optional[int]							=       spm_model.serialized_model_proto()
																						fi.write(lowerCamelCase__			)
																	saved_files.append(lowerCamelCase__			)

							return tuple(lowerCamelCase__			)





		def  _A							(							self      :		Any			):
							return self.get_src_vocab()





		def  _A							(							self      :		Optional[int]			):
							return dict(self.encoder  ,					**self.added_tokens_encoder			)





		def  _A							(							self      :		Dict			):
							return dict(self.target_encoder  ,					**self.added_tokens_decoder			)





		def __getstate__(							self      :		Optional[int]			):
							lowerCAmelCase						:   Dict							=       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      :		str  ,					lowerCamelCase__      :		Dict			):
							lowerCAmelCase						:   Dict							=       d

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

							lowerCAmelCase						:   Tuple							=       (load_spm(lowerCamelCase__  ,					self.sp_model_kwargs			) for f in self.spm_files)
							lowerCAmelCase						:   List[Any]							=       self.spm_source
							self._setup_normalizer()





		def  _A							(							self      :		str  ,					*lowerCamelCase__      :		Optional[Any]  ,					**lowerCamelCase__      :		Tuple			):
							return 1





		def  _A							(							self      :		Dict  ,					lowerCamelCase__      :		Optional[Any]			):
							lowerCAmelCase						:   Optional[int]							=       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  _A							(							self      :		Any  ,					lowerCamelCase__      :		List  ,					lowerCamelCase__      :		Optional[List] = None  ,					lowerCamelCase__      :		bool = False			):
							if already_has_special_tokens:
												return self._special_token_mask(lowerCamelCase__			)
							elif token_ids_a is None:
												return self._special_token_mask(lowerCamelCase__			) + [1]
							else:
												return self._special_token_mask(token_ids_a + token_ids_a			) + [1]



def       UpperCAmelCase__							(    __magic_name__  :       str							,					__magic_name__  :       Dict[str, Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[int]							=       sentencepiece.SentencePieceProcessor(**__magic_name__					)
					spm.Load(__magic_name__					)
					return spm
def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       str					):


					'''simple docstring'''

					with open(__magic_name__							,					'''w'''					) as f:
										json.dump(__magic_name__							,					__magic_name__							,					indent=2					)
def       UpperCAmelCase__							(    __magic_name__  :       str					):


					'''simple docstring'''

					with open(__magic_name__							,					'''r'''					) as f:
										return json.load(__magic_name__					)


import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__SCREAMING_SNAKE_CASE     :				List[Any]             =       logging.get_logger(__name__)


class 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		str  ,					*lowerCamelCase__      :		Dict  ,					**lowerCamelCase__      :		int			):
							warnings.warn(
							    '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
							    ''' Please use ConditionalDetrImageProcessor instead.'''  ,					lowerCamelCase__  ,					)
							super().__init__(*lowerCamelCase__  ,					**lowerCamelCase__			)
