
'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__				:	List[Any]					=						{
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__				:	List[str]					=						['''FlavaFeatureExtractor''']
		UpperCAmelCase__				:	List[str]					=						['''FlavaImageProcessor''']
		UpperCAmelCase__				:	Optional[int]					=						['''FlavaProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase__				:	Optional[Any]					=						[
		    '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlavaForPreTraining''',
		    '''FlavaImageCodebook''',
		    '''FlavaImageModel''',
		    '''FlavaModel''',
		    '''FlavaMultimodalModel''',
		    '''FlavaPreTrainedModel''',
		    '''FlavaTextModel''',
		]

if TYPE_CHECKING:
		from .configuration_flava import (
		    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    FlavaConfig,
		    FlavaImageCodebookConfig,
		    FlavaImageConfig,
		    FlavaMultimodalConfig,
		    FlavaTextConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_flava import FlavaFeatureExtractor
				from .image_processing_flava import FlavaImageProcessor
				from .processing_flava import FlavaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flava import (
				    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlavaForPreTraining,
				    FlavaImageCodebook,
				    FlavaImageModel,
				    FlavaModel,
				    FlavaMultimodalModel,
				    FlavaPreTrainedModel,
				    FlavaTextModel,
				)

else:
		import sys

		UpperCAmelCase__				:	str					=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


'''simple docstring'''

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


UpperCAmelCase__				:	Tuple					=						logging.get_logger(__name__)

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

UpperCAmelCase__				:	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"
        )
    },
}

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






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





	def __init__(   self     :    Tuple ,							__magic_name__     :    str ,							__magic_name__     :    Tuple ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    Optional[Any]=None ,							__magic_name__     :    Optional[Any]=None ,							__magic_name__     :    str=None ,							__magic_name__     :    str="<unk>" ,							__magic_name__     :    Dict="</s>" ,							__magic_name__     :    List[str]="<pad>" ,							__magic_name__     :    str=512 ,							__magic_name__     :    Optional[Dict[str, Any]] = None ,							__magic_name__     :    Union[str, Any]=False ,							**__magic_name__     :    int ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							{} 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=__magic_name__ ,							target_lang=__magic_name__ ,							unk_token=__magic_name__ ,							eos_token=__magic_name__ ,							pad_token=__magic_name__ ,							model_max_length=__magic_name__ ,							sp_model_kwargs=self.sp_model_kwargs ,							target_vocab_file=__magic_name__ ,							separate_vocabs=__magic_name__ ,							**__magic_name__ ,							)
				assert Path(__magic_name__							).exists(), f"""cannot find spm source {source_spm}"""

				lowerCAmelCase__									=							separate_vocabs
				lowerCAmelCase__									=							load_json(__magic_name__							)
				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__									=							load_json(__magic_name__							)
							lowerCAmelCase__									=							{v: k for k, v in self.target_encoder.items()}
							lowerCAmelCase__									=							[]
				else:
							lowerCAmelCase__									=							{v: k for k, v in self.encoder.items()}
							lowerCAmelCase__									=							[k for k in self.encoder if k.startswith(">>"							) and k.endswith("<<"							)]

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

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

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

				self._setup_normalizer()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				try:
							from sacremoses import MosesPunctNormalizer

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    str							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    List[str]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.language_code_re.match(__magic_name__							)
				lowerCAmelCase__									=							[match.group(0							)] if match else []
				return code, self.language_code_re.sub("" ,							__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    str							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.remove_language_code(__magic_name__							)
				lowerCAmelCase__									=							self.current_spm.encode(__magic_name__ ,							out_type=__magic_name__							)
				return code + pieces





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    int							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    Optional[int] ,							**__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict ,							__magic_name__     :    Union[str, Any] ,							**__magic_name__     :    Optional[int]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.spm_source if self._decode_use_source_tokenizer else self.spm_target
				lowerCAmelCase__									=							[]
				lowerCAmelCase__									=							""
				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(__magic_name__							) + token + " "
										lowerCAmelCase__									=							[]
							else:
										current_sub_tokens.append(__magic_name__							)
				out_string += sp_model.decode_pieces(__magic_name__							)
				return out_string.strip()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Dict ,							__magic_name__     :    Union[str, Any]=None							):


				"""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[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.spm_source
				lowerCAmelCase__									=							self.encoder





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

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





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				return len(self.encoder							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    str ,							__magic_name__     :    Optional[str] = None							):


				"""simple docstring"""

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

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

				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__									=							os.path.join(
							    __magic_name__ ,							(filename_prefix + "-" if filename_prefix else "") + spm_save_filename							)
							if os.path.abspath(__magic_name__							) != os.path.abspath(__magic_name__							) and os.path.isfile(__magic_name__							):
										copyfile(__magic_name__ ,							__magic_name__							)
										saved_files.append(__magic_name__							)
							elif not os.path.isfile(__magic_name__							):
										with open(__magic_name__ ,							"wb"							) as fi:
													lowerCAmelCase__									=							spm_model.serialized_model_proto()
													fi.write(__magic_name__							)
										saved_files.append(__magic_name__							)

				return tuple(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    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     :    List[Any]							):


				"""simple docstring"""

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





	def __getstate__(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							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     :    Dict ,							__magic_name__     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							d

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

				lowerCAmelCase__		,lowerCAmelCase__									=							(load_spm(__magic_name__ ,							self.sp_model_kwargs							) for f in self.spm_files)
				lowerCAmelCase__									=							self.spm_source
				self._setup_normalizer()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							*__magic_name__     :    Any ,							**__magic_name__     :    Tuple							):


				"""simple docstring"""

				return 1





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							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     :    Optional[int] ,							__magic_name__     :    List ,							__magic_name__     :    Optional[List] = None ,							__magic_name__     :    bool = False							):


				"""simple docstring"""

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







def 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       Dict[str, Any] )   ->    sentencepiece.SentencePieceProcessor:







			'''simple docstring'''



			lowerCAmelCase__									=							sentencepiece.SentencePieceProcessor(**UpperCamelCase_ )
			spm.Load(UpperCamelCase_ )
			return spm







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       str )   ->    None:







			'''simple docstring'''



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







def 					A       ( UpperCamelCase_   :       str )   ->    Union[Dict, List]:







			'''simple docstring'''



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





