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

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE__   :			int												=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :			Any												=    {"vocab_file": "sentencepiece.bpe.model"}

SCREAMING_SNAKE_CASE__   :			Any												=    {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    {
    "camembert-base": 512,
}

SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    "▁"

class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Dict        =		VOCAB_FILES_NAMES
		a__       :						Union[str, Any]        =		PRETRAINED_VOCAB_FILES_MAP
		a__       :						Any        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__       :						Union[str, Any]        =		["""input_ids""", """attention_mask"""]






		def __init__(		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							str="<s>"							,			SCREAMING_SNAKE_CASE__		:							Dict="</s>"							,			SCREAMING_SNAKE_CASE__		:							Dict="</s>"							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]="<s>"							,			SCREAMING_SNAKE_CASE__		:							Tuple="<unk>"							,			SCREAMING_SNAKE_CASE__		:							Any="<pad>"							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]="<mask>"							,			SCREAMING_SNAKE_CASE__		:							Dict=["<s>NOTUSED", "</s>NOTUSED"]							,			SCREAMING_SNAKE_CASE__		:							Optional[Dict[str, Any]] = None							,			**SCREAMING_SNAKE_CASE__		:							Dict							,			)     ->							None:
						# Mask token behave like a normal word, i.e. include the space before it
						__lowerCamelCase    					=	AddedToken(SCREAMING_SNAKE_CASE__							,			lstrip=SCREAMING_SNAKE_CASE__							,			rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else mask_token

						__lowerCamelCase    					=	{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=SCREAMING_SNAKE_CASE__							,			eos_token=SCREAMING_SNAKE_CASE__							,			unk_token=SCREAMING_SNAKE_CASE__							,			sep_token=SCREAMING_SNAKE_CASE__							,			cls_token=SCREAMING_SNAKE_CASE__							,			pad_token=SCREAMING_SNAKE_CASE__							,			mask_token=SCREAMING_SNAKE_CASE__							,			additional_special_tokens=SCREAMING_SNAKE_CASE__							,			sp_model_kwargs=self.sp_model_kwargs							,			**SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(str(SCREAMING_SNAKE_CASE__  )  )
						__lowerCamelCase    					=	vocab_file
						# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
						# sentencepiece vocabulary (this is the case for <s> and </s>
						__lowerCamelCase    					=	{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
						__lowerCamelCase    					=	len(self.fairseq_tokens_to_ids  )
						__lowerCamelCase    					=	len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
						__lowerCamelCase    					=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None  )     ->							List[int]:

						if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						__lowerCamelCase    					=	[self.cls_token_id]
						__lowerCamelCase    					=	[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None							,			SCREAMING_SNAKE_CASE__		:							bool = False  )     ->							List[int]:
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=SCREAMING_SNAKE_CASE__							,			token_ids_a=SCREAMING_SNAKE_CASE__							,			already_has_special_tokens=SCREAMING_SNAKE_CASE__  )

						if token_ids_a is None:
										return [1] + ([0] * len(SCREAMING_SNAKE_CASE__  )) + [1]
						return [1] + ([0] * len(SCREAMING_SNAKE_CASE__  )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__  )) + [1]






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							List[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[List[int]] = None  )     ->							List[int]:
						__lowerCamelCase    					=	[self.sep_token_id]
						__lowerCamelCase    					=	[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]






		@property
		def  __A       (		self		:							Optional[int]  )     ->							List[str]:
						return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase    					=	{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  ): i for i in range(self.vocab_size  )}
						vocab.update(self.added_tokens_encoder  )
						return vocab






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							List[str]:
						return self.sp_model.encode(SCREAMING_SNAKE_CASE__							,			out_type=SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Optional[int]:
						if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
						elif self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__  ) == 0:
										# Convert sentence piece unk token to fairseq unk token index
										return self.unk_token_id
						return self.fairseq_offset + self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Any:
						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		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	[]
						__lowerCamelCase    					=	''''''
						__lowerCamelCase    					=	False
						for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														if not prev_is_special:
																		out_string += " "
														out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__  ) + token
														__lowerCamelCase    					=	True
														__lowerCamelCase    					=	[]
										else:
														current_sub_tokens.append(SCREAMING_SNAKE_CASE__  )
														__lowerCamelCase    					=	False
						out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__  )
						return out_string.strip()






		def __getstate__(		self		:							Union[str, Any]  )     ->							str:
						__lowerCamelCase    					=	self.__dict__.copy()
						__lowerCamelCase    					=	None
						return state






		def __setstate__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Any:
						__lowerCamelCase    					=	d

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

						__lowerCamelCase    					=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
						self.sp_model.Load(self.vocab_file  )







		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[str] = None  )     ->							Tuple[str]:
						if not os.path.isdir(SCREAMING_SNAKE_CASE__  ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
										return
						__lowerCamelCase    					=	os.path.join(
						    SCREAMING_SNAKE_CASE__							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

						if os.path.abspath(self.vocab_file  ) != os.path.abspath(SCREAMING_SNAKE_CASE__  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file							,			SCREAMING_SNAKE_CASE__  )
						elif not os.path.isfile(self.vocab_file  ):
										with open(SCREAMING_SNAKE_CASE__							,			'''wb'''  ) as fi:
														__lowerCamelCase    					=	self.sp_model.serialized_model_proto()
														fi.write(SCREAMING_SNAKE_CASE__  )

						return (out_vocab_file,)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__   :			List[str]												=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			str												=    ["BlipImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BlipModel",
					    "BlipPreTrainedModel",
					    "BlipForConditionalGeneration",
					    "BlipForQuestionAnswering",
					    "BlipVisionModel",
					    "BlipTextModel",
					    "BlipForImageTextRetrieval",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    [
					    "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFBlipModel",
					    "TFBlipPreTrainedModel",
					    "TFBlipForConditionalGeneration",
					    "TFBlipForQuestionAnswering",
					    "TFBlipVisionModel",
					    "TFBlipTextModel",
					    "TFBlipForImageTextRetrieval",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Tuple												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



