
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase__		:Dict			  =   logging.get_logger(__name__)

lowercase__		:int			  =   {
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[str]	 ='''blip_2_vision_model'''




							def __init__(    self       ,A__=1_4_0_8       ,A__=6_1_4_4       ,A__=3_9       ,A__=1_6       ,A__=2_2_4       ,A__=1_4       ,A__="gelu"       ,A__=0.00001       ,A__=0.0       ,A__=1E-10       ,A__=True       ,**A__       ,):
												super().__init__(**A__)

												lowercase						     =						hidden_size
												lowercase						     =						intermediate_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						patch_size
												lowercase						     =						image_size
												lowercase						     =						initializer_range
												lowercase						     =						attention_dropout
												lowercase						     =						layer_norm_eps
												lowercase						     =						hidden_act
												lowercase						     =						qkv_bias





							@classmethod
							def 			A__				(    cls       ,A__       ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase     ,						lowercase						     =						cls.get_config_dict(A__       ,**A__)

												# get the vision config dict if we are loading from Blip2Config
												if config_dict.get('''model_type''') == "blip-2":
																	lowercase						     =						config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls       ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__       ,**A__)







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[int]	 ='''blip_2_qformer'''




							def __init__(    self       ,A__=3_0_5_2_2       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=0.02       ,A__=1E-12       ,A__=0       ,A__="absolute"       ,A__=2       ,A__=1_4_0_8       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,**A__)

												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						hidden_act
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						position_embedding_type
												lowercase						     =						cross_attention_frequency
												lowercase						     =						encoder_hidden_size





							@classmethod
							def 			A__				(    cls       ,A__       ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase     ,						lowercase						     =						cls.get_config_dict(A__       ,**A__)

												# get the qformer config dict if we are loading from Blip2Config
												if config_dict.get('''model_type''') == "blip-2":
																	lowercase						     =						config_dict['''qformer_config''']

												if "model_type" in config_dict and hasattr(cls       ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__       ,**A__)







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[str]	 ='''blip-2'''
							lowercase_	:	Any	 =True




							def __init__(    self       ,A__=None       ,A__=None       ,A__=None       ,A__=3_2       ,**A__):
												super().__init__(**A__)

												if vision_config is None:
																	lowercase						     =						{}
																	logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''')

												if qformer_config is None:
																	lowercase						     =						{}
																	logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''')

												if text_config is None:
																	lowercase						     =						{}
																	logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''')

												lowercase						     =						BlipaVisionConfig(**A__)
												lowercase						     =						BlipaQFormerConfig(**A__)
												lowercase						     =						text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
												lowercase						     =						CONFIG_MAPPING[text_model_type](**A__)

												lowercase						     =						self.text_config.tie_word_embeddings
												lowercase						     =						self.text_config.is_encoder_decoder

												lowercase						     =						num_query_tokens
												lowercase						     =						self.vision_config.hidden_size
												lowercase						     =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
												lowercase						     =						1.0
												lowercase						     =						0.02




							@classmethod
							def 			A__				(    cls       ,A__       ,A__       ,A__       ,**A__       ,):
												return cls(
												    vision_config=vision_config.to_dict()       ,qformer_config=qformer_config.to_dict()       ,text_config=text_config.to_dict()       ,**A__       ,)





							def 			A__				(    self):
												lowercase						     =						copy.deepcopy(self.__dict__)
												lowercase						     =						self.vision_config.to_dict()
												lowercase						     =						self.qformer_config.to_dict()
												lowercase						     =						self.text_config.to_dict()
												lowercase						     =						self.__class__.model_type
												return output






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


lowercase__		:str			  =   logging.get_logger(__name__)

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

lowercase__		:Tuple			  =   {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

lowercase__		:str			  =   {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

lowercase__		:int			  =   "▁"







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Union[str, Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	List[Any]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Tuple	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




							def __init__(    self       ,A__       ,A__="<s>"       ,A__="</s>"       ,A__="</s>"       ,A__="<s>"       ,A__="<unk>"       ,A__="<pad>"       ,A__="<mask>"       ,A__ = None       ,**A__       ,):
												# Mask token behave like a normal word, i.e. include the space before it
												lowercase						     =						AddedToken(A__       ,lstrip=A__       ,rstrip=A__) if isinstance(A__       ,A__) else mask_token

												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,unk_token=A__       ,sep_token=A__       ,cls_token=A__       ,pad_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												lowercase						     =						vocab_file
												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))

												lowercase						     =						{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												lowercase						     =						len(self.sp_model) - 1
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def 			A__				(    self       ,A__       ,A__ = None):

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




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = 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__)

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




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[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):
												return len(self.sp_model)




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												return spm_id if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						[]
												lowercase						     =						''''''
												lowercase						     =						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(A__) + token
																						lowercase						     =						True
																						lowercase						     =						[]
																	else:
																						current_sub_tokens.append(A__)
																						lowercase						     =						False
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

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

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





							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

												return (out_vocab_file,)




