'''simple docstring'''




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


_a   :	Tuple 							=   logging.get_logger(__name__)

_a   :	List[Any] 							=   {
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class 		__A       (UpperCAmelCase_   ):
       snake_case       :List[Any]				    =					'blip_2_vision_model'






       def __init__(      self				,     UpperCamelCase_=14_08				,     UpperCamelCase_=61_44				,     UpperCamelCase_=39				,     UpperCamelCase_=16				,     UpperCamelCase_=2_24				,     UpperCamelCase_=14				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.0_0_0_0_1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=1E-10				,     UpperCamelCase_=True				,     **UpperCamelCase_				,     ):
              super().__init__(**_lowercase      )

              __UpperCAmelCase  :				Optional[int]       				=		hidden_size
              __UpperCAmelCase  :				Dict       				=		intermediate_size
              __UpperCAmelCase  :				str       				=		num_hidden_layers
              __UpperCAmelCase  :				List[Any]       				=		num_attention_heads
              __UpperCAmelCase  :				Any       				=		patch_size
              __UpperCAmelCase  :				Dict       				=		image_size
              __UpperCAmelCase  :				Optional[Any]       				=		initializer_range
              __UpperCAmelCase  :				str       				=		attention_dropout
              __UpperCAmelCase  :				Dict       				=		layer_norm_eps
              __UpperCAmelCase  :				Tuple       				=		hidden_act
              __UpperCAmelCase  :				Optional[int]       				=		qkv_bias







       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     **UpperCamelCase_      ):
              cls._set_token_in_kwargs(_lowercase      )

              __UpperCAmelCase  :				Any       				=		cls.get_config_dict(_lowercase				,     **_lowercase      )

              # get the vision config dict if we are loading from Blip2Config
              if config_dict.get("model_type"      ) == "blip-2":
                     __UpperCAmelCase  :				Union[str, Any]       				=		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(_lowercase				,     **_lowercase      )






class 		__A       (UpperCAmelCase_   ):
       snake_case       :int				    =					'blip_2_qformer'






       def __init__(      self				,     UpperCamelCase_=3_05_22				,     UpperCamelCase_=7_68				,     UpperCamelCase_=12				,     UpperCamelCase_=12				,     UpperCamelCase_=30_72				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     UpperCamelCase_=0				,     UpperCamelCase_="absolute"				,     UpperCamelCase_=2				,     UpperCamelCase_=14_08				,     **UpperCamelCase_				,     ):
              super().__init__(pad_token_id=_lowercase				,     **_lowercase      )

              __UpperCAmelCase  :				List[Any]       				=		vocab_size
              __UpperCAmelCase  :				Tuple       				=		hidden_size
              __UpperCAmelCase  :				Optional[int]       				=		num_hidden_layers
              __UpperCAmelCase  :				Tuple       				=		num_attention_heads
              __UpperCAmelCase  :				Dict       				=		hidden_act
              __UpperCAmelCase  :				Optional[Any]       				=		intermediate_size
              __UpperCAmelCase  :				Optional[int]       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Any       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				List[Any]       				=		max_position_embeddings
              __UpperCAmelCase  :				Any       				=		initializer_range
              __UpperCAmelCase  :				Tuple       				=		layer_norm_eps
              __UpperCAmelCase  :				Optional[int]       				=		position_embedding_type
              __UpperCAmelCase  :				List[str]       				=		cross_attention_frequency
              __UpperCAmelCase  :				Optional[Any]       				=		encoder_hidden_size







       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     **UpperCamelCase_      ):
              cls._set_token_in_kwargs(_lowercase      )

              __UpperCAmelCase  :				str       				=		cls.get_config_dict(_lowercase				,     **_lowercase      )

              # get the qformer config dict if we are loading from Blip2Config
              if config_dict.get("model_type"      ) == "blip-2":
                     __UpperCAmelCase  :				Union[str, Any]       				=		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(_lowercase				,     **_lowercase      )






class 		__A       (UpperCAmelCase_   ):
       snake_case       :Optional[Any]				    =					'blip-2'
       snake_case       :str				    =					True






       def __init__(      self				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=32				,     **UpperCamelCase_      ):
              super().__init__(**_lowercase      )

              if vision_config is None:
                     __UpperCAmelCase  :				Tuple       				=		{}
                     logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."      )

              if qformer_config is None:
                     __UpperCAmelCase  :				str       				=		{}
                     logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."      )

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

              __UpperCAmelCase  :				List[str]       				=		BlipaVisionConfig(**_lowercase      )
              __UpperCAmelCase  :				Union[str, Any]       				=		BlipaQFormerConfig(**_lowercase      )
              __UpperCAmelCase  :				List[Any]       				=		text_config['model_type'] if 'model_type' in text_config else 'opt'
              __UpperCAmelCase  :				Any       				=		CONFIG_MAPPING[text_model_type](**_lowercase      )

              __UpperCAmelCase  :				int       				=		self.text_config.tie_word_embeddings
              __UpperCAmelCase  :				int       				=		self.text_config.is_encoder_decoder

              __UpperCAmelCase  :				Tuple       				=		num_query_tokens
              __UpperCAmelCase  :				Tuple       				=		self.vision_config.hidden_size
              __UpperCAmelCase  :				Union[str, Any]       				=		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
              __UpperCAmelCase  :				Any       				=		1.0
              __UpperCAmelCase  :				Union[str, Any]       				=		0.0_2






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







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		copy.deepcopy(self.__dict__      )
              __UpperCAmelCase  :				int       				=		self.vision_config.to_dict()
              __UpperCAmelCase  :				Any       				=		self.qformer_config.to_dict()
              __UpperCAmelCase  :				Optional[int]       				=		self.text_config.to_dict()
              __UpperCAmelCase  :				str       				=		self.__class__.model_type
              return output







'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


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


_a   :	int 							=   {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
_a   :	Tuple 							=   {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

_a   :	List[Any] 							=   {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}

@lru_cache()
def _lowercase	(      )					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		(
           list(range(ord("!"						)							,    ord("~"						) + 1						)						) + list(range(ord("¡"						)							,    ord("¬"						) + 1						)						) + list(range(ord("®"						)							,    ord("ÿ"						) + 1						)						)
       )
       __UpperCAmelCase  :				Optional[Any]       				=		bs[:]
       __UpperCAmelCase  :				Optional[int]       				=		0
       for b in range(2**8						):
              if b not in bs:
                     bs.append(lowerCamelCase__						)
                     cs.append(2**8 + n						)
                     n += 1
       __UpperCAmelCase  :				Dict       				=		[chr(lowerCamelCase__						) for n in cs]
       return dict(zip(lowerCamelCase__							,    lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		set()
       __UpperCAmelCase  :				Union[str, Any]       				=		word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              __UpperCAmelCase  :				Optional[Any]       				=		char
       return pairs






class 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					VOCAB_FILES_NAMES
       snake_case       :List[Any]				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Any				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Optional[int]				    =					["input_ids", "attention_mask"]






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_="replace"				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="<unk>"				,     UpperCamelCase_="<pad>"				,     UpperCamelCase_="<mask>"				,     UpperCamelCase_=False				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				str       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else bos_token
              __UpperCAmelCase  :				List[str]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else eos_token
              __UpperCAmelCase  :				Optional[int]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else sep_token
              __UpperCAmelCase  :				int       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else cls_token
              __UpperCAmelCase  :				Optional[int]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else unk_token
              __UpperCAmelCase  :				Dict       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else pad_token

              # Mask token behave like a normal word, i.e. include the space before it
              __UpperCAmelCase  :				Union[str, Any]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else mask_token

              super().__init__(
                  errors=UpperCamelCase_				,     bos_token=UpperCamelCase_				,     eos_token=UpperCamelCase_				,     unk_token=UpperCamelCase_				,     sep_token=UpperCamelCase_				,     cls_token=UpperCamelCase_				,     pad_token=UpperCamelCase_				,     mask_token=UpperCamelCase_				,     add_prefix_space=UpperCamelCase_				,     **UpperCamelCase_				,     )

              with open(UpperCamelCase_				,     encoding="utf-8"      ) as vocab_handle:
                     __UpperCAmelCase  :				int       				=		json.load(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		{v: k for k, v in self.encoder.items()}
              __UpperCAmelCase  :				Any       				=		errors  # how to handle errors in decoding
              __UpperCAmelCase  :				str       				=		bytes_to_unicode()
              __UpperCAmelCase  :				List[str]       				=		{v: k for k, v in self.byte_encoder.items()}
              with open(UpperCamelCase_				,     encoding="utf-8"      ) as merges_handle:
                     __UpperCAmelCase  :				str       				=		merges_handle.read().split("\n"      )[1:-1]
              __UpperCAmelCase  :				List[str]       				=		[tuple(merge.split()      ) for merge in bpe_merges]
              __UpperCAmelCase  :				Union[str, Any]       				=		dict(zip(UpperCamelCase_				,     range(len(UpperCamelCase_      )      )      )      )
              __UpperCAmelCase  :				Optional[int]       				=		{}
              __UpperCAmelCase  :				Optional[int]       				=		add_prefix_space

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              __UpperCAmelCase  :				Dict       				=		re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"      )






       @property
       def 			_snake_case						(      self      ):
              return len(self.encoder      )






       def 			_snake_case						(      self      ):
              return dict(self.encoder				,     **self.added_tokens_encoder      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if token in self.cache:
                     return self.cache[token]
              __UpperCAmelCase  :				List[str]       				=		tuple(UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		get_pairs(UpperCamelCase_      )

              if not pairs:
                     return token

              while True:
                     __UpperCAmelCase  :				str       				=		min(UpperCamelCase_				,     key=lambda UpperCamelCase_      : self.bpe_ranks.get(UpperCamelCase_				,     float("inf"      )      )      )
                     if bigram not in self.bpe_ranks:
                            break
                     __UpperCAmelCase				, __UpperCAmelCase  :				List[Any]       				=		bigram
                     __UpperCAmelCase  :				Any       				=		[]
                     __UpperCAmelCase  :				List[str]       				=		0
                     while i < len(UpperCamelCase_      ):
                            try:
                                   __UpperCAmelCase  :				Union[str, Any]       				=		word.index(UpperCamelCase_				,     UpperCamelCase_      )
                            except ValueError:
                                   new_word.extend(word[i:]      )
                                   break
                            else:
                                   new_word.extend(word[i:j]      )
                                   __UpperCAmelCase  :				str       				=		j

                            if word[i] == first and i < len(UpperCamelCase_      ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second      )
                                   i += 2
                            else:
                                   new_word.append(word[i]      )
                                   i += 1
                     __UpperCAmelCase  :				Dict       				=		tuple(UpperCamelCase_      )
                     __UpperCAmelCase  :				str       				=		new_word
                     if len(UpperCamelCase_      ) == 1:
                            break
                     else:
                            __UpperCAmelCase  :				int       				=		get_pairs(UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		" ".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		word
              return word






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[Any]       				=		[]
              for token in re.findall(self.pat				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				Any       				=		"".join(
                         self.byte_encoder[b] for b in token.encode("utf-8"      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase_      ).split(" "      )      )
              return bpe_tokens






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.encoder.get(UpperCamelCase_				,     self.encoder.get(self.unk_token      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.decoder.get(UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		"".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		bytearray([self.byte_decoder[c] for c in text]      ).decode("utf-8"				,     errors=self.errors      )
              return text






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if not os.path.isdir(UpperCamelCase_      ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
                     return
              __UpperCAmelCase  :				Any       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
              __UpperCAmelCase  :				Optional[int]       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]      )

              with open(UpperCamelCase_				,     "w"				,     encoding="utf-8"      ) as f:
                     f.write(json.dumps(self.encoder				,     indent=2				,     sort_keys=UpperCamelCase_				,     ensure_ascii=UpperCamelCase_      ) + "\n"      )

              __UpperCAmelCase  :				str       				=		0
              with open(UpperCamelCase_				,     "w"				,     encoding="utf-8"      ) as writer:
                     writer.write("#version: 0.2\n"      )
                     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,     key=lambda UpperCamelCase_      : kv[1]      ):
                            if index != token_index:
                                   logger.warning(
                                       f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                       " Please check that the tokenizer is not corrupted!"      )
                                   __UpperCAmelCase  :				str       				=		token_index
                            writer.write(" ".join(UpperCamelCase_      ) + "\n"      )
                            index += 1

              return vocab_file, merge_file






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __UpperCAmelCase  :				List[Any]       				=		[self.cls_token_id]
              __UpperCAmelCase  :				Tuple       				=		[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False      ):
              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=UpperCamelCase_				,     token_ids_a=UpperCamelCase_				,     already_has_special_tokens=UpperCamelCase_      )

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				int       				=		[self.sep_token_id]
              __UpperCAmelCase  :				List[str]       				=		[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]







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=False				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		kwargs.pop("add_prefix_space"				,     self.add_prefix_space      )
              if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_      ) > 0 and not text[0].isspace()):
                     __UpperCAmelCase  :				Tuple       				=		" " + text
              return (text, kwargs)






