

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


snake_case_			  = logging.get_logger(__name__)

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


class       SCREAMING_SNAKE_CASE__					(_lowerCamelCase     ):
    __lowerCamelCase						:       Optional[Any]														=     """blip_2_vision_model"""





    def __init__(      self						,   a=1408						,   a=6144						,   a=39						,   a=16						,   a=224						,   a=14						,   a="gelu"						,   a=0.00_001						,   a=0.0						,   a=1e-10						,   a=True						,   **a						,   ):
         super().__init__(**_UpperCamelCase)

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





    @classmethod
    def       snake_case_				(      cls						,   a						,   **a):
         cls._set_token_in_kwargs(_UpperCamelCase)

         lowercase__      , lowercase__    :   Dict      =       cls.get_config_dict(_UpperCamelCase						,   **_UpperCamelCase)

         # get the vision config dict if we are loading from Blip2Config
         if config_dict.get('model_type') == "blip-2":
              lowercase__    :   Dict      =       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(_UpperCamelCase						,   **_UpperCamelCase)



class       SCREAMING_SNAKE_CASE__					(_lowerCamelCase     ):
    __lowerCamelCase						:       Any														=     """blip_2_qformer"""





    def __init__(      self						,   a=3_0522						,   a=768						,   a=12						,   a=12						,   a=3072						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=512						,   a=0.02						,   a=1e-12						,   a=0						,   a="absolute"						,   a=2						,   a=1408						,   **a						,   ):
         super().__init__(pad_token_id=_UpperCamelCase						,   **_UpperCamelCase)

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





    @classmethod
    def       snake_case_				(      cls						,   a						,   **a):
         cls._set_token_in_kwargs(_UpperCamelCase)

         lowercase__      , lowercase__    :   List[Any]      =       cls.get_config_dict(_UpperCamelCase						,   **_UpperCamelCase)

         # get the qformer config dict if we are loading from Blip2Config
         if config_dict.get('model_type') == "blip-2":
              lowercase__    :   List[str]      =       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(_UpperCamelCase						,   **_UpperCamelCase)



class       SCREAMING_SNAKE_CASE__					(_lowerCamelCase     ):
    __lowerCamelCase						:       Any														=     """blip-2"""
    __lowerCamelCase						:       str														=     True





    def __init__(      self						,   a=None						,   a=None						,   a=None						,   a=32						,   **a):
         super().__init__(**_UpperCamelCase)

         if vision_config is None:
              lowercase__    :   List[str]      =       {}
              logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.')

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

         if text_config is None:
              lowercase__    :   Optional[int]      =       {}
              logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).')

         lowercase__    :   str      =       BlipaVisionConfig(**_UpperCamelCase)
         lowercase__    :   Tuple      =       BlipaQFormerConfig(**_UpperCamelCase)
         lowercase__    :   List[str]      =       text_config['model_type'] if 'model_type' in text_config else 'opt'
         lowercase__    :   Optional[Any]      =       CONFIG_MAPPING[text_model_type](**_UpperCamelCase)

         lowercase__    :   Tuple      =       self.text_config.tie_word_embeddings
         lowercase__    :   Tuple      =       self.text_config.is_encoder_decoder

         lowercase__    :   Dict      =       num_query_tokens
         lowercase__    :   List[str]      =       self.vision_config.hidden_size
         lowercase__    :   str      =       self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         lowercase__    :   List[Any]      =       1.0
         lowercase__    :   List[Any]      =       0.02





    @classmethod
    def       snake_case_				(      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()						,   **_UpperCamelCase						,   )





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       copy.deepcopy(self.__dict__)
         lowercase__    :   List[str]      =       self.vision_config.to_dict()
         lowercase__    :   Tuple      =       self.qformer_config.to_dict()
         lowercase__    :   Tuple      =       self.text_config.to_dict()
         lowercase__    :   Any      =       self.__class__.model_type
         return output








from __future__ import annotations



def _a				(	lowerCamelCase:   list[int | str] )	->						None:







      '''simple docstring'''






      create_state_space_tree(lowerCamelCase					,				[]					,				0					,				[0 for i in range(len(lowerCamelCase ) )] )



def _a				(	lowerCamelCase:   list[int | str]					,				lowerCamelCase:   list[int | str]					,				lowerCamelCase:   int					,				lowerCamelCase:   list[int]					,				)	->						None:







      '''simple docstring'''






      if index == len(lowerCamelCase ):
            print(lowerCamelCase )
            return

      for i in range(len(lowerCamelCase ) ):
            if not index_used[i]:
                  current_sequence.append(sequence[i] )
                  __A          =				True
                  create_state_space_tree(lowerCamelCase					,				lowerCamelCase					,				index + 1					,				lowerCamelCase )
                  current_sequence.pop()
                  __A          =				False
snake_case__					:							list[int | str]													=     [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case__					:							list[int | str]													=     ["A", "B", "C"]







generate_all_permutations(sequence_a)





