
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  :					str						    =							logging.get_logger(__name__)

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

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"blip_2_vision_model"
    def __init__(  self		,snake_case=1408		,snake_case=6144		,snake_case=39		,snake_case=16		,snake_case=224		,snake_case=14		,snake_case="gelu"		,snake_case=0.00_001		,snake_case=0.0		,snake_case=1e-10		,snake_case=True		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case      )

           lowercase							:     str       	=      hidden_size
           lowercase							:     int       	=      intermediate_size
           lowercase							:     int       	=      num_hidden_layers
           lowercase							:     Any       	=      num_attention_heads
           lowercase							:     str       	=      patch_size
           lowercase							:     Union[str, Any]       	=      image_size
           lowercase							:     List[Any]       	=      initializer_range
           lowercase							:     Tuple       	=      attention_dropout
           lowercase							:     Optional[int]       	=      layer_norm_eps
           lowercase							:     List[Any]       	=      hidden_act
           lowercase							:     str       	=      qkv_bias
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,**snake_case      ):



           '''simple docstring'''







           cls._set_token_in_kwargs(snake_case      )

           lowercase	,			lowercase							:     Union[str, Any]       	=      cls.get_config_dict(snake_case		,**snake_case      )

           # get the vision config dict if we are loading from Blip2Config
           if config_dict.get("""model_type"""      ) == "blip-2":
                  lowercase							:     Optional[int]       	=      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(snake_case		,**snake_case      )






class __snake_case		(      lowerCAmelCase     ):
    _a		:						List[str]=				"blip_2_qformer"
    def __init__(  self		,snake_case=30522		,snake_case=768		,snake_case=12		,snake_case=12		,snake_case=3072		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=0.02		,snake_case=1e-12		,snake_case=0		,snake_case="absolute"		,snake_case=2		,snake_case=1408		,**snake_case		,):



           '''simple docstring'''







           super().__init__(pad_token_id=snake_case		,**snake_case      )

           lowercase							:     Optional[Any]       	=      vocab_size
           lowercase							:     int       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_hidden_layers
           lowercase							:     int       	=      num_attention_heads
           lowercase							:     Optional[int]       	=      hidden_act
           lowercase							:     Dict       	=      intermediate_size
           lowercase							:     Dict       	=      hidden_dropout_prob
           lowercase							:     Any       	=      attention_probs_dropout_prob
           lowercase							:     Any       	=      max_position_embeddings
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     str       	=      layer_norm_eps
           lowercase							:     Union[str, Any]       	=      position_embedding_type
           lowercase							:     List[str]       	=      cross_attention_frequency
           lowercase							:     Union[str, Any]       	=      encoder_hidden_size
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,**snake_case      ):



           '''simple docstring'''







           cls._set_token_in_kwargs(snake_case      )

           lowercase	,			lowercase							:     Optional[int]       	=      cls.get_config_dict(snake_case		,**snake_case      )

           # get the qformer config dict if we are loading from Blip2Config
           if config_dict.get("""model_type"""      ) == "blip-2":
                  lowercase							:     Tuple       	=      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(snake_case		,**snake_case      )






class __snake_case		(      lowerCAmelCase     ):
    _a		:						Optional[int]=				"blip-2"
    _a		:						Optional[Any]=				True
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case=32		,**snake_case      ):



           '''simple docstring'''







           super().__init__(**snake_case      )

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

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

           lowercase							:     str       	=      BlipaVisionConfig(**snake_case      )
           lowercase							:     Union[str, Any]       	=      BlipaQFormerConfig(**snake_case      )
           lowercase							:     Optional[Any]       	=      text_config["""model_type"""] if """model_type""" in text_config else """opt"""
           lowercase							:     int       	=      CONFIG_MAPPING[text_model_type](**snake_case      )

           lowercase							:     Optional[int]       	=      self.text_config.tie_word_embeddings
           lowercase							:     Dict       	=      self.text_config.is_encoder_decoder

           lowercase							:     List[Any]       	=      num_query_tokens
           lowercase							:     int       	=      self.vision_config.hidden_size
           lowercase							:     List[str]       	=      self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           lowercase							:     List[str]       	=      1.0
           lowercase							:     Union[str, Any]       	=      0.02
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls		,snake_case		,snake_case		,snake_case		,**snake_case		,):



           '''simple docstring'''







           return cls(
               vision_config=vision_config.to_dict()		,qformer_config=qformer_config.to_dict()		,text_config=text_config.to_dict()		,**snake_case		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      copy.deepcopy(self.__dict__      )
           lowercase							:     Dict       	=      self.vision_config.to_dict()
           lowercase							:     Any       	=      self.qformer_config.to_dict()
           lowercase							:     Any       	=      self.text_config.to_dict()
           lowercase							:     int       	=      self.__class__.model_type
           return output







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




