





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


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

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


class       __lowerCAmelCase					(       __a      ):


	'''simple docstring'''





	_A													=				"blip_2_vision_model"

	def __init__(						self:						Optional[int],					lowerCamelCase_:						List[Any]=1408,					lowerCamelCase_:						Any=6144,					lowerCamelCase_:						int=39,					lowerCamelCase_:						int=16,					lowerCamelCase_:						int=224,					lowerCamelCase_:						Any=14,					lowerCamelCase_:						Dict="gelu",					lowerCamelCase_:						Optional[Any]=0.0_0_0_0_1,					lowerCamelCase_:						Union[str, Any]=0.0,					lowerCamelCase_:						Tuple=1E-10,					lowerCamelCase_:						Optional[int]=True,					**lowerCamelCase_:						str,					):
			super().__init__(**lowercase_			)

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





	@classmethod
	def    snake_case__(						cls:						List[str],					lowerCamelCase_:						Union[str, os.PathLike],					**lowerCamelCase_:						Tuple			):
			cls._set_token_in_kwargs(lowercase_			)

			lowercase__   ,				lowercase__					:      Optional[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":
					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(lowercase_,					**lowercase_			)


class       __lowerCAmelCase					(       __a      ):


	'''simple docstring'''





	_A													=				"blip_2_qformer"

	def __init__(						self:						List[str],					lowerCamelCase_:						List[str]=30522,					lowerCamelCase_:						Tuple=768,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Optional[int]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						Union[str, Any]="gelu",					lowerCamelCase_:						Union[str, Any]=0.1,					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						Dict=0.0_2,					lowerCamelCase_:						List[Any]=1E-12,					lowerCamelCase_:						Any=0,					lowerCamelCase_:						Dict="absolute",					lowerCamelCase_:						Tuple=2,					lowerCamelCase_:						List[Any]=1408,					**lowerCamelCase_:						Any,					):
			super().__init__(pad_token_id=lowercase_,					**lowercase_			)

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





	@classmethod
	def    snake_case__(						cls:						List[Any],					lowerCamelCase_:						Union[str, os.PathLike],					**lowerCamelCase_:						Optional[int]			):
			cls._set_token_in_kwargs(lowercase_			)

			lowercase__   ,				lowercase__					:      List[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":
					lowercase__					:      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(lowercase_,					**lowercase_			)


class       __lowerCAmelCase					(       __a      ):


	'''simple docstring'''





	_A													=				"blip-2"
	_A													=				True

	def __init__(						self:						List[str],					lowerCamelCase_:						Dict=None,					lowerCamelCase_:						Dict=None,					lowerCamelCase_:						Any=None,					lowerCamelCase_:						Optional[Any]=32,					**lowerCamelCase_:						Dict			):
			super().__init__(**lowercase_			)

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

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

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

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

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

			lowercase__					:      Optional[int]      =		num_query_tokens
			lowercase__					:      Any      =		self.vision_config.hidden_size
			lowercase__					:      int      =		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
			lowercase__					:      Dict      =		1.0
			lowercase__					:      Any      =		0.0_2

	@classmethod
	def    snake_case__(						cls:						Union[str, Any],					lowerCamelCase_:						BlipaVisionConfig,					lowerCamelCase_:						BlipaQFormerConfig,					lowerCamelCase_:						PretrainedConfig,					**lowerCamelCase_:						Dict,					):
			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:						Optional[int]			):
			lowercase__					:      Optional[Any]      =		copy.deepcopy(self.__dict__			)
			lowercase__					:      Optional[Any]      =		self.vision_config.to_dict()
			lowercase__					:      Optional[Any]      =		self.qformer_config.to_dict()
			lowercase__					:      Tuple      =		self.text_config.to_dict()
			lowercase__					:      Optional[Any]      =		self.__class__.model_type
			return output



'''simple docstring'''


import math
import sys

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  if number != int(SCREAMING_SNAKE_CASE_						):
    raise ValueError("""the value of input must be a natural number"""						)
  if number < 0:
    raise ValueError("""the value of input must not be a negative number"""						)
  if number == 0:
    return 1
  lowercase_     =       [-1] * (number + 1)
  lowercase_     =       0
  for i in range(1 ,	number + 1						):
    lowercase_     =       sys.maxsize
    lowercase_     =       int(math.sqrt(SCREAMING_SNAKE_CASE_						)						)
    for j in range(1 ,	root + 1						):
      lowercase_     =       1 + answers[i - (j**2)]
      lowercase_     =       min(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       answer
  return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

