


'''simple docstring'''







import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__  :					Any        =       logging.get_logger(__name__)

a__  :					Tuple        =       {
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}
class 					lowercase_     (    __lowerCamelCase   ):
    __UpperCAmelCase   			=	"""blip_text_model"""


    def __init__(     self  ,					a=3_05_24  ,					a=7_68  ,					a=7_68  ,					a=30_72  ,					a=7_68  ,					a=12  ,					a=8  ,					a=5_12  ,					a="gelu"  ,					a=1e-12  ,					a=0.0  ,					a=0.0  ,					a=0.02  ,					a=3_05_22  ,					a=2  ,					a=0  ,					a=1_02  ,					a=True  ,					a=True  ,					**a  ,					):
          super().__init__(
              pad_token_id=a  ,					bos_token_id=a  ,					eos_token_id=a  ,					sep_token_id=a  ,					**a  ,					)

          UpperCamelCase__										=				vocab_size
          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				encoder_hidden_size
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				projection_dim
          UpperCamelCase__										=				hidden_dropout_prob
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				max_position_embeddings
          UpperCamelCase__										=				layer_norm_eps
          UpperCamelCase__										=				hidden_act
          UpperCamelCase__										=				initializer_range
          UpperCamelCase__										=				attention_probs_dropout_prob
          UpperCamelCase__										=				is_decoder
          UpperCamelCase__										=				use_cache


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

          UpperCamelCase__					,	UpperCamelCase__										=				cls.get_config_dict(a  ,					**a    )

          # get the text config dict if we are loading from BlipConfig
          if config_dict.get("model_type"    ) == "blip":
                UpperCamelCase__										=				config_dict["text_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_     (    __lowerCamelCase   ):
    __UpperCAmelCase   			=	"""blip_vision_model"""


    def __init__(     self  ,					a=7_68  ,					a=30_72  ,					a=5_12  ,					a=12  ,					a=12  ,					a=3_84  ,					a=16  ,					a="gelu"  ,					a=1e-5  ,					a=0.0  ,					a=1e-10  ,					**a  ,					):
          super().__init__(**a    )

          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				projection_dim
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				patch_size
          UpperCamelCase__										=				image_size
          UpperCamelCase__										=				initializer_range
          UpperCamelCase__										=				attention_dropout
          UpperCamelCase__										=				layer_norm_eps
          UpperCamelCase__										=				hidden_act


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

          UpperCamelCase__					,	UpperCamelCase__										=				cls.get_config_dict(a  ,					**a    )

          # get the vision config dict if we are loading from BlipConfig
          if config_dict.get("model_type"    ) == "blip":
                UpperCamelCase__										=				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_     (    __lowerCamelCase   ):
    __UpperCAmelCase   			=	"""blip"""
    __UpperCAmelCase   			=	True


    def __init__(     self  ,					a=None  ,					a=None  ,					a=5_12  ,					a=2.6592  ,					a=2_56  ,					**a  ,					):
          super().__init__(**a    )

          if text_config is None:
                UpperCamelCase__										=				{}
                logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."    )

          if vision_config is None:
                UpperCamelCase__										=				{}
                logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."    )

          UpperCamelCase__										=				BlipTextConfig(**a    )
          UpperCamelCase__										=				BlipVisionConfig(**a    )

          UpperCamelCase__										=				self.vision_config.hidden_size

          UpperCamelCase__										=				projection_dim
          UpperCamelCase__										=				logit_scale_init_value
          UpperCamelCase__										=				1.0
          UpperCamelCase__										=				0.02
          UpperCamelCase__										=				image_text_hidden_size


    @classmethod
    def    __a							(     cls  ,					a  ,					a  ,					**a    ):
          return cls(text_config=text_config.to_dict()  ,					vision_config=vision_config.to_dict()  ,					**a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				copy.deepcopy(self.__dict__    )
          UpperCamelCase__										=				self.text_config.to_dict()
          UpperCamelCase__										=				self.vision_config.to_dict()
          UpperCamelCase__										=				self.__class__.model_type
          return output







def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    max(
			    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							j - wt[i - 1]							) + val[i - 1] ,							)
		lowercase       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[0] * (w + 1) for _ in range(n + 1							)]

	for i in range(1 ,							n + 1							):
		for w_ in range(1 ,							w + 1							):
			if wt[i - 1] <= w_:
				lowercase       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not (isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							) and isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							)):
		raise ValueError(
		    'Both the weights and values vectors must be either lists or tuples'							)

	lowercase       =    len(__SCREAMING_SNAKE_CASE							)
	if num_items != len(__SCREAMING_SNAKE_CASE							):
		lowercase       =    (
		    'The number of weights must be the same as the number of values.\n'
		    F'''But got {num_items} weights and {len(__SCREAMING_SNAKE_CASE							)} values'''
		)
		raise ValueError(__SCREAMING_SNAKE_CASE							)
	for i in range(__SCREAMING_SNAKE_CASE							):
		if not isinstance(wt[i] ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    (
			    'All weights must be integers but got weight of '
			    F'''type {type(wt[i]							)} at index {i}'''
			)
			raise TypeError(__SCREAMING_SNAKE_CASE							)

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    set()
	_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	return optimal_val, example_optional_set






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			optimal_set.add(__SCREAMING_SNAKE_CASE							)
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							j - wt[i - 1] ,							__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = [3, 2, 4, 4]
	UpperCAmelCase	      = [4, 3, 2, 3]
	UpperCAmelCase	      = 4
	UpperCAmelCase	      = 6
	UpperCAmelCase	      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	UpperCAmelCase      , UpperCAmelCase	      = knapsack(w, wt, val, n)
	print(optimal_solution)
	print(mf_knapsack(n, wt, val, w))  # switched the n and w

	# testing the dynamic programming problem with example
	# the optimal subset for the above example are items 3 and 4
	UpperCAmelCase      , UpperCAmelCase	      = knapsack_with_example_solution(w, wt, val)
	assert optimal_solution == 8
	assert optimal_subset == {3, 4}
	print('''optimal_value = ''', optimal_solution)
	print('''An optimal subset corresponding to the optimal value''', optimal_subset)

