





def    __snake_case  (    _lowerCAmelCase       :       str , _lowerCAmelCase       :       str , _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       Optional[Any]		)       ->  str:
   global f  # a global dp table for knapsack
   if f[i][j] < 0:
      if j < wt[i - 1]:
         A_    : Optional[int]								=	mf_knapsack(i - 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__		)
      else:
         A_    : Dict								=	max(
             mf_knapsack(i - 1 , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__		) , mf_knapsack(i - 1 , lowerCAmelCase__ , lowerCAmelCase__ , j - wt[i - 1]		) + val[i - 1] , )
      A_    : List[Any]								=	val
   return f[i][j]

def    __snake_case  (    _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       Tuple		)       ->  str:
   A_    : Optional[Any]								=	[[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_:
            A_    : Tuple								=	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_]		)
         else:
            A_    : str								=	dp[i - 1][w_]

   return dp[n][w_], dp

def    __snake_case  (    _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       str , _lowerCAmelCase       :       Dict		)       ->  Optional[int]:
   if not (isinstance(lowerCAmelCase__ , (list, tuple)		) and isinstance(lowerCAmelCase__ , (list, tuple)		)):
      raise ValueError(
          "Both the weights and values vectors must be either lists or tuples"		)

   A_    : Any								=	len(lowerCAmelCase__		)
   if num_items != len(lowerCAmelCase__		):
      A_    : int								=	(
          "The number of weights must be the same as the number of values.\n"
          f"But got {num_items} weights and {len(lowerCAmelCase__		)} values"
      )
      raise ValueError(lowerCAmelCase__		)
   for i in range(lowerCAmelCase__		):
      if not isinstance(wt[i] , lowerCAmelCase__		):
         A_    : Union[str, Any]								=	(
             "All weights must be integers but got weight of "
             f"type {type(wt[i]		)} at index {i}"
         )
         raise TypeError(lowerCAmelCase__		)

   A_					,  A_    : Optional[Any]								=	knapsack(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__		)
   A_    : Optional[int]								=	set()
   _construct_solution(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__		)

   return optimal_val, example_optional_set

def    __snake_case  (    _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       List[Any] , _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       Any		)       ->  Any:
   # 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(lowerCAmelCase__ , lowerCAmelCase__ , i - 1 , lowerCAmelCase__ , lowerCAmelCase__		)
      else:
         optimal_set.add(lowerCAmelCase__		)
         _construct_solution(lowerCAmelCase__ , lowerCAmelCase__ , i - 1 , j - wt[i - 1] , lowerCAmelCase__		)


if __name__ == "__main__":
 _lowerCAmelCase							:					Dict		    =		[3, 2, 4, 4]
 _lowerCAmelCase							:					Union[str, Any]		    =		[4, 3, 2, 3]
 _lowerCAmelCase							:					int		    =		4
 _lowerCAmelCase							:					List[str]		    =		6
 _lowerCAmelCase							:					Tuple		    =		[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 _lowerCAmelCase							:					str		    =		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
 _lowerCAmelCase							:					int		    =		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)


import copy
import os
from typing import Union

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


lowercase__		:Dict   =    logging.get_logger(__name__)

lowercase__		:Optional[int]   =    {
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip_text_model'''




							def __init__(				self   ,A__=2_5_0_0_0_2   ,A__=1_0_2_4   ,A__=2_4   ,A__=1_6   ,A__=4_0_9_6   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_4   ,A__=1   ,A__=0.02   ,A__=0.02   ,A__=1E-05   ,A__=1   ,A__=0   ,A__=2   ,A__="absolute"   ,A__=True   ,A__=7_6_8   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,bos_token_id=A__   ,eos_token_id=A__   ,**A__)

												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						hidden_act
												lowercase							=						intermediate_size
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						layer_norm_eps
												lowercase							=						position_embedding_type
												lowercase							=						use_cache
												lowercase							=						project_dim







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[int]       					='''altclip_vision_model'''




							def __init__(				self   ,A__=7_6_8   ,A__=3_0_7_2   ,A__=5_1_2   ,A__=1_2   ,A__=1_2   ,A__=3   ,A__=2_2_4   ,A__=3_2   ,A__="quick_gelu"   ,A__=1E-5   ,A__=0.0   ,A__=0.02   ,A__=1.0   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						hidden_size
												lowercase							=						intermediate_size
												lowercase							=						projection_dim
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						image_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						attention_dropout
												lowercase							=						layer_norm_eps
												lowercase							=						hidden_act





							@classmethod
							def  A__      (				cls   ,A__   ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase	,						lowercase							=						cls.get_config_dict(A__   ,**A__)

												# get the vision config dict if we are loading from AltCLIPConfig
												if config_dict.get('''model_type''') == "altclip":
																	lowercase							=						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		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip'''
							lowercase_       :       List[Any]       					=True




							def __init__(				self   ,A__=None   ,A__=None   ,A__=7_6_8   ,A__=2.6592   ,**A__):
												# If `_config_dict` exist, we use them for the backward compatibility.
												# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
												# of confusion!).
												lowercase							=						kwargs.pop('''text_config_dict'''   ,A__)
												lowercase							=						kwargs.pop('''vision_config_dict'''   ,A__)

												super().__init__(**A__)

												# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
												# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
												# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
												if text_config_dict is not None:
																	if text_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `text_config_dict`.
																	lowercase							=						AltCLIPTextConfig(**A__).to_dict()

																	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																	for key, value in _text_config_dict.items():
																						if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																											# If specified in `text_config_dict`
																											if key in text_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
																																    f'The value `text_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
																																    f'value `text_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																	text_config.update(_text_config_dict)

												if vision_config_dict is not None:
																	if vision_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `vision_config_dict`.
																	lowercase							=						AltCLIPVisionConfig(**A__).to_dict()
																	# convert keys to string instead of integer
																	if "id2label" in _vision_config_dict:
																						lowercase							=						{
																						    str(A__): value for key, value in _vision_config_dict['''id2label'''].items()
																						}

																	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																	for key, value in _vision_config_dict.items():
																						if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																											# If specified in `vision_config_dict`
																											if key in vision_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
																																    f'values. The value `vision_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
																																    f'The value `vision_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																	vision_config.update(_vision_config_dict)

												if text_config is None:
																	lowercase							=						{}
																	logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')

												if vision_config is None:
																	lowercase							=						{}
																	logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')

												lowercase							=						AltCLIPTextConfig(**A__)
												lowercase							=						AltCLIPVisionConfig(**A__)

												lowercase							=						projection_dim
												lowercase							=						logit_scale_init_value
												lowercase							=						1.0




							@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):
												lowercase							=						copy.deepcopy(self.__dict__)
												lowercase							=						self.text_config.to_dict()
												lowercase							=						self.vision_config.to_dict()
												lowercase							=						self.__class__.model_type
												return output




