
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  : str						   =							logging.get_logger(__name__)

lowercase  : str						   =							{
    """google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
    """google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
    """google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Tuple=    "owlvit_text_model"
    def __init__(  self		,snake_case=49408		,snake_case=512		,snake_case=2048		,snake_case=12		,snake_case=8		,snake_case=16		,snake_case="quick_gelu"		,snake_case=1e-5		,snake_case=0.0		,snake_case=0.02		,snake_case=1.0		,snake_case=0		,snake_case=49406		,snake_case=49407		,**snake_case		,):



           '''simple docstring'''







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

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



           '''simple docstring'''







           cls._set_token_in_kwargs(snake_case				)

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

           # get the text config dict if we are loading from OwlViTConfig
           if config_dict.get("""model_type"""				) == "owlvit":
                  lowercase							:					Any	     =				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(snake_case		,**snake_case				)






class __snake_case		(				lowerCAmelCase					):
    _a		:						List[Any]=    "owlvit_vision_model"
    def __init__(  self		,snake_case=768		,snake_case=3072		,snake_case=12		,snake_case=12		,snake_case=3		,snake_case=768		,snake_case=32		,snake_case="quick_gelu"		,snake_case=1e-5		,snake_case=0.0		,snake_case=0.02		,snake_case=1.0		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

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



           '''simple docstring'''







           cls._set_token_in_kwargs(snake_case				)

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

           # get the vision config dict if we are loading from OwlViTConfig
           if config_dict.get("""model_type"""				) == "owlvit":
                  lowercase							:					List[str]	     =				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		:						Any=    "owlvit"
    _a		:						Tuple=    True
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=512		,snake_case=2.6_592		,snake_case=True		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           if text_config is None:
                  lowercase							:					int	     =				{}
                  logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values."""				)

           if vision_config is None:
                  lowercase							:					List[str]	     =				{}
                  logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values."""				)

           lowercase							:					List[str]	     =				OwlViTTextConfig(**snake_case				)
           lowercase							:					Optional[int]	     =				OwlViTVisionConfig(**snake_case				)

           lowercase							:					List[Any]	     =				projection_dim
           lowercase							:					Dict	     =				logit_scale_init_value
           lowercase							:					Optional[Any]	     =				return_dict
           lowercase							:					str	     =				1.0
    @classmethod
    def 				_SCREAMING_SNAKE_CASE	(  cls		,snake_case		,**snake_case				):



           '''simple docstring'''







           cls._set_token_in_kwargs(snake_case				)

           lowercase     ,      lowercase							:					Any	     =				cls.get_config_dict(snake_case		,**snake_case				)

           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				)
    @classmethod
    def 				_SCREAMING_SNAKE_CASE	(  cls		,snake_case		,snake_case		,**snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				{}
           lowercase							:					Dict	     =				text_config
           lowercase							:					str	     =				vision_config

           return cls.from_dict(snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				copy.deepcopy(self.__dict__				)
           lowercase							:					Any	     =				self.text_config.to_dict()
           lowercase							:					List[str]	     =				self.vision_config.to_dict()
           lowercase							:					int	     =				self.__class__.model_type
           return output






class __snake_case		(				lowerCAmelCase					):
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                   ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
               ]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict(
               [
                   ("""logits_per_image""", {0: """batch"""}),
                   ("""logits_per_text""", {0: """batch"""}),
                   ("""text_embeds""", {0: """batch"""}),
                   ("""image_embeds""", {0: """batch"""}),
               ]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				super().generate_dummy_inputs(
               processor.tokenizer		,batch_size=snake_case		,seq_length=snake_case		,framework=snake_case				)
           lowercase							:					str	     =				super().generate_dummy_inputs(
               processor.image_processor		,batch_size=snake_case		,framework=snake_case				)
           return {**text_input_dict, **image_input_dict}
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 14


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     lowercase							:					str	     =				mf_knapsack(i - 1 ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
              else:
                     lowercase							:					Optional[int]	     =				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							:					List[Any]	     =				val
       return f[i][j]







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Optional[int]	     =				[[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							:					Union[str, Any]	     =				max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,     dp[i - 1][w_]    )
                     else:
                            lowercase							:					Any	     =				dp[i - 1][w_]

       return dp[n][w_], dp







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       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							:					Optional[Any]	     =				len(SCREAMING_SNAKE_CASE__    )
       if num_items != len(SCREAMING_SNAKE_CASE__    ):
              lowercase							:					Optional[Any]	     =				(
                  """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							:					Tuple	     =				(
                         """All weights must be integers but got weight of """
                         f"type {type(wt[i]    )} at index {i}"
                     )
                     raise TypeError(SCREAMING_SNAKE_CASE__    )

       lowercase     ,      lowercase							:					Any	     =				knapsack(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					set	     =				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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       # 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__":
  lowercase  : Dict						   =							[3, 2, 4, 4]
  lowercase  : List[Any]						   =							[4, 3, 2, 3]
  lowercase  : Tuple						   =							4
  lowercase  : Tuple						   =							6
  lowercase  : Tuple						   =							[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
  lowercase   ,  lowercase  : List[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
  lowercase   ,  lowercase  : Optional[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)
