
import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class __snake_case		(				lowerCAmelCase					):
    _a		:						str=    ["image_processor", "tokenizer"]
    _a		:						Dict=    "OwlViTImageProcessor"
    _a		:						Dict=    ("CLIPTokenizer", "CLIPTokenizerFast")
    def __init__(  self		,snake_case=None		,snake_case=None		,**snake_case				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""		,snake_case		,)
                  lowercase							:					int	     =				kwargs.pop("""feature_extractor"""				)

           lowercase							:					List[str]	     =				image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError("""You need to specify an `image_processor`."""				)
           if tokenizer is None:
                  raise ValueError("""You need to specify a `tokenizer`."""				)

           super().__init__(snake_case		,snake_case				)
    def __call__(  self		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case="max_length"		,snake_case="np"		,**snake_case				):



           '''simple docstring'''







           if text is None and query_images is None and images is None:
                  raise ValueError(
                      """You have to specify at least one text or query image or image. All three cannot be none."""				)

           if text is not None:
                  if isinstance(snake_case		,snake_case				) or (isinstance(snake_case		,snake_case				) and not isinstance(text[0]		,snake_case				)):
                         lowercase							:					List[Any]	     =				[self.tokenizer(snake_case		,padding=snake_case		,return_tensors=snake_case		,**snake_case				)]

                  elif isinstance(snake_case		,snake_case				) and isinstance(text[0]		,snake_case				):
                         lowercase							:					Optional[int]	     =				[]

                         # Maximum number of queries across batch
                         lowercase							:					Any	     =				max([len(snake_case				) for t in text]				)

                         # Pad all batch samples to max number of text queries
                         for t in text:
                                if len(snake_case				) != max_num_queries:
                                       lowercase							:					int	     =				t + [""" """] * (max_num_queries - len(snake_case				))

                                lowercase							:					int	     =				self.tokenizer(snake_case		,padding=snake_case		,return_tensors=snake_case		,**snake_case				)
                                encodings.append(snake_case				)
                  else:
                         raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""				)

                  if return_tensors == "np":
                         lowercase							:					Optional[Any]	     =				np.concatenate([encoding["""input_ids"""] for encoding in encodings]		,axis=0				)
                         lowercase							:					List[Any]	     =				np.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,axis=0				)

                  elif return_tensors == "jax" and is_flax_available():
                         import jax.numpy as jnp

                         lowercase							:					int	     =				jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]		,axis=0				)
                         lowercase							:					str	     =				jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]		,axis=0				)

                  elif return_tensors == "pt" and is_torch_available():
                         import torch

                         lowercase							:					Dict	     =				torch.cat([encoding["""input_ids"""] for encoding in encodings]		,dim=0				)
                         lowercase							:					List[Any]	     =				torch.cat([encoding["""attention_mask"""] for encoding in encodings]		,dim=0				)

                  elif return_tensors == "tf" and is_tf_available():
                         import tensorflow as tf

                         lowercase							:					Dict	     =				tf.stack([encoding["""input_ids"""] for encoding in encodings]		,axis=0				)
                         lowercase							:					int	     =				tf.stack([encoding["""attention_mask"""] for encoding in encodings]		,axis=0				)

                  else:
                         raise ValueError("""Target return tensor type could not be returned"""				)

                  lowercase							:					Tuple	     =				BatchEncoding()
                  lowercase							:					Dict	     =				input_ids
                  lowercase							:					Optional[int]	     =				attention_mask

           if query_images is not None:
                  lowercase							:					Optional[int]	     =				BatchEncoding()
                  lowercase							:					Union[str, Any]	     =				self.image_processor(
                      snake_case		,return_tensors=snake_case		,**snake_case				).pixel_values
                  lowercase							:					str	     =				query_pixel_values

           if images is not None:
                  lowercase							:					List[Any]	     =				self.image_processor(snake_case		,return_tensors=snake_case		,**snake_case				)

           if text is not None and images is not None:
                  lowercase							:					Tuple	     =				image_features.pixel_values
                  return encoding
           elif query_images is not None and images is not None:
                  lowercase							:					Tuple	     =				image_features.pixel_values
                  return encoding
           elif text is not None or query_images is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**snake_case				)		,tensor_type=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.image_processor.post_process(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.image_processor.post_process_object_detection(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.image_processor.post_process_image_guided_detection(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case		,**snake_case				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,snake_case		,)
           return self.image_processor_class
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,snake_case		,)
           return self.image_processor


def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:

       def count_of_possible_combinations(SCREAMING_SNAKE_CASE__    ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item    ) for item in array    )

       return count_of_possible_combinations(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:

       def count_of_possible_combinations_with_dp_array(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              lowercase							:					Any	     =				sum(
                  count_of_possible_combinations_with_dp_array(target - item ,     SCREAMING_SNAKE_CASE__    )
                  for item in array    )
              lowercase							:					Optional[int]	     =				answer
              return answer

       lowercase							:					int	     =				[-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       lowercase							:					str	     =				[0] * (target + 1)
       lowercase							:					Tuple	     =				1

       for i in range(1 ,     target + 1    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  lowercase  : Any						   =							3
  lowercase  : Optional[Any]						   =							5
  lowercase  : Tuple						   =							[1, 2, 5]
  print(combination_sum_iv(n, array, target))
