






'''simple docstring'''

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 							a_		(     __lowercase							):
  lowercase							= ["""image_processor""", """tokenizer"""]
  lowercase							= """OwlViTImageProcessor"""
  lowercase							= ("""CLIPTokenizer""", """CLIPTokenizerFast""")

  def __init__( self     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE )    -> Dict:



        """simple docstring"""






        UpperCamelCase							    =   None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""     ,						SCREAMING_SNAKE_CASE_     ,						)
              UpperCamelCase							    =   kwargs.pop("""feature_extractor""" )

        UpperCamelCase							    =   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__(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_ )

  def __call__( self     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE="max_length"     ,						_SCREAMING_SNAKE_CASE="np"     ,						**_SCREAMING_SNAKE_CASE )    -> List[Any]:



        """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(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_ ) or (isinstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_ ) and not isinstance(text[0]     ,						SCREAMING_SNAKE_CASE_ )):
                    UpperCamelCase							    =   [self.tokenizer(SCREAMING_SNAKE_CASE_     ,						padding=SCREAMING_SNAKE_CASE_     ,						return_tensors=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )]

              elif isinstance(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_ ) and isinstance(text[0]     ,						SCREAMING_SNAKE_CASE_ ):
                    UpperCamelCase							    =   []

                    # Maximum number of queries across batch
                    UpperCamelCase							    =   max([len(SCREAMING_SNAKE_CASE_ ) for t in text] )

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

                          UpperCamelCase							    =   self.tokenizer(SCREAMING_SNAKE_CASE_     ,						padding=SCREAMING_SNAKE_CASE_     ,						return_tensors=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )
                          encodings.append(SCREAMING_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":
                    UpperCamelCase							    =   np.concatenate([encoding["""input_ids"""] for encoding in encodings]     ,						axis=0 )
                    UpperCamelCase							    =   np.concatenate([encoding["""attention_mask"""] for encoding in encodings]     ,						axis=0 )

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

                    UpperCamelCase							    =   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]     ,						axis=0 )
                    UpperCamelCase							    =   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]     ,						axis=0 )

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

                    UpperCamelCase							    =   torch.cat([encoding["""input_ids"""] for encoding in encodings]     ,						dim=0 )
                    UpperCamelCase							    =   torch.cat([encoding["""attention_mask"""] for encoding in encodings]     ,						dim=0 )

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

                    UpperCamelCase							    =   tf.stack([encoding["""input_ids"""] for encoding in encodings]     ,						axis=0 )
                    UpperCamelCase							    =   tf.stack([encoding["""attention_mask"""] for encoding in encodings]     ,						axis=0 )

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

              UpperCamelCase							    =   BatchEncoding()
              UpperCamelCase							    =   input_ids
              UpperCamelCase							    =   attention_mask

        if query_images is not None:
              UpperCamelCase							    =   BatchEncoding()
              UpperCamelCase							    =   self.image_processor(
                  SCREAMING_SNAKE_CASE_     ,						return_tensors=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ ).pixel_values
              UpperCamelCase							    =   query_pixel_values

        if images is not None:
              UpperCamelCase							    =   self.image_processor(SCREAMING_SNAKE_CASE_     ,						return_tensors=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )

        if text is not None and images is not None:
              UpperCamelCase							    =   image_features.pixel_values
              return encoding
        elif query_images is not None and images is not None:
              UpperCamelCase							    =   image_features.pixel_values
              return encoding
        elif text is not None or query_images is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_ )     ,						tensor_type=SCREAMING_SNAKE_CASE_ )

  def     A__		( self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE )    -> Union[str, Any]:



        """simple docstring"""






        return self.image_processor.post_process(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )

  def     A__		( self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE )    -> Dict:



        """simple docstring"""






        return self.image_processor.post_process_object_detection(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )

  def     A__		( self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE )    -> Optional[Any]:



        """simple docstring"""






        return self.image_processor.post_process_image_guided_detection(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )

  def     A__		( self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE )    -> Union[str, Any]:



        """simple docstring"""






        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )

  def     A__		( self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE )    -> Union[str, Any]:



        """simple docstring"""






        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_ )

  @property
  def     A__		( self )    -> Dict:



        """simple docstring"""






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,						SCREAMING_SNAKE_CASE_     ,						)
        return self.image_processor_class


  @property
  def     A__		( self )    -> Union[str, Any]:



        """simple docstring"""






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


"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a = 100,				):
		SCREAMING_SNAKE_CASE_    			=			x_start
		SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
		SCREAMING_SNAKE_CASE_    			=			0.0
		for _ in range(__a     ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				SCREAMING_SNAKE_CASE_    			=			(x_end - x_start) / steps + xa
				SCREAMING_SNAKE_CASE_    			=			fnc(__a     )
				area += abs(fxa + fxa     ) * (xa - xa) / 2
				# Increment step
				SCREAMING_SNAKE_CASE_    			=			xa
				SCREAMING_SNAKE_CASE_    			=			fxa
		return area


if __name__ == "__main__":



						def 							_lowerCamelCase			(			__a     ):
								return x**3 + x**2

						print('f(x) = x^3 + x^2')
						print('The area between the curve, x = -5, x = 5 and the x axis is:')
						lowerCAmelCase__				=					10
						while i <= 100000:
												print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
												i *= 10