



"""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__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['image_processor', 'tokenizer']
	SCREAMING_SNAKE_CASE        =      'OwlViTImageProcessor'
	SCREAMING_SNAKE_CASE        =      ('CLIPTokenizer', 'CLIPTokenizerFast')

	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     **_SCREAMING_SNAKE_CASE:						Tuple)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    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."			,     _SCREAMING_SNAKE_CASE			,     )
							__lowerCAmelCase :    Optional[int]				=			kwargs.pop("feature_extractor")

				__lowerCAmelCase :    Any				=			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:						str			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]="max_length"			,     _SCREAMING_SNAKE_CASE:						Any="np"			,     **_SCREAMING_SNAKE_CASE:						List[Any])							->			Union[str, 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)):
										__lowerCAmelCase :    Optional[Any]				=			[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):
										__lowerCAmelCase :    Union[str, Any]				=			[]

										# Maximum number of queries across batch
										__lowerCAmelCase :    Optional[Any]				=			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:
																__lowerCAmelCase :    Optional[Any]				=			t + [" "] * (max_num_queries - len(_SCREAMING_SNAKE_CASE))

													__lowerCAmelCase :    Optional[int]				=			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":
										__lowerCAmelCase :    Dict				=			np.concatenate([encoding["input_ids"] for encoding in encodings]			,     axis=0)
										__lowerCAmelCase :    Dict				=			np.concatenate([encoding["attention_mask"] for encoding in encodings]			,     axis=0)

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

										__lowerCAmelCase :    Optional[Any]				=			jnp.concatenate([encoding["input_ids"] for encoding in encodings]			,     axis=0)
										__lowerCAmelCase :    List[str]				=			jnp.concatenate([encoding["attention_mask"] for encoding in encodings]			,     axis=0)

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

										__lowerCAmelCase :    Optional[int]				=			torch.cat([encoding["input_ids"] for encoding in encodings]			,     dim=0)
										__lowerCAmelCase :    Optional[int]				=			torch.cat([encoding["attention_mask"] for encoding in encodings]			,     dim=0)

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

										__lowerCAmelCase :    List[str]				=			tf.stack([encoding["input_ids"] for encoding in encodings]			,     axis=0)
										__lowerCAmelCase :    Optional[Any]				=			tf.stack([encoding["attention_mask"] for encoding in encodings]			,     axis=0)

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

							__lowerCAmelCase :    int				=			BatchEncoding()
							__lowerCAmelCase :    Tuple				=			input_ids
							__lowerCAmelCase :    Dict				=			attention_mask

				if query_images is not None:
							__lowerCAmelCase :    List[str]				=			BatchEncoding()
							__lowerCAmelCase :    Optional[Any]				=			self.image_processor(
							    _SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE).pixel_values
							__lowerCAmelCase :    str				=			query_pixel_values

				if images is not None:
							__lowerCAmelCase :    str				=			self.image_processor(_SCREAMING_SNAKE_CASE			,     return_tensors=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				if text is not None and images is not None:
							__lowerCAmelCase :    int				=			image_features.pixel_values
							return encoding
				elif query_images is not None and images is not None:
							__lowerCAmelCase :    Dict				=			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 				_SCREAMING_SNAKE_CASE  (	self:						int			,     *_SCREAMING_SNAKE_CASE:						List[str]			,     **_SCREAMING_SNAKE_CASE:						List[Any])							->			Optional[Any]:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     *_SCREAMING_SNAKE_CASE:						Dict			,     **_SCREAMING_SNAKE_CASE:						int)							->			Optional[Any]:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     *_SCREAMING_SNAKE_CASE:						Tuple			,     **_SCREAMING_SNAKE_CASE:						Optional[int])							->			Any:

				"""simple docstring"""

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

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

				"""simple docstring"""

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

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

				"""simple docstring"""

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

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[str]:

				"""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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			int:

				"""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"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			__lowerCAmelCase :    Optional[Any]				=			(boundary[1] - boundary[0]) / steps
			__lowerCAmelCase :    List[Any]				=			boundary[0]
			__lowerCAmelCase :    Any				=			boundary[1]
			__lowerCAmelCase :    Dict				=			make_points(__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			0.0
			y += (h / 2.0) * f(__snake_case      )
			for i in x_i:
						# print(i)
						y += h * f(__snake_case      )
			y += (h / 2.0) * f(__snake_case      )
			return y


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    int				=			a + h
			while x < (b - h):
						yield x
						__lowerCAmelCase :    List[str]				=			x + h


def      _lowercase							(    __snake_case      )		->  Tuple:  # enter your function here
			__lowerCAmelCase :    int				=			(x - 0) * (x - 0)
			return y


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Tuple				=			0.0  # Lower bound of integration
			__lowerCAmelCase :    Union[str, Any]				=			1.0  # Upper bound of integration
			__lowerCAmelCase :    Dict				=			10.0  # define number of steps or resolution
			__lowerCAmelCase :    Optional[Any]				=			[a, b]  # define boundary of integration
			__lowerCAmelCase :    Optional[int]				=			method_a(__snake_case   ,__snake_case      )
			print(F"""y = {y}"""      )


if __name__ == "__main__":
	main()