






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        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       ['image_processor', 'tokenizer']
	__snake_case	:Dict   =       'OwlViTImageProcessor'
	__snake_case	:str   =       ('CLIPTokenizer', 'CLIPTokenizerFast')







	def __init__(  self      :							str       ,				_lowerCAmelCase      :							Any=None       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				**_lowerCAmelCase      :							List[Any]			)		-> Tuple:
		"""simple docstring"""







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

		__lowercase             =  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__(_lowerCAmelCase       ,				_lowerCAmelCase			)







	def __call__(  self      :							str       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				_lowerCAmelCase      :							Optional[Any]=None       ,				_lowerCAmelCase      :							Tuple=None       ,				_lowerCAmelCase      :							Dict="max_length"       ,				_lowerCAmelCase      :							Optional[Any]="np"       ,				**_lowerCAmelCase      :							Optional[int]			)		-> Optional[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(_lowerCAmelCase       ,				_lowerCAmelCase			) or (isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			) and not isinstance(text[0]       ,				_lowerCAmelCase			)):
				__lowercase             =  [self.tokenizer(_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				return_tensors=_lowerCAmelCase       ,				**_lowerCAmelCase			)]

			elif isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			) and isinstance(text[0]       ,				_lowerCAmelCase			):
				__lowercase             =  []

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

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

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

			if return_tensors == "np":
				__lowercase             =  np.concatenate([encoding["""input_ids"""] for encoding in encodings]       ,				axis=0			)
				__lowercase             =  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             =  jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]       ,				axis=0			)
				__lowercase             =  jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]       ,				axis=0			)

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

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

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

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

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

			__lowercase             =  BatchEncoding()
			__lowercase             =  input_ids
			__lowercase             =  attention_mask

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

		if images is not None:
			__lowercase             =  self.image_processor(_lowerCAmelCase       ,				return_tensors=_lowerCAmelCase       ,				**_lowerCAmelCase			)

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







	def  _a   (  self      :							Optional[int]       ,				*_lowerCAmelCase      :							Optional[Any]       ,				**_lowerCAmelCase      :							Dict			)		-> int:
		"""simple docstring"""







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







	def  _a   (  self      :							int       ,				*_lowerCAmelCase      :							Dict       ,				**_lowerCAmelCase      :							List[str]			)		-> Optional[int]:
		"""simple docstring"""







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







	def  _a   (  self      :							str       ,				*_lowerCAmelCase      :							Union[str, Any]       ,				**_lowerCAmelCase      :							str			)		-> List[Any]:
		"""simple docstring"""







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







	def  _a   (  self      :							Tuple       ,				*_lowerCAmelCase      :							int       ,				**_lowerCAmelCase      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







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







	def  _a   (  self      :							Optional[int]       ,				*_lowerCAmelCase      :							Optional[int]       ,				**_lowerCAmelCase      :							Optional[Any]			)		-> str:
		"""simple docstring"""







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







	@property
	def  _a   (  self      :							int			)		-> Dict:
		"""simple docstring"""







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







	@property
	def  _a   (  self      :							Optional[Any]			)		-> List[str]:
		"""simple docstring"""







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









from __future__ import annotations





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if len(lowerCamelCase					) < 2:
		raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""					)
	if any(i <= 0 for i in nums					):
		raise ValueError("""All values must be greater than 0"""					)
	__lowercase             =  nums.copy()
	copy_nums.sort()
	return copy_nums[-1] < sum(copy_nums[:-1]					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

