

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							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =['image_processor', 'tokenizer']
			lowerCamelCase__           ='OwlViTImageProcessor'
			lowerCamelCase__           =('CLIPTokenizer', 'CLIPTokenizerFast')
			def __init__(			self							: Optional[Any]      ,      a							: int=None      ,      a							: Optional[Any]=None      ,      **a							: str	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
				if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."      ,      a      ,      )
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       kwargs.pop("feature_extractor"	)

				SCREAMING_SNAKE_CASE   :      int				     =       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__(a      ,      a	)
			def __call__(			self							: Optional[Any]      ,      a							: Optional[Any]=None      ,      a							: List[Any]=None      ,      a							: Tuple=None      ,      a							: int="max_length"      ,      a							: str="np"      ,      **a							: Any	)							->       Dict:

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

					elif isinstance(a      ,      a	) and isinstance(text[0]      ,      a	):
						SCREAMING_SNAKE_CASE   :      int				     =       []

						# Maximum number of queries across batch
						SCREAMING_SNAKE_CASE   :      int				     =       max([len(a	) for t in text]	)

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

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

					if return_tensors == "np":
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       np.concatenate([encoding["input_ids"] for encoding in encodings]      ,      axis=0	)
						SCREAMING_SNAKE_CASE   :      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

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

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

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

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

						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       tf.stack([encoding["input_ids"] for encoding in encodings]      ,      axis=0	)
						SCREAMING_SNAKE_CASE   :      int				     =       tf.stack([encoding["attention_mask"] for encoding in encodings]      ,      axis=0	)

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

					SCREAMING_SNAKE_CASE   :      Any				     =       BatchEncoding()
					SCREAMING_SNAKE_CASE   :      str				     =       input_ids
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       attention_mask

				if query_images is not None:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       BatchEncoding()
					SCREAMING_SNAKE_CASE   :      Any				     =       self.image_processor(
					    a      ,      return_tensors=a      ,      **a	).pixel_values
					SCREAMING_SNAKE_CASE   :      int				     =       query_pixel_values

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

				if text is not None and images is not None:
					SCREAMING_SNAKE_CASE   :      Dict				     =       image_features.pixel_values
					return encoding
				elif query_images is not None and images is not None:
					SCREAMING_SNAKE_CASE   :      int				     =       image_features.pixel_values
					return encoding
				elif text is not None or query_images is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**a	)      ,      tensor_type=a	)
			def 							__UpperCamelCase (			self							: Any      ,      *a							: List[Any]      ,      **a							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

				return self.image_processor.post_process(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      *a							: List[str]      ,      **a							: int	)							->       List[Any]:

				"""simple docstring"""

				return self.image_processor.post_process_object_detection(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      *a							: Any      ,      **a							: Tuple	)							->       int:

				"""simple docstring"""

				return self.image_processor.post_process_image_guided_detection(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      *a							: Dict      ,      **a							: List[Any]	)							->       Any:

				"""simple docstring"""

				return self.tokenizer.batch_decode(*a      ,      **a	)
			def 							__UpperCamelCase (			self							: str      ,      *a							: Optional[int]      ,      **a							: Union[str, Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				return self.tokenizer.decode(*a      ,      **a	)
			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Any:

				"""simple docstring"""

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






			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[Any]:

				"""simple docstring"""

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


class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return index | (index + 1)
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return (index & (index + 1)) - 1
			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       value
				while index < self.size:
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_prev(a	) + 1
					if current_left_border == index:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       value
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       max(a      ,      a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_next(a	)






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int      ,      a							: int	)							->       int:

				"""simple docstring"""

				right -= 1  # Because of right is exclusive
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       0
				while left <= right:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.get_prev(a	)
					if left <= current_left:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       max(a      ,      self.tree[right]	)
						SCREAMING_SNAKE_CASE   :      str				     =       current_left
					else:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       max(a      ,      self.arr[right]	)
						right -= 1
				return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()