





'''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 		lowerCamelCase  (      __snake_case       ):
	snake_case_      =   ["image_processor", "tokenizer"]
	snake_case_      =   "OwlViTImageProcessor"
	snake_case_      =   ("CLIPTokenizer", "CLIPTokenizerFast")



	def __init__(   self		,      a_=None		,      a_=None		,      **a_	):
			lowerCAmelCase     :    Dict						=   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,      _lowercase		,      )
					lowerCAmelCase     :    str						=   kwargs.pop("feature_extractor"	)

			lowerCAmelCase     :    Union[str, 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__(_lowercase		,      _lowercase	)



	def __call__(   self		,      a_=None		,      a_=None		,      a_=None		,      a_="max_length"		,      a_="np"		,      **a_	):

			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(_lowercase		,      _lowercase	) or (isinstance(_lowercase		,      _lowercase	) and not isinstance(text[0]		,      _lowercase	)):
							lowerCAmelCase     :    Any						=   [self.tokenizer(_lowercase		,      padding=_lowercase		,      return_tensors=_lowercase		,      **_lowercase	)]

					elif isinstance(_lowercase		,      _lowercase	) and isinstance(text[0]		,      _lowercase	):
							lowerCAmelCase     :    Optional[Any]						=   []

							# Maximum number of queries across batch
							lowerCAmelCase     :    str						=   max([len(_lowercase	) for t in text]	)

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

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

					if return_tensors == "np":
							lowerCAmelCase     :    Union[str, Any]						=   np.concatenate([encoding["input_ids"] for encoding in encodings]		,      axis=0	)
							lowerCAmelCase     :    Union[str, 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

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

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

							lowerCAmelCase     :    List[str]						=   torch.cat([encoding["input_ids"] for encoding in encodings]		,      dim=0	)
							lowerCAmelCase     :    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     :    Dict						=   tf.stack([encoding["attention_mask"] for encoding in encodings]		,      axis=0	)

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

					lowerCAmelCase     :    List[str]						=   BatchEncoding()
					lowerCAmelCase     :    Union[str, Any]						=   input_ids
					lowerCAmelCase     :    str						=   attention_mask

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

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

			if text is not None and images is not None:
					lowerCAmelCase     :    List[Any]						=   image_features.pixel_values
					return encoding
			elif query_images is not None and images is not None:
					lowerCAmelCase     :    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(**_lowercase	)		,      tensor_type=_lowercase	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.image_processor.post_process(*_lowercase		,      **_lowercase	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.image_processor.post_process_object_detection(*_lowercase		,      **_lowercase	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.image_processor.post_process_image_guided_detection(*_lowercase		,      **_lowercase	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.batch_decode(*_lowercase		,      **_lowercase	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.decode(*_lowercase		,      **_lowercase	)



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





	@property
	def   _lowerCamelCase    (   self	):
			warnings.warn(
			    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,      _lowercase		,      )
			return self.image_processor










'''simple docstring'''






def      __A						(							a_						:  int	):
		if not isinstance(a_      ,a_	):
				lowerCAmelCase     :    Dict						=   f'''Input value of [number={number}] must be an integer'''
				raise TypeError(a_	)
		if number < 0:
				return False
		lowerCAmelCase     :    Dict						=   number * number
		while number > 0:
				if number % 1_0 != number_square % 1_0:
						return False
				number //= 1_0
				number_square //= 1_0
		return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()



