




"""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 __lowercase(   lowerCAmelCase__					):




					'''simple docstring'''


					__a    :    Any  		=			['''image_processor''', '''tokenizer''']
					__a    :    Optional[Any]  		=			'''OwlViTImageProcessor'''
					__a    :    List[str]  		=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')
					def __init__(     self    ,						__a=None    ,						__a=None    ,						**__a  ):
								__lowerCamelCase       :						Optional[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.'    ,						__lowerCAmelCase    ,						)
											__lowerCamelCase       :						Optional[Any]													=					kwargs.pop('feature_extractor'  )

								__lowerCamelCase       :						Tuple													=					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    ,						__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(__lowerCAmelCase    ,						__lowerCAmelCase  ) or (isinstance(__lowerCAmelCase    ,						__lowerCAmelCase  ) and not isinstance(text[0]    ,						__lowerCAmelCase  )):
														__lowerCamelCase       :						str													=					[self.tokenizer(__lowerCAmelCase    ,						padding=__lowerCAmelCase    ,						return_tensors=__lowerCAmelCase    ,						**__lowerCAmelCase  )]

											elif isinstance(__lowerCAmelCase    ,						__lowerCAmelCase  ) and isinstance(text[0]    ,						__lowerCAmelCase  ):
														__lowerCamelCase       :						int													=					[]

														# Maximum number of queries across batch
														__lowerCamelCase       :						Optional[Any]													=					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:
																				__lowerCamelCase       :						Union[str, Any]													=					t + [' '] * (max_num_queries - len(__lowerCAmelCase  ))

																	__lowerCamelCase       :						Dict													=					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":
														__lowerCamelCase       :						Any													=					np.concatenate([encoding['input_ids'] for encoding in encodings]    ,						axis=0  )
														__lowerCamelCase       :						Optional[int]													=					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       :						int													=					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       :						Union[str, Any]													=					torch.cat([encoding['input_ids'] for encoding in encodings]    ,						dim=0  )
														__lowerCamelCase       :						Optional[Any]													=					torch.cat([encoding['attention_mask'] for encoding in encodings]    ,						dim=0  )

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

														__lowerCamelCase       :						Union[str, Any]													=					tf.stack([encoding['input_ids'] for encoding in encodings]    ,						axis=0  )
														__lowerCamelCase       :						Tuple													=					tf.stack([encoding['attention_mask'] for encoding in encodings]    ,						axis=0  )

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

											__lowerCamelCase       :						Union[str, Any]													=					BatchEncoding()
											__lowerCamelCase       :						str													=					input_ids
											__lowerCamelCase       :						int													=					attention_mask

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

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

								if text is not None and images is not None:
											__lowerCamelCase       :						Optional[Any]													=					image_features.pixel_values
											return encoding
								elif query_images is not None and images is not None:
											__lowerCamelCase       :						Tuple													=					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      snake_case_					(     self    ,						*__a    ,						**__a  ):
								return self.image_processor.post_process(*__lowerCAmelCase    ,						**__lowerCAmelCase  )
					def      snake_case_					(     self    ,						*__a    ,						**__a  ):
								return self.image_processor.post_process_object_detection(*__lowerCAmelCase    ,						**__lowerCAmelCase  )
					def      snake_case_					(     self    ,						*__a    ,						**__a  ):
								return self.image_processor.post_process_image_guided_detection(*__lowerCAmelCase    ,						**__lowerCAmelCase  )
					def      snake_case_					(     self    ,						*__a    ,						**__a  ):
								return self.tokenizer.batch_decode(*__lowerCAmelCase    ,						**__lowerCAmelCase  )
					def      snake_case_					(     self    ,						*__a    ,						**__a  ):
								return self.tokenizer.decode(*__lowerCAmelCase    ,						**__lowerCAmelCase  )
					@property
					def      snake_case_					(     self  ):
								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      snake_case_					(     self  ):
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,						__lowerCAmelCase    ,						)
								return self.image_processor




'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    b.T
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=1					)
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=0					)
		lowerCAmelCase     =    np.matmul(_A ,  _A					)
		lowerCAmelCase     =    aa[:, None] - 2 * ab + ba[None, :]
		return d






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}")
					return resize(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray.""")

					if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True.""")

					if do_color_quantize and clusters is None:
							raise ValueError("""Clusters must be specified if do_color_quantize is True.""")

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

					if do_color_quantize:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       ChannelDimension.LAST) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							lowerCAmelCase     =    np.array(__lowerCAmelCase)
							lowerCAmelCase     =    color_quantize(__lowerCAmelCase			,       __lowerCAmelCase).reshape(images.shape[:-1])

							# flatten to (batch_size, height*width)
							lowerCAmelCase     =    images.shape[0]
							lowerCAmelCase     =    images.reshape(__lowerCAmelCase			,       -1)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							lowerCAmelCase     =    list(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




