





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


_lowerCamelCase 					=      logging.get_logger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   b.T
		SCREAMING_SNAKE_CASE__								=   np.sum(np.square(UpperCamelCase__     )       , axis=1     )
		SCREAMING_SNAKE_CASE__								=   np.sum(np.square(UpperCamelCase__     )       , axis=0     )
		SCREAMING_SNAKE_CASE__								=   np.matmul(UpperCamelCase__       , UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   aa[:, None] - 2 * ab + ba[None, :]
		return d






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   x.reshape(-1       , 3     )
		SCREAMING_SNAKE_CASE__								=   squared_euclidean_distance(UpperCamelCase__       , UpperCamelCase__     )
		return np.argmin(UpperCamelCase__       , axis=1     )




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["pixel_values"]


							def __init__(				self     :int				,					__A     :Optional[Union[List[List[int]], np.ndarray]] = None				,					__A     :bool = True				,					__A     :Dict[str, int] = None				,					__A     :PILImageResampling = PILImageResampling.BILINEAR				,					__A     :bool = True				,					__A     :bool = True				,					**__A     :int				,					) -> None:



									"""simple docstring"""



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


							def   _snake_case	(				self     :str				,					__A     :np.ndarray				,					__A     :Dict[str, int]				,					__A     :PILImageResampling = PILImageResampling.BILINEAR				,					__A     :Optional[Union[str, ChannelDimension]] = None				,					**__A     :List[str]				,					) -> np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   get_size_dict(__A		)
									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(
									    __A				,					size=(size["""height"""], size["""width"""])				,					resample=__A				,					data_format=__A				,					**__A		)


							def   _snake_case	(				self     :List[Any]				,					__A     :np.ndarray				,					__A     :Optional[Union[str, ChannelDimension]] = None				,					) -> np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   rescale(image=__A				,					scale=1 / 1_2_7.5				,					data_format=__A		)
									SCREAMING_SNAKE_CASE__								=   image - 1
									return image


							def   _snake_case	(				self     :Optional[int]				,					__A     :ImageInput				,					__A     :bool = None				,					__A     :Dict[str, int] = None				,					__A     :PILImageResampling = None				,					__A     :bool = None				,					__A     :Optional[bool] = None				,					__A     :Optional[Union[List[List[int]], np.ndarray]] = None				,					__A     :Optional[Union[str, TensorType]] = None				,					__A     :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST				,					**__A     :List[str]				,					) -> PIL.Image.Image:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								=   make_list_of_images(__A		)

									if not valid_images(__A		):
											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.
									SCREAMING_SNAKE_CASE__								=   [to_numpy_array(__A		) for image in images]

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

									if do_normalize:
											SCREAMING_SNAKE_CASE__								=   [self.normalize(image=__A		) for image in images]

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

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

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

									SCREAMING_SNAKE_CASE__								=   {"""input_ids""": images}
									return BatchFeature(data=__A				,					tensor_type=__A		)






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_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "OwlViTImageProcessor"
							lowerCamelCase_							     =       ("CLIPTokenizer", "CLIPTokenizerFast")


							def __init__(				self     :Optional[Any]				,					__A     :int=None				,					__A     :Optional[int]=None				,					**__A     :str		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   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__								=   kwargs.pop("""feature_extractor"""		)

									SCREAMING_SNAKE_CASE__								=   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     :str				,					__A     :Dict=None				,					__A     :List[str]=None				,					__A     :str=None				,					__A     :Optional[int]="max_length"				,					__A     :Tuple="np"				,					**__A     :int		) -> Tuple:



									"""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__								=   [self.tokenizer(__A				,					padding=__A				,					return_tensors=__A				,					**__A		)]

											elif isinstance(__A				,					__A		) and isinstance(text[0]				,					__A		):
													SCREAMING_SNAKE_CASE__								=   []

													# Maximum number of queries across batch
													SCREAMING_SNAKE_CASE__								=   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__								=   t + [""" """] * (max_num_queries - len(__A		))

															SCREAMING_SNAKE_CASE__								=   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__								=   np.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   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__								=   jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]				,					axis=0		)

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

													SCREAMING_SNAKE_CASE__								=   torch.cat([encoding["""input_ids"""] for encoding in encodings]				,					dim=0		)
													SCREAMING_SNAKE_CASE__								=   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__								=   tf.stack([encoding["""input_ids"""] for encoding in encodings]				,					axis=0		)
													SCREAMING_SNAKE_CASE__								=   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__								=   BatchEncoding()
											SCREAMING_SNAKE_CASE__								=   input_ids
											SCREAMING_SNAKE_CASE__								=   attention_mask

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

									if images is not None:
											SCREAMING_SNAKE_CASE__								=   self.image_processor(__A				,					return_tensors=__A				,					**__A		)

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



									"""simple docstring"""



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


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



									"""simple docstring"""



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


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



									"""simple docstring"""



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


							def   _snake_case	(				self     :Dict				,					*__A     :List[str]				,					**__A     :List[str]		) -> int:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Dict				,					*__A     :Dict				,					**__A     :List[str]		) -> str:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :List[Any]		) -> Optional[int]:



									"""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   _snake_case	(				self     :Any		) -> 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