




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class a :
						"""simple docstring"""

						def __init__( self :     List[str]				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Tuple=sys.maxsize				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    '''bilinear'''
													__lowercase      =    max_size
													__lowercase      =    short_edge_length






						def __call__( self :     Optional[int]				,							lowerCamelCase__ :     List[str]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    []
													for img in imgs:
																				__lowercase     ,     __lowercase      =    img.shape[:2]
																				# later: provide list and randomly choose index for resize
																				__lowercase      =    np.random.randint(self.short_edge_length[0]				,							self.short_edge_length[1] + 1				)
																				if size == 0:
																											return img
																				__lowercase      =    size * 1.0 / min(lowerCamelCase__				,							lowerCamelCase__				)
																				if h < w:
																											__lowercase     ,     __lowercase      =    size, scale * w
																				else:
																											__lowercase     ,     __lowercase      =    scale * h, size
																				if max(lowerCamelCase__				,							lowerCamelCase__				) > self.max_size:
																											__lowercase      =    self.max_size * 1.0 / max(lowerCamelCase__				,							lowerCamelCase__				)
																											__lowercase      =    newh * scale
																											__lowercase      =    neww * scale
																				__lowercase      =    int(neww + 0.5				)
																				__lowercase      =    int(newh + 0.5				)

																				if img.dtype == np.uinta:
																											__lowercase      =    Image.fromarray(lowerCamelCase__				)
																											__lowercase      =    pil_image.resize((neww, newh)				,							PILImageResampling.BILINEAR				)
																											__lowercase      =    np.asarray(lowerCamelCase__				)
																				else:
																											__lowercase      =    img.permute(2				,							0				,							1				).unsqueeze(0				)  # 3, 0, 1)  # hw(c) -> nchw
																											__lowercase      =    nn.functional.interpolate(
																											    lowerCamelCase__				,							(newh, neww)				,							mode=self.interp_method				,							align_corners=lowerCamelCase__				).squeeze(0				)
																				img_augs.append(lowerCamelCase__				)

													return img_augs







class a :
						"""simple docstring"""

						def __init__( self :     List[str]				,							lowerCamelCase__ :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,							cfg.INPUT.MAX_SIZE_TEST				)
													__lowercase      =    cfg.INPUT.FORMAT
													__lowercase      =    cfg.SIZE_DIVISIBILITY
													__lowercase      =    cfg.PAD_VALUE
													__lowercase      =    cfg.INPUT.MAX_SIZE_TEST
													__lowercase      =    cfg.MODEL.DEVICE
													__lowercase      =    torch.tensor(cfg.MODEL.PIXEL_STD				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)				,							1				,							1				)
													__lowercase      =    torch.tensor(cfg.MODEL.PIXEL_MEAN				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)				,							1				,							1				)
													__lowercase      =    lambda lowerCamelCase__				: (x - self.pixel_mean) / self.pixel_std






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     int				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    tuple(max(lowerCamelCase__				) for s in zip(*[img.shape for img in images]				)				)
													__lowercase      =    [im.shape[-2:] for im in images]
													__lowercase      =    [
													    nn.functional.pad(
													        lowerCamelCase__				,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,							value=self.pad_value				,							)
													    for size, im in zip(lowerCamelCase__				,							lowerCamelCase__				)
													]

													return torch.stack(lowerCamelCase__				), torch.tensor(lowerCamelCase__				)






						def __call__( self :     str				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Optional[int]=False				)    ->						List[str]:
													"""simple docstring"""


													with torch.no_grad():
																				if not isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																											__lowercase      =    [images]
																				if single_image:
																											assert len(lowerCamelCase__				) == 1
																				for i in range(len(lowerCamelCase__				)				):
																											if isinstance(images[i]				,							torch.Tensor				):
																																		images.insert(lowerCamelCase__				,							images.pop(lowerCamelCase__				).to(self.device				).float()				)
																											elif not isinstance(images[i]				,							torch.Tensor				):
																																		images.insert(
																																		    lowerCamelCase__				,							torch.as_tensor(img_tensorize(images.pop(lowerCamelCase__				)				,							input_format=self.input_format				)				)
																																		    .to(self.device				)
																																		    .float()				,							)
            # resize smallest edge
																				__lowercase      =    torch.tensor([im.shape[:2] for im in images]				)
																				__lowercase      =    self.aug(lowerCamelCase__				)
																				# transpose images and convert to torch tensors
																				# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																				# now normalize before pad to avoid useless arithmetic
																				__lowercase      =    [self.normalizer(lowerCamelCase__				) for x in images]
																				# now pad them to do the following operations
																				__lowercase     ,     __lowercase      =    self.pad(lowerCamelCase__				)
																				# Normalize

																				if self.size_divisibility > 0:
																											raise NotImplementedError()
																				# pad
																				__lowercase      =    torch.true_divide(lowerCamelCase__				,							lowerCamelCase__				)
																				if single_image:
																											return images[0], sizes[0], scales_yx[0]
																				else:
																											return images, sizes, scales_yx







def 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			str				)    -> Union[str, Any]:
							'''simple docstring'''


							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Tuple[int, int]				)    -> Dict:
							'''simple docstring'''


							assert torch.isfinite(UpperCamelCase__				).all(), "Box tensor contains infinite or NaN!"
							__lowercase     ,     __lowercase      =    box_size
							tensor[:, 0].clamp_(min=0				,       max=UpperCamelCase__				)
							tensor[:, 1].clamp_(min=0				,       max=UpperCamelCase__				)
							tensor[:, 2].clamp_(min=0				,       max=UpperCamelCase__				)
							tensor[:, 3].clamp_(min=0				,       max=UpperCamelCase__				)












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 a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		['image_processor', 'tokenizer']
						UpperCamelCase_	:       Dict       	=		'OwlViTImageProcessor'
						UpperCamelCase_	:       List[str]       	=		('CLIPTokenizer', 'CLIPTokenizerFast')






						def __init__( self :     str				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     List[Any]=None				,							**lowerCamelCase__ :     Union[str, 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 :     List[Any]				,							lowerCamelCase__ :     List[str]=None				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     str="max_length"				,							lowerCamelCase__ :     Union[str, Any]="np"				,							**lowerCamelCase__ :     Dict				)    ->						Union[str, 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     UpperCAmelCase_      ( self :     int				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Optional[int]				)    ->						Tuple:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     Tuple				,							**lowerCamelCase__ :     Tuple				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				,							*lowerCamelCase__ :     List[str]				,							**lowerCamelCase__ :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     str				,							*lowerCamelCase__ :     Optional[int]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


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






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


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






