






'''simple docstring'''



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 	UpperCamelCase__:




				def __init__(		self      :			Optional[Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			int=sys.maxsize				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     '''bilinear'''
										UpperCAmelCase					    =     max_size
										UpperCAmelCase					    =     short_edge_length





				def __call__(		self      :			Any				,   lowerCAmelCase      :			int				)->					Union[str, Any]:

										"""simple docstring"""




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

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

										return img_augs







class 	UpperCamelCase__:




				def __init__(		self      :			int				,   lowerCAmelCase      :			str				)->					Tuple:

										"""simple docstring"""




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




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Optional[int]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     tuple(max(lowerCAmelCase				) for s in zip(*[img.shape for img in images]				)				)
										UpperCAmelCase					    =     [im.shape[-2:] for im in images]
										UpperCAmelCase					    =     [
										    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      :			Any				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			Optional[Any]=False				)->					Union[str, Any]:

										"""simple docstring"""




										with torch.no_grad():
																if not isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						UpperCAmelCase					    =     [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
																UpperCAmelCase					    =     torch.tensor([im.shape[:2] for im in images]				)
																UpperCAmelCase					    =     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
																UpperCAmelCase					    =     [self.normalizer(lowerCAmelCase				) for x in images]
																# now pad them to do the following operations
																UpperCAmelCase    ,    UpperCAmelCase					    =     self.pad(lowerCAmelCase				)
																# Normalize

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







def 				lowerCamelCase__				(	A       :      Dict				,       A       :      Optional[int]					):




						'''simple docstring'''






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




def 				lowerCamelCase__				(	A       :      Any				,       A       :      Tuple[int, int]					):




						'''simple docstring'''






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









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[Any]  		=	["image_processor", "tokenizer"]
				__magic_name__    :				Tuple  		=	"ViTImageProcessor"
				__magic_name__    :				int  		=	("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			Tuple=None				,   lowerCAmelCase      :			List[str]=None				,   **lowerCAmelCase      :			Optional[int]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     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				,   )
																UpperCAmelCase					    =     kwargs.pop('''feature_extractor'''				)

										UpperCAmelCase					    =     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      :			int				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Union[str, Any]=None				,   lowerCAmelCase      :			int=None				,   **lowerCAmelCase      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''				)

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

										if text is not None:
																UpperCAmelCase					    =     self.tokenizer(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if images is not None:
																UpperCAmelCase					    =     self.image_processor(lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				)

										if visual_prompt is not None and images is not None:
																UpperCAmelCase					    =     {
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase					    =     image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase					    =     {
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase				)				,   tensor_type=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




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




				def 					a__(		self      :			List[Any]				,   *lowerCAmelCase      :			str				,   **lowerCAmelCase      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




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




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

										"""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 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




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





