




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_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=sys.maxsize      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'bilinear'
				SCREAMING_SNAKE_CASE_								=						max_size
				SCREAMING_SNAKE_CASE_								=						short_edge_length





	def __call__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



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

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

				return img_augs




class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



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

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





	def __call__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=False      )  ->					int:




				"""simple docstring"""



				with torch.no_grad():
							if not isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
										SCREAMING_SNAKE_CASE_								=						[images]
							if single_image:
										assert len(SCREAMING_SNAKE_CASE      ) == 1
							for i in range(len(SCREAMING_SNAKE_CASE      )      ):
										if isinstance(images[i]      ,     torch.Tensor      ):
													images.insert(SCREAMING_SNAKE_CASE      ,     images.pop(SCREAMING_SNAKE_CASE      ).to(self.device      ).float()      )
										elif not isinstance(images[i]      ,     torch.Tensor      ):
													images.insert(
													    SCREAMING_SNAKE_CASE      ,     torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE      )      ,     input_format=self.input_format      )      )
													    .to(self.device      )
													    .float()      ,     )
            # resize smallest edge
							SCREAMING_SNAKE_CASE_								=						torch.tensor([im.shape[:2] for im in images]      )
							SCREAMING_SNAKE_CASE_								=						self.aug(SCREAMING_SNAKE_CASE      )
							# 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
							SCREAMING_SNAKE_CASE_								=						[self.normalizer(SCREAMING_SNAKE_CASE      ) for x in images]
							# now pad them to do the following operations
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						self.pad(SCREAMING_SNAKE_CASE      )
							# Normalize

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




def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__     :     str		    =       {
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				SCREAMING_SNAKE_CASE__     :     Any		    =       [
				    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "AltCLIPPreTrainedModel",
				    "AltCLIPModel",
				    "AltCLIPTextModel",
				    "AltCLIPVisionModel",
				]


if TYPE_CHECKING:
				from .configuration_altclip import (
				    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AltCLIPConfig,
				    AltCLIPTextConfig,
				    AltCLIPVisionConfig,
				)
				from .processing_altclip import AltCLIPProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_altclip import (
								    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AltCLIPModel,
								    AltCLIPPreTrainedModel,
								    AltCLIPTextModel,
								    AltCLIPVisionModel,
								)


else:
				import sys

				SCREAMING_SNAKE_CASE__     :     Tuple		    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
