





"""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   ,      lowerCAmelCase__   ,      lowerCAmelCase__=sys.maxsize   )			->				str:
								a   :	List[str]			=       "bilinear"
								a   :	str			=       max_size
								a   :	Union[str, Any]			=       short_edge_length






							def __call__(     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	List[Any]			=       []
								for img in imgs:
									a, a   :	int			=       img.shape[:2]
									# later: provide list and randomly choose index for resize
									a   :	str			=       np.random.randint(self.short_edge_length[0]   ,      self.short_edge_length[1] + 1   )
									if size == 0:
										return img
									a   :	Dict			=       size * 1.0 / min(lowerCAmelCase__   ,      lowerCAmelCase__   )
									if h < w:
										a, a   :	Dict			=       size, scale * w
									else:
										a, a   :	List[Any]			=       scale * h, size
									if max(lowerCAmelCase__   ,      lowerCAmelCase__   ) > self.max_size:
										a   :	int			=       self.max_size * 1.0 / max(lowerCAmelCase__   ,      lowerCAmelCase__   )
										a   :	Dict			=       newh * scale
										a   :	Optional[int]			=       neww * scale
									a   :	int			=       int(neww + 0.5   )
									a   :	int			=       int(newh + 0.5   )

									if img.dtype == np.uinta:
										a   :	Optional[int]			=       Image.fromarray(lowerCAmelCase__   )
										a   :	Any			=       pil_image.resize((neww, newh)   ,      PILImageResampling.BILINEAR   )
										a   :	Optional[Any]			=       np.asarray(lowerCAmelCase__   )
									else:
										a   :	Dict			=       img.permute(2   ,      0   ,      1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
										a   :	Optional[Any]			=       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   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a   :	Any			=       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]   ,      cfg.INPUT.MAX_SIZE_TEST   )
								a   :	Any			=       cfg.INPUT.FORMAT
								a   :	Optional[Any]			=       cfg.SIZE_DIVISIBILITY
								a   :	List[str]			=       cfg.PAD_VALUE
								a   :	List[str]			=       cfg.INPUT.MAX_SIZE_TEST
								a   :	Optional[int]			=       cfg.MODEL.DEVICE
								a   :	List[Any]			=       torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )   ,      1   ,      1   )
								a   :	Union[str, Any]			=       torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )   ,      1   ,      1   )
								a   :	Dict			=       lambda lowerCAmelCase__   : (x - self.pixel_mean) / self.pixel_std






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				str:
								a   :	int			=       tuple(max(lowerCAmelCase__   ) for s in zip(*[img.shape for img in images]   )   )
								a   :	Dict			=       [im.shape[-2:] for im in images]
								a   :	Optional[Any]			=       [
								    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   ,      lowerCAmelCase__   ,      lowerCAmelCase__=False   )			->				List[Any]:
								with torch.no_grad():
									if not isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
										a   :	Optional[Any]			=       [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
									a   :	int			=       torch.tensor([im.shape[:2] for im in images]   )
									a   :	List[str]			=       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
									a   :	Optional[int]			=       [self.normalizer(lowerCAmelCase__   ) for x in images]
									# now pad them to do the following operations
									a, a   :	List[Any]			=       self.pad(lowerCAmelCase__   )
									# Normalize

									if self.size_divisibility > 0:
										raise NotImplementedError()
									# pad
									a   :	Union[str, Any]			=       torch.true_divide(lowerCAmelCase__   ,      lowerCAmelCase__   )
									if single_image:
										return images[0], sizes[0], scales_yx[0]
									else:
										return images, sizes, scales_yx






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					Any      )				->str:






	'''simple docstring'''






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







def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					Tuple[int, int]      )				->Union[str, Any]:






	'''simple docstring'''






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









"""simple docstring"""




import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible


class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Optional[int]			=       [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	str			=       [
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Dict:
								a   :	List[str]			=       [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								    "unet/diffusion_pytorch_model.bin",
								    # Removed: 'unet/diffusion_pytorch_model.safetensors',
								]
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       [
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								]
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       [
								    "safety_checker/pytorch_model.bin",
								    "safety_checker/model.safetensors",
								    "vae/diffusion_pytorch_model.bin",
								    "vae/diffusion_pytorch_model.safetensors",
								    "text_encoder/pytorch_model.bin",
								    # Removed: 'text_encoder/model.safetensors',
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Dict:
								a   :	Dict			=       [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								a   :	Dict			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	List[Any]			=       [
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								a   :	Any			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				int:
								# pass variant but use the non-variant filenames
								a   :	int			=       [
								    "unet/diffusion_pytorch_model.bin",
								    "unet/diffusion_pytorch_model.safetensors",
								]
								a   :	Tuple			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								a   :	str			=       [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								    "unet/diffusion_pytorch_model.fp16.bin",
								    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
								]
								a   :	Any			=       "fp16"
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								a   :	Union[str, Any]			=       [
								    "text_encoder/pytorch_model.fp16.bin",
								    "text_encoder/model.fp16.safetensors",
								]
								a   :	str			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								# pass variant but use the non-variant filenames
								a   :	Optional[int]			=       [
								    "text_encoder/pytorch_model.bin",
								    "text_encoder/model.safetensors",
								]
								a   :	str			=       "fp16"
								self.assertTrue(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Any			=       [
								    "safety_checker/pytorch_model.fp16.bin",
								    "safety_checker/model.fp16.safetensors",
								    "vae/diffusion_pytorch_model.fp16.bin",
								    "vae/diffusion_pytorch_model.fp16.safetensors",
								    "text_encoder/pytorch_model.fp16.bin",
								    # 'text_encoder/model.fp16.safetensors',
								    "unet/diffusion_pytorch_model.fp16.bin",
								    "unet/diffusion_pytorch_model.fp16.safetensors",
								]
								a   :	Optional[int]			=       "fp16"
								self.assertFalse(is_safetensors_compatible(lowerCAmelCase__   ,      variant=lowerCAmelCase__   )   )


