
"""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 				a			:



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=sys.maxsize      ):
          UpperCAmelCase__	:      List[Any]     							=					'bilinear'
          UpperCAmelCase__	:      List[Any]     							=					max_size
          UpperCAmelCase__	:      Optional[Any]     							=					short_edge_length



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

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

          return img_augs





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]     ,	cfg.INPUT.MAX_SIZE_TEST      )
          UpperCAmelCase__	:      Any     							=					cfg.INPUT.FORMAT
          UpperCAmelCase__	:      int     							=					cfg.SIZE_DIVISIBILITY
          UpperCAmelCase__	:      Any     							=					cfg.PAD_VALUE
          UpperCAmelCase__	:      int     							=					cfg.INPUT.MAX_SIZE_TEST
          UpperCAmelCase__	:      Union[str, Any]     							=					cfg.MODEL.DEVICE
          UpperCAmelCase__	:      Tuple     							=					torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )     ,	1     ,	1      )
          UpperCAmelCase__	:      Optional[Any]     							=					torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )     ,	1     ,	1      )
          UpperCAmelCase__	:      int     							=					lambda UpperCamelCase_      : (x - self.pixel_mean) / self.pixel_std



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[Any]     							=					tuple(max(UpperCamelCase_      ) for s in zip(*[img.shape for img in images]      )      )
          UpperCAmelCase__	:      str     							=					[im.shape[-2:] for im in images]
          UpperCAmelCase__	:      Union[str, Any]     							=					[
              nn.functional.pad(
                  UpperCamelCase_     ,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]     ,	value=self.pad_value     ,	)
              for size, im in zip(UpperCamelCase_     ,	UpperCamelCase_      )
          ]

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



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=False      ):
          with torch.no_grad():
              if not isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
                  UpperCAmelCase__	:      Any     							=					[images]
              if single_image:
                  assert len(UpperCamelCase_      ) == 1
              for i in range(len(UpperCamelCase_      )      ):
                  if isinstance(images[i]     ,	torch.Tensor      ):
                      images.insert(UpperCamelCase_     ,	images.pop(UpperCamelCase_      ).to(self.device      ).float()      )
                  elif not isinstance(images[i]     ,	torch.Tensor      ):
                      images.insert(
                          UpperCamelCase_     ,	torch.as_tensor(img_tensorize(images.pop(UpperCamelCase_      )     ,	input_format=self.input_format      )      )
                          .to(self.device      )
                          .float()     ,	)
            # resize smallest edge
              UpperCAmelCase__	:      List[str]     							=					torch.tensor([im.shape[:2] for im in images]      )
              UpperCAmelCase__	:      Optional[int]     							=					self.aug(UpperCamelCase_      )
              # 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__	:      List[Any]     							=					[self.normalizer(UpperCamelCase_      ) for x in images]
              # now pad them to do the following operations
              UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					self.pad(UpperCamelCase_      )
              # Normalize

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




def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes

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



"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   int           =     IFImgaImgSuperResolutionPipeline
      UpperCamelCase							:   Union[str, Any]           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      UpperCamelCase							:   Tuple           =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}      )
      UpperCamelCase							:   Union[str, Any]           =     PipelineTesterMixin.required_optional_params - {"""latents"""}



      def  __snake_case							(   self      ):
          return self._get_superresolution_dummy_components()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Optional[int]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      int     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					floats_tensor((1, 3, 16, 16)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					{
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'original_image': original_image,
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }

          return inputs



      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available()     ,	reason='XFormers attention is only available with CUDA and `xformers` installed'     ,	)
      def  __snake_case							(   self      ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )



      def  __snake_case							(   self      ):
          self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != 'cuda'     ,	reason='float16 requires CUDA'      )
      def  __snake_case							(   self      ):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1E-1      )



      def  __snake_case							(   self      ):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )



      def  __snake_case							(   self      ):
          self._test_save_load_local()



      def  __snake_case							(   self      ):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2     ,	)

