



"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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		(					__UpperCAmelCase		, __UpperCAmelCase		, unittest.TestCase    ):
  UpperCamelCase		:			List[str]         =   IFSuperResolutionPipeline
  UpperCamelCase		:			Tuple         =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  UpperCamelCase		:			Optional[Any]         =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  UpperCamelCase		:			Optional[int]         =   PipelineTesterMixin.required_optional_params - {"""latents"""}



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Any:


     '''simple docstring'''




     return self._get_superresolution_dummy_components()



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   str=0						)       ->   List[str]:


     '''simple docstring'''




     if str(lowerCamelCase__						).startswith("""mps"""						):
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =torch.manual_seed(lowerCamelCase__						)
     else:
        SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.Generator(device=lowerCamelCase__						).manual_seed(lowerCamelCase__						)

     SCREAMING_SNAKE_CASE_:	Optional[int]            =floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(lowerCamelCase__						)						).to(lowerCamelCase__						)

     SCREAMING_SNAKE_CASE_:	int            ={
         """prompt""": """A painting of a squirrel eating a burger""",
         """image""": 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 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Tuple:


     '''simple docstring'''




     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



  def 		lowerCamelCase__    (  self	:   Any						)       ->   List[Any]:


     '''simple docstring'''




     self._test_save_load_optional_components()



  @unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""						)
  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   List[Any]:


     '''simple docstring'''




     super().test_save_load_floataa(expected_max_diff=1E-1						)



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


     '''simple docstring'''




     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   Dict:


     '''simple docstring'''




     self._test_save_load_local()





  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




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








"""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	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict=sys.maxsize						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            ="""bilinear"""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =short_edge_length





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


     '''simple docstring'''




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

        if img.dtype == np.uinta:
           SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	List[str]            =pil_image.resize((neww, newh)    ,						PILImageResampling.BILINEAR						)
           SCREAMING_SNAKE_CASE_:	Dict            =np.asarray(lowerCAmelCase						)
        else:
           SCREAMING_SNAKE_CASE_:	str            =img.permute(2    ,						0    ,						1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
           SCREAMING_SNAKE_CASE_:	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    a		:



  def __init__(  self	:   Any    ,						lowerCAmelCase	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   Any:


     '''simple docstring'''




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


     '''simple docstring'''




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

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







def        __magic_name__			(							lowercase							,     lowercase	):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes





def        __magic_name__			(							lowercase							,     lowercase	):
   assert torch.isfinite(lowercase	).all(), "Box tensor contains infinite or NaN!"
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =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	)



