






import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler





@dataclass
class       __a  (   __lowerCamelCase	):
 _lowerCAmelCase	:	int				=			0
 _lowerCAmelCase	:	bool				=			False
 _lowerCAmelCase	:	float				=			3.0







class       __a  (   unittest.TestCase	):







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      self.assertDictEqual(MockClass().to_kwargs()							,       {}						)
      self.assertDictEqual(MockClass(a=2						).to_kwargs()							,       {"a": 2}						)
      self.assertDictEqual(MockClass(a=2							,       b=UpperCamelCase_						).to_kwargs()							,       {"a": 2, "b": True}						)
      self.assertDictEqual(MockClass(a=2							,       c=2.2_5						).to_kwargs()							,       {"a": 2, "c": 2.2_5}						)







 @require_cuda
 def  __lowercase (		self :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  GradScalerKwargs(init_scale=10_24							,       growth_factor=2						)
      AcceleratorState._reset_state()
      UpperCamelCase__      :       str	    =  Accelerator(mixed_precision="fp16"							,       kwargs_handlers=[scaler_handler]						)
      print(accelerator.use_fpaa						)
      UpperCamelCase__      :       Union[str, Any]	    =  accelerator.scaler

      # Check the kwargs have been applied
      self.assertEqual(scaler._init_scale							,       1_0_2_4.0						)
      self.assertEqual(scaler._growth_factor							,       2.0						)

      # Check the other values are at the default
      self.assertEqual(scaler._backoff_factor							,       0.5						)
      self.assertEqual(scaler._growth_interval							,       20_00						)
      self.assertEqual(scaler._enabled							,       UpperCamelCase_						)







 @require_multi_gpu
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  ["torchrun", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__						)]
      execute_subprocess_async(UpperCamelCase_							,       env=os.environ.copy()						)


if __name__ == "__main__":
 lowerCamelCase     :							Union[str, Any]          =DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 lowerCamelCase     :							Optional[int]          =Accelerator(kwargs_handlers=[ddp_scaler])
 lowerCamelCase     :							Dict          =torch.nn.Linear(100, 200)
 lowerCamelCase     :							Optional[int]          =accelerator.prepare(model)

 # Check the values changed in kwargs
 lowerCamelCase     :							Optional[Any]          =''''''
 lowerCamelCase     :							Dict          =model.bucket_bytes_cap // (1024 * 1024)
 if observed_bucket_cap_map != 15:
  error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase     :							Optional[Any]    =False





class       __a  (   unittest.TestCase	):
 pass







@nightly
@require_torch_gpu
class       __a  (   unittest.TestCase	):







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)

      UpperCamelCase__      :       Dict	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       str	    =  pipe.dual_guided(
          prompt="first prompt"							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=2							,       output_type="numpy"							,       ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Tuple	    =  VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       int	    =  generator.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.dual_guided(
          prompt="first prompt"							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=2							,       output_type="numpy"							,       ).images

      assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"							,       torch_dtype=torch.floataa						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  "cyberpunk 2077"
      UpperCamelCase__      :       Any	    =  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__      :       int	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Any	    =  pipe.dual_guided(
          prompt=SCREAMING_SNAKE_CASE							,       image=SCREAMING_SNAKE_CASE							,       text_to_image_strength=0.7_5							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=50							,       output_type="numpy"							,       ).images

      UpperCamelCase__      :       Tuple	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Optional[int]	    =  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1

      UpperCamelCase__      :       List[str]	    =  "A painting of a squirrel eating a burger "
      UpperCamelCase__      :       List[Any]	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  pipe.text_to_image(
          prompt=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       guidance_scale=7.5							,       num_inference_steps=50							,       output_type="numpy"						).images

      UpperCamelCase__      :       Union[str, Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Dict	    =  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1

      UpperCamelCase__      :       Any	    =  pipe.image_variation(SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       output_type="numpy"						).images

      UpperCamelCase__      :       Optional[Any]	    =  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       int	    =  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1