






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			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							int				= 0
    lowerCamelCase	:							bool				= False
    lowerCamelCase	:							float				= 3.0




class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def _a							(				self      :     Tuple      )      ->							Dict:
           # If no defaults are changed, `to_kwargs` returns an empty dict.
           self.assertDictEqual(MockClass().to_kwargs() ,     {}      )
           self.assertDictEqual(MockClass(a=2      ).to_kwargs() ,     {"""a""": 2}      )
           self.assertDictEqual(MockClass(a=2 ,     b=__SCREAMING_SNAKE_CASE      ).to_kwargs() ,     {"""a""": 2, """b""": True}      )
           self.assertDictEqual(MockClass(a=2 ,     c=2.25      ).to_kwargs() ,     {"""a""": 2, """c""": 2.25}      )



    @require_cuda
    def _a							(				self      :     str      )      ->							Optional[Any]:
           # If no defaults are changed, `to_kwargs` returns an empty dict.
           __UpperCAmelCase     				=GradScalerKwargs(init_scale=1024 ,     growth_factor=2      )
           AcceleratorState._reset_state()
           __UpperCAmelCase     				=Accelerator(mixed_precision="""fp16""" ,     kwargs_handlers=[scaler_handler]      )
           print(accelerator.use_fpaa      )
           __UpperCAmelCase     				=accelerator.scaler

           # Check the kwargs have been applied
           self.assertEqual(scaler._init_scale ,     1_024.0      )
           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 ,     2000      )
           self.assertEqual(scaler._enabled ,     __SCREAMING_SNAKE_CASE      )



    @require_multi_gpu
    def _a							(				self      :     Optional[Any]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
           execute_subprocess_async(__SCREAMING_SNAKE_CASE ,     env=os.environ.copy()      )


if __name__ == "__main__":
   __A					=	DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   __A					=	Accelerator(kwargs_handlers=[ddp_scaler])
   __A					=	torch.nn.Linear(1_00, 2_00)
   __A					=	accelerator.prepare(model)

   # Check the values changed in kwargs
   __A					=	""
   __A					=	model.bucket_bytes_cap // (10_24 * 10_24)
   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)












"""simple docstring"""



import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard




@slow
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()




   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"							)
        UpperCAmelCase__						:    Any							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

        UpperCAmelCase__						:    str							=							"xvjiarui/stable-diffusion-2-inpainting"
        UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__							,			safety_checker=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							"Face of a yellow cat, high resolution, sitting on a park bench"

        UpperCAmelCase__						:    List[str]							=							jax.random.PRNGKey(0							)
        UpperCAmelCase__						:    Optional[int]							=							5_0

        UpperCAmelCase__						:    List[Any]							=							jax.device_count()
        UpperCAmelCase__						:    int							=							num_samples * [prompt]
        UpperCAmelCase__						:    str							=							num_samples * [init_image]
        UpperCAmelCase__						:    Optional[Any]							=							num_samples * [mask_image]
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							pipeline.prepare_inputs(snake_case__							,			snake_case__							,			snake_case__							)

        # shard inputs and rng
        UpperCAmelCase__						:    Dict							=							replicate(snake_case__							)
        UpperCAmelCase__						:    Dict							=							jax.random.split(snake_case__							,			jax.device_count()							)
        UpperCAmelCase__						:    List[str]							=							shard(snake_case__							)
        UpperCAmelCase__						:    str							=							shard(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							shard(snake_case__							)

        UpperCAmelCase__						:    Dict							=							pipeline(
            snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			jit=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							output.images.reshape(snake_case__							,			5_1_2							,			5_1_2							,			3							)

        UpperCAmelCase__						:    Tuple							=							images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        UpperCAmelCase__						:    str							=							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        UpperCAmelCase__						:    int							=							jnp.array(
            [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]							)
        print(f'output_slice: {output_slice}'							)
        assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


