



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=()   ,   UpperCAmelCase__=None   ,   UpperCAmelCase__="no"   ,   UpperCAmelCase__="29500" ):


      """simple docstring"""



      lowerCamelCase								=		False
      lowerCamelCase								=		False
      if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
            lowerCamelCase								=		True
      elif "IPython" in sys.modules:
            lowerCamelCase								=		"google.colab" in str(sys.modules["IPython"].get_ipython() )

      try:
            lowerCamelCase								=		PrecisionType(mixed_precision.lower() )
      except ValueError:
            raise ValueError(
                F"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.""" )

      if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"   ,   __snake_case ) is not None):
            # TPU launch
            import torch_xla.distributed.xla_multiprocessing as xmp

            if len(AcceleratorState._shared_state ) > 0:
                  raise ValueError(
                      "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                      "your training function. Restart your notebook and make sure no cells initializes an "
                      "`Accelerator`." )
            if num_processes is None:
                  lowerCamelCase								=		8

            lowerCamelCase								=		PrepareForLaunch(__snake_case   ,   distributed_type="TPU" )
            print(F"""Launching a training on {num_processes} TPU cores.""" )
            xmp.spawn(__snake_case   ,   args=__snake_case   ,   nprocs=__snake_case   ,   start_method="fork" )
      elif in_colab:
            # No need for a distributed launch otherwise as it's either CPU or one GPU.
            if torch.cuda.is_available():
                  print("Launching training on one GPU." )
            else:
                  print("Launching training on one CPU." )
            function(*__snake_case )
      else:
            if num_processes is None:
                  raise ValueError(
                      "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." )

            if num_processes > 1:
                  # Multi-GPU launch
                  from torch.multiprocessing import start_processes
                  from torch.multiprocessing.spawn import ProcessRaisedException

                  if len(AcceleratorState._shared_state ) > 0:
                        raise ValueError(
                            "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                            "inside your training function. Restart your notebook and make sure no cells initializes an "
                            "`Accelerator`." )

                  if torch.cuda.is_initialized():
                        raise ValueError(
                            "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                            "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                            "function." )

                  # torch.distributed will expect a few environment variable to be here. We set the ones common to each
                  # process here (the other ones will be set be the launcher).
                  with patch_environment(
                      world_size=__snake_case   ,   master_addr="127.0.01"   ,   master_port=__snake_case   ,   mixed_precision=__snake_case ):
                        lowerCamelCase								=		PrepareForLaunch(__snake_case   ,   distributed_type="MULTI_GPU" )
                        print(F"""Launching training on {num_processes} GPUs.""" )
                        try:
                              start_processes(__snake_case   ,   args=__snake_case   ,   nprocs=__snake_case   ,   start_method="fork" )
                        except ProcessRaisedException as e:
                              if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                                    raise RuntimeError(
                                        "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                                        "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                                        "Please review your imports and test them when running the `notebook_launcher()` to identify "
                                        "which one is problematic." ) from e

            else:
                  # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
                  if is_mps_available():
                        lowerCamelCase								=		"1"
                        print("Launching training on MPS." )
                  elif torch.cuda.is_available():
                        print("Launching training on one GPU." )
                  else:
                        print("Launching training on CPU." )
                  function(*__snake_case )






def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=()   ,   UpperCAmelCase__=2 ):


      """simple docstring"""



      from torch.multiprocessing import start_processes

      with tempfile.NamedTemporaryFile() as tmp_file:
            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=__snake_case   ,   master_addr="127.0.01"   ,   master_port="29500"   ,   accelerate_mixed_precision="no"   ,   accelerate_debug_rdv_file=tmp_file.name   ,   accelerate_use_cpu="yes"   ,   ):
                  lowerCamelCase								=		PrepareForLaunch(__snake_case   ,   debug=__snake_case )
                  start_processes(__snake_case   ,   args=__snake_case   ,   nprocs=__snake_case   ,   start_method="fork" )




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):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           super().tearDown()
           gc.collect()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-inpaint/init_image.png"    )
           lowerCamelCase								=		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"    )

           lowerCamelCase								=		"xvjiarui/stable-diffusion-2-inpainting"
           lowerCamelCase			,							lowerCamelCase								=		FlaxStableDiffusionInpaintPipeline.from_pretrained(__a				,			safety_checker=__a    )

           lowerCamelCase								=		"Face of a yellow cat, high resolution, sitting on a park bench"

           lowerCamelCase								=		jax.random.PRNGKey(0    )
           lowerCamelCase								=		50

           lowerCamelCase								=		jax.device_count()
           lowerCamelCase								=		num_samples * [prompt]
           lowerCamelCase								=		num_samples * [init_image]
           lowerCamelCase								=		num_samples * [mask_image]
           lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		pipeline.prepare_inputs(__a				,			__a				,			__a    )

           # shard inputs and rng
           lowerCamelCase								=		replicate(__a    )
           lowerCamelCase								=		jax.random.split(__a				,			jax.device_count()    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )
           lowerCamelCase								=		shard(__a    )

           lowerCamelCase								=		pipeline(
               __a				,			__a				,			__a				,			__a				,			__a				,			__a				,			jit=__a    )

           lowerCamelCase								=		output.images.reshape(__a				,			5_12				,			5_12				,			3    )

           lowerCamelCase								=		images[0, 2_53:2_56, 2_53:2_56, -1]

           lowerCamelCase								=		jnp.asarray(jax.device_get(image_slice.flatten()    )    )
           lowerCamelCase								=		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