




from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_lowerCAmelCase						:   int		=   "Create a default config file for Accelerate with only a few flags set."



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int="no"						,							_lowerCAmelCase							:   Optional[Any] = default_json_config_file						,							_lowerCAmelCase							:   int = False	):




 """simple docstring"""







 UpperCAmelCase__							 =						Path(_snake_case	)
 path.parent.mkdir(parents=_snake_case						,							exist_ok=_snake_case	)
 if path.exists():
  print(
      F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''	)
  return False
 UpperCAmelCase__							 =						mixed_precision.lower()
 if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
  raise ValueError(
      F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''	)
 UpperCAmelCase__							 =						{
     "compute_environment": "LOCAL_MACHINE",
     "mixed_precision": mixed_precision,
 }
 if torch.cuda.is_available():
  UpperCAmelCase__							 =						torch.cuda.device_count()
  UpperCAmelCase__							 =						num_gpus
  UpperCAmelCase__							 =						False
  if num_gpus > 1:
   UpperCAmelCase__							 =						"MULTI_GPU"
  else:
   UpperCAmelCase__							 =						"NO"
 elif is_xpu_available() and use_xpu:
  UpperCAmelCase__							 =						torch.xpu.device_count()
  UpperCAmelCase__							 =						num_xpus
  UpperCAmelCase__							 =						False
  if num_xpus > 1:
   UpperCAmelCase__							 =						"MULTI_XPU"
  else:
   UpperCAmelCase__							 =						"NO"
 elif is_npu_available():
  UpperCAmelCase__							 =						torch.npu.device_count()
  UpperCAmelCase__							 =						num_npus
  UpperCAmelCase__							 =						False
  if num_npus > 1:
   UpperCAmelCase__							 =						"MULTI_NPU"
  else:
   UpperCAmelCase__							 =						"NO"
 else:
  UpperCAmelCase__							 =						0
  UpperCAmelCase__							 =						True
  UpperCAmelCase__							 =						1
  UpperCAmelCase__							 =						"NO"
 UpperCAmelCase__							 =						ClusterConfig(**_snake_case	)
 config.to_json_file(_snake_case	)
 return path



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]						,							_lowerCAmelCase							:   Optional[Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						parser.add_parser("default"						,							parents=_snake_case						,							help=_snake_case						,							formatter_class=_snake_case	)
 parser.add_argument(
     "--config_file"						,							default=_snake_case						,							help=(
         "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
         "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
         "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
         "with 'huggingface'."
     )						,							dest="save_location"						,							)

 parser.add_argument(
     "--mixed_precision"						,							choices=["no", "fp16", "bf16"]						,							type=_snake_case						,							help="Whether or not to use mixed precision training. "
     "Choose between FP16 and BF16 (bfloat16) training. "
     "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."						,							default="no"						,							)
 parser.set_defaults(func=_snake_case	)
 return parser



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 UpperCAmelCase__							 =						write_basic_config(args.mixed_precision						,							args.save_location	)
 if config_file:
  print(F'''accelerate configuration saved at {config_file}'''	)











import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_UpperCamelCase      (				lowerCAmelCase	,       lowerCAmelCase	,       unittest.TestCase		):
      UpperCAmelCase_        =		IFInpaintingSuperResolutionPipeline
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
      UpperCAmelCase_        =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}		)
      UpperCAmelCase_        =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       return self._get_superresolution_dummy_components()
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]=0      )     ->			int:
       if str(lowerCamelCase      ).startswith("mps"      ):
        UpperCAmelCase__							 =						torch.manual_seed(lowerCamelCase      )
       else:
        UpperCAmelCase__							 =						torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

       UpperCAmelCase__							 =						floats_tensor((1, 3, 16, 16) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )
       UpperCAmelCase__							 =						floats_tensor((1, 3, 32, 32) ,      rng=random.Random(lowerCamelCase      )      ).to(lowerCamelCase      )

       UpperCAmelCase__							 =						{
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_image,
           "mask_image": mask_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 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Dict:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Dict:
       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda" ,      reason="float16 requires CUDA"      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       # 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 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._test_save_load_local()







      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[str]:
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2 ,      )





