


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      a    (		_a     ,  _a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str							       =       StableDiffusionInstructPixaPixPipeline
     SCREAMING_SNAKE_CASE	:			int							       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
     SCREAMING_SNAKE_CASE	:			List[str]							       =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 			lowerCamelCase__			(    self					:		Any      )						->							List[str]:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Tuple  							=      UNetaDConditionModel(
              block_out_channels=(32, 64)							,			layers_per_block=2							,			sample_size=32							,			in_channels=8							,			out_channels=4							,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,			cross_attention_dim=32							,			)
          __UpperCAmelCase     :			Optional[int]  							=      PNDMScheduler(skip_prk_steps=snake_case_      )
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Tuple  							=      AutoencoderKL(
              block_out_channels=[32, 64]							,			in_channels=3							,			out_channels=3							,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,			latent_channels=4							,			)
          torch.manual_seed(0      )
          __UpperCAmelCase     :			List[Any]  							=      CLIPTextConfig(
              bos_token_id=0							,			eos_token_id=2							,			hidden_size=32							,			intermediate_size=37							,			layer_norm_eps=1E-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1000							,			)
          __UpperCAmelCase     :			Dict  							=      CLIPTextModel(snake_case_      )
          __UpperCAmelCase     :			Optional[Any]  							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

          __UpperCAmelCase     :			Optional[int]  							=      {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		Dict							,			snake_case					:		int=0      )						->							Any:
          __UpperCAmelCase     :			int  							=      floats_tensor((1, 3, 32, 32)							,			rng=random.Random(snake_case_      )      ).to(snake_case_      )
          __UpperCAmelCase     :			Any  							=      image.cpu().permute(0							,			2							,			3							,			1      )[0]
          __UpperCAmelCase     :			str  							=      Image.fromarray(np.uinta(snake_case_      )      ).convert('''RGB'''      )
          if str(snake_case_      ).startswith('''mps'''      ):
               __UpperCAmelCase     :			Any  							=      torch.manual_seed(snake_case_      )
          else:
               __UpperCAmelCase     :			List[Any]  							=      torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
          __UpperCAmelCase     :			Optional[Any]  							=      {
              """prompt""": """A painting of a squirrel eating a burger""",
              """image""": image,
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """image_guidance_scale""": 1,
              """output_type""": """numpy""",
          }
          return inputs

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							str:
          __UpperCAmelCase     :			Tuple  							=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			List[Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Any  							=      StableDiffusionInstructPixaPixPipeline(**snake_case_      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case_      )
          sd_pipe.set_progress_bar_config(disable=snake_case_      )

          __UpperCAmelCase     :			Any  							=      self.get_dummy_inputs(snake_case_      )
          __UpperCAmelCase     :			Dict  							=      sd_pipe(**snake_case_      ).images
          __UpperCAmelCase     :			str  							=      image[0, -3:, -3:, -1]
          assert image.shape == (1, 32, 32, 3)
          __UpperCAmelCase     :			str  							=      np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

          assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							str:
          __UpperCAmelCase     :			List[str]  							=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Optional[Any]  							=      StableDiffusionInstructPixaPixPipeline(**snake_case_      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case_      )
          sd_pipe.set_progress_bar_config(disable=snake_case_      )

          __UpperCAmelCase     :			List[str]  							=      self.get_dummy_inputs(snake_case_      )
          __UpperCAmelCase     :			Any  							=      """french fries"""
          __UpperCAmelCase     :			Optional[Any]  							=      sd_pipe(**snake_case_							,			negative_prompt=snake_case_      )
          __UpperCAmelCase     :			Optional[int]  							=      output.images
          __UpperCAmelCase     :			List[str]  							=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCAmelCase     :			Any  							=      np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

          assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							str:
          __UpperCAmelCase     :			str  							=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Optional[Any]  							=      StableDiffusionInstructPixaPixPipeline(**snake_case_      )
          __UpperCAmelCase     :			List[Any]  							=      sd_pipe.to(snake_case_      )
          sd_pipe.set_progress_bar_config(disable=snake_case_      )

          __UpperCAmelCase     :			Optional[int]  							=      self.get_dummy_inputs(snake_case_      )
          __UpperCAmelCase     :			Optional[int]  							=      [inputs["""prompt"""]] * 2

          __UpperCAmelCase     :			Dict  							=      np.array(inputs['''image''']      ).astype(np.floataa      ) / 255.0
          __UpperCAmelCase     :			Dict  							=      torch.from_numpy(snake_case_      ).unsqueeze(0      ).to(snake_case_      )
          __UpperCAmelCase     :			Dict  							=      image / 2 + 0.5
          __UpperCAmelCase     :			List[str]  							=      image.permute(0							,			3							,			1							,			2      )
          __UpperCAmelCase     :			str  							=      image.repeat(2							,			1							,			1							,			1      )

          __UpperCAmelCase     :			Dict  							=      sd_pipe(**snake_case_      ).images
          __UpperCAmelCase     :			Dict  							=      image[-1, -3:, -3:, -1]

          assert image.shape == (2, 32, 32, 3)
          __UpperCAmelCase     :			List[Any]  							=      np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

          assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[str]:
          __UpperCAmelCase     :			List[Any]  							=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			Dict  							=      self.get_dummy_components()
          __UpperCAmelCase     :			List[Any]  							=      EulerAncestralDiscreteScheduler(
              beta_start=0.00_085							,			beta_end=0.012							,			beta_schedule='''scaled_linear'''      )
          __UpperCAmelCase     :			Any  							=      StableDiffusionInstructPixaPixPipeline(**snake_case_      )
          __UpperCAmelCase     :			Optional[Any]  							=      sd_pipe.to(snake_case_      )
          sd_pipe.set_progress_bar_config(disable=snake_case_      )

          __UpperCAmelCase     :			Any  							=      self.get_dummy_inputs(snake_case_      )
          __UpperCAmelCase     :			List[str]  							=      sd_pipe(**snake_case_      ).images
          __UpperCAmelCase     :			Tuple  							=      image[0, -3:, -3:, -1]

          __UpperCAmelCase     :			Dict  							=      [round(snake_case_							,			4      ) for x in image_slice.flatten().tolist()]
          print(''','''.join([str(snake_case_      ) for x in slice]      )      )

          assert image.shape == (1, 32, 32, 3)
          __UpperCAmelCase     :			List[str]  							=      np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

          assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

     def 			lowerCamelCase__			(    self					:		Any      )						->							Any:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Dict:
          __UpperCAmelCase     :			Optional[int]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Dict  							=      StableDiffusionInstructPixaPixPipeline(**snake_case_      )
          __UpperCAmelCase     :			Any  							=      VaeImageProcessor(do_resize=snake_case_							,			do_normalize=snake_case_      )
          __UpperCAmelCase     :			Dict  							=      pipe.to(snake_case_      )
          pipe.set_progress_bar_config(disable=snake_case_      )

          __UpperCAmelCase     :			Optional[Any]  							=      pipe(**self.get_dummy_inputs_by_type(snake_case_							,			input_image_type='''pt'''      )      )[0]

          __UpperCAmelCase     :			Any  							=      components["""vae"""]
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_dummy_inputs_by_type(snake_case_							,			input_image_type='''pt'''      )

          for image_param in self.image_latents_params:
               if image_param in inputs.keys():
                    __UpperCAmelCase     :			Tuple  							=      vae.encode(inputs[image_param]      ).latent_dist.mode()

          __UpperCAmelCase     :			Any  							=      pipe(**snake_case_      )[0]

          __UpperCAmelCase     :			List[Any]  							=      np.abs(out - out_latents_inputs      ).max()
          self.assertLess(snake_case_							,			1E-4							,			'''passing latents as image input generate different result from passing image'''      )



@slow
@require_torch_gpu
class      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Dict      )						->							str:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		str=0      )						->							Union[str, Any]:
          __UpperCAmelCase     :			int  							=      torch.manual_seed(snake_case_      )
          __UpperCAmelCase     :			int  							=      load_image(
              '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''      )
          __UpperCAmelCase     :			Tuple  							=      {
              """prompt""": """turn him into a cyborg""",
              """image""": image,
              """generator""": generator,
              """num_inference_steps""": 3,
              """guidance_scale""": 7.5,
              """image_guidance_scale""": 1.0,
              """output_type""": """numpy""",
          }
          return inputs

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							List[Any]:
          __UpperCAmelCase     :			Any  							=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''							,			safety_checker=snake_case_      )
          pipe.to(snake_case_      )
          pipe.set_progress_bar_config(disable=snake_case_      )
          pipe.enable_attention_slicing()

          __UpperCAmelCase     :			Optional[int]  							=      self.get_inputs()
          __UpperCAmelCase     :			Dict  							=      pipe(**snake_case_      ).images
          __UpperCAmelCase     :			Optional[int]  							=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCAmelCase     :			str  							=      np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

          assert np.abs(expected_slice - image_slice      ).max() < 1E-3

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							int:
          __UpperCAmelCase     :			Any  							=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''							,			safety_checker=snake_case_      )
          __UpperCAmelCase     :			Union[str, Any]  							=      LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
          pipe.to(snake_case_      )
          pipe.set_progress_bar_config(disable=snake_case_      )
          pipe.enable_attention_slicing()

          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_inputs()
          __UpperCAmelCase     :			Tuple  							=      pipe(**snake_case_      ).images
          __UpperCAmelCase     :			Optional[Any]  							=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCAmelCase     :			Union[str, Any]  							=      np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

          assert np.abs(expected_slice - image_slice      ).max() < 1E-3

     def 			lowerCamelCase__			(    self					:		str      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''							,			safety_checker=snake_case_      )
          __UpperCAmelCase     :			List[str]  							=      DDIMScheduler.from_config(pipe.scheduler.config      )
          pipe.to(snake_case_      )
          pipe.set_progress_bar_config(disable=snake_case_      )
          pipe.enable_attention_slicing()

          __UpperCAmelCase     :			Dict  							=      self.get_inputs()
          __UpperCAmelCase     :			Optional[Any]  							=      pipe(**snake_case_      ).images
          __UpperCAmelCase     :			Optional[Any]  							=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCAmelCase     :			Union[str, Any]  							=      np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

          assert np.abs(expected_slice - image_slice      ).max() < 1E-3

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Tuple:
          __UpperCAmelCase     :			Optional[Any]  							=      0

          def callback_fn(snake_case					:		int							,			snake_case					:		int							,			snake_case					:		torch.FloatTensor      ) -> None:
               __UpperCAmelCase     :			List[str]  							=      True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                    __UpperCAmelCase     :			List[str]  							=      latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 64)
                    __UpperCAmelCase     :			int  							=      latents[0, -3:, -3:, -1]
                    __UpperCAmelCase     :			Optional[int]  							=      np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

                    assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
               elif step == 2:
                    __UpperCAmelCase     :			List[str]  							=      latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 64)
                    __UpperCAmelCase     :			Union[str, Any]  							=      latents[0, -3:, -3:, -1]
                    __UpperCAmelCase     :			Tuple  							=      np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

                    assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2

          __UpperCAmelCase     :			Tuple  							=      False

          __UpperCAmelCase     :			int  							=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''							,			safety_checker=snake_case_							,			torch_dtype=torch.floataa      )
          __UpperCAmelCase     :			Optional[int]  							=      pipe.to(snake_case_      )
          pipe.set_progress_bar_config(disable=snake_case_      )
          pipe.enable_attention_slicing()

          __UpperCAmelCase     :			List[Any]  							=      self.get_inputs()
          pipe(**snake_case_							,			callback=snake_case_							,			callback_steps=1      )
          assert callback_fn.has_been_called
          assert number_of_steps == 3

     def 			lowerCamelCase__			(    self					:		Any      )						->							List[str]:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          __UpperCAmelCase     :			Any  							=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
              '''timbrooks/instruct-pix2pix'''							,			safety_checker=snake_case_							,			torch_dtype=torch.floataa      )
          __UpperCAmelCase     :			Optional[int]  							=      pipe.to(snake_case_      )
          pipe.set_progress_bar_config(disable=snake_case_      )
          pipe.enable_attention_slicing(1      )
          pipe.enable_sequential_cpu_offload()

          __UpperCAmelCase     :			Any  							=      self.get_inputs()
          __UpperCAmelCase     :			Dict  							=      pipe(**snake_case_      )

          __UpperCAmelCase     :			int  							=      torch.cuda.max_memory_allocated()
          # make sure that less than 2.2 GB is allocated
          assert mem_bytes < 2.2 * 10**9






     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          __UpperCAmelCase     :			List[str]  							=      self.get_inputs()
          # resize to resolution that is divisible by 8 but not 16 or 32
          __UpperCAmelCase     :			List[str]  							=      inputs["""image"""].resize((504, 504)      )

          __UpperCAmelCase     :			Optional[Any]  							=      """timbrooks/instruct-pix2pix"""
          __UpperCAmelCase     :			Union[str, Any]  							=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
              snake_case_							,			safety_checker=snake_case_							,			)
          pipe.to(snake_case_      )
          pipe.set_progress_bar_config(disable=snake_case_      )
          pipe.enable_attention_slicing()

          __UpperCAmelCase     :			Any  							=      pipe(**snake_case_      )
          __UpperCAmelCase     :			str  							=      output.images[0]

          __UpperCAmelCase     :			Optional[int]  							=      image[255:258, 383:386, -1]

          assert image.shape == (504, 504, 3)
          __UpperCAmelCase     :			Tuple  							=      np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

          assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-3



'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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


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




def      _a     (						_lowercase :     List[Any]="no"      , _lowercase :     str = default_json_config_file      , _lowercase :     bool = False  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Dict  							=      Path(_lowercase  )
     path.parent.mkdir(parents=_lowercase      , exist_ok=_lowercase  )
     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     :			List[str]  							=      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     :			int  							=      {
         '''compute_environment''': '''LOCAL_MACHINE''',
         '''mixed_precision''': mixed_precision,
     }
     if torch.cuda.is_available():
          __UpperCAmelCase     :			Optional[Any]  							=      torch.cuda.device_count()
          __UpperCAmelCase     :			List[str]  							=      num_gpus
          __UpperCAmelCase     :			int  							=      False
          if num_gpus > 1:
               __UpperCAmelCase     :			Any  							=      '''MULTI_GPU'''
          else:
               __UpperCAmelCase     :			int  							=      '''NO'''
     elif is_xpu_available() and use_xpu:
          __UpperCAmelCase     :			List[Any]  							=      torch.xpu.device_count()
          __UpperCAmelCase     :			List[Any]  							=      num_xpus
          __UpperCAmelCase     :			Optional[int]  							=      False
          if num_xpus > 1:
               __UpperCAmelCase     :			Any  							=      '''MULTI_XPU'''
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      '''NO'''
     elif is_npu_available():
          __UpperCAmelCase     :			Dict  							=      torch.npu.device_count()
          __UpperCAmelCase     :			Any  							=      num_npus
          __UpperCAmelCase     :			Any  							=      False
          if num_npus > 1:
               __UpperCAmelCase     :			Dict  							=      '''MULTI_NPU'''
          else:
               __UpperCAmelCase     :			Optional[int]  							=      '''NO'''
     else:
          __UpperCAmelCase     :			List[str]  							=      0
          __UpperCAmelCase     :			Dict  							=      True
          __UpperCAmelCase     :			Dict  							=      1
          __UpperCAmelCase     :			Tuple  							=      '''NO'''
     __UpperCAmelCase     :			List[Any]  							=      ClusterConfig(**_lowercase  )
     config.to_json_file(_lowercase  )
     return path




def      _a     (						_lowercase :     Union[str, Any]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      parser.add_parser('''default'''      , parents=_lowercase      , help=_lowercase      , formatter_class=_lowercase  )
     parser.add_argument(
         '''--config_file'''      , default=_lowercase      , 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=_lowercase      , 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=_lowercase  )
     return parser




def      _a     (						_lowercase :     List[Any]  ):





     '''simple docstring'''






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