


"""simple docstring"""


import os
import sys
import tempfile

import torch

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


def _lowerCamelCase	(		_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"    , _UpperCamelCase      ) 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(_UpperCamelCase    , distributed_type="TPU"      )
						print(f"Launching a training on {num_processes} TPU cores."      )
						xmp.spawn(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , 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(*_UpperCamelCase      )
			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=_UpperCamelCase    , master_addr="127.0.01"    , master_port=_UpperCamelCase    , mixed_precision=_UpperCamelCase      ):
												__lowerCAmelCase				    =			PrepareForLaunch(_UpperCamelCase    , distributed_type="MULTI_GPU"      )
												print(f"Launching training on {num_processes} GPUs."      )
												try:
															start_processes(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , 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(*_UpperCamelCase      )


def _lowerCamelCase	(		_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=_UpperCamelCase    , 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(_UpperCamelCase    , debug=_UpperCamelCase      )
									start_processes(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , start_method="fork"      )








"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
			__snake_case	:						List[str]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						Tuple						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						str						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						List[str]						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Dict						=				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						str						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						List[str]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						Any						=				output.images

			__snake_case	:						Tuple						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Tuple						=				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-1



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_dpmpp_2m'''					)

			__snake_case	:						int						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						Optional[Any]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe(
			    [prompt]				,       generator=a_				,       guidance_scale=7.5				,       num_inference_steps=15				,       output_type='''np'''				,       use_karras_sigmas=a_				,       )

			__snake_case	:						Optional[int]						=				output.images

			__snake_case	:						Any						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Any						=				np.array(
			    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2





