"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput



@dataclass
class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      torch.FloatTensor
				_lowerCAmelCase       :      torch.FloatTensor





class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      str     					=  1

				@register_to_config
				def __init__(			self						,      lowerCAmelCase = 20_00						,      lowerCAmelCase = 0.15						,      lowerCAmelCase = 0.01						,      lowerCAmelCase = 13_48.0						,      lowerCAmelCase = 1E-5						,      lowerCAmelCase = 1						,      ):






										"""simple docstring"""

										snake_case        =     sigma_max

										# setable values
										snake_case        =     None

										self.set_sigmas(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										return sample

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										snake_case        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

										snake_case        =     torch.linspace(1						,      lowerCAmelCase						,      lowerCAmelCase						,      device=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										snake_case        =     sigma_min if sigma_min is not None else self.config.sigma_min
										snake_case        =     sigma_max if sigma_max is not None else self.config.sigma_max
										snake_case        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if self.timesteps is None:
																self.set_timesteps(lowerCAmelCase						,      lowerCAmelCase      )

										snake_case        =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
										snake_case        =     torch.exp(torch.linspace(math.log(lowerCAmelCase      )						,      math.log(lowerCAmelCase      )						,      lowerCAmelCase      )      )
										snake_case        =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										return torch.where(
										    timesteps == 0						,      torch.zeros_like(t.to(timesteps.device      )      )						,      self.discrete_sigmas[timesteps - 1].to(timesteps.device      )						,      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      ):






										"""simple docstring"""

										if self.timesteps is None:
																raise ValueError(
																    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'      )

										snake_case        =     timestep * torch.ones(
										    sample.shape[0]						,      device=sample.device      )  # torch.repeat_interleave(timestep, sample.shape[0])
										snake_case        =     (timestep * (len(self.timesteps      ) - 1)).long()

										# mps requires indices to be in the same device, so we use cpu as is the default with cuda
										snake_case        =     timesteps.to(self.discrete_sigmas.device      )

										snake_case        =     self.discrete_sigmas[timesteps].to(sample.device      )
										snake_case        =     self.get_adjacent_sigma(lowerCAmelCase						,      lowerCAmelCase      ).to(sample.device      )
										snake_case        =     torch.zeros_like(lowerCAmelCase      )
										snake_case        =     (sigma**2 - adjacent_sigma**2) ** 0.5

										# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
										# also equation 47 shows the analog from SDE models to ancestral sampling methods
										snake_case        =     diffusion.flatten()
										while len(diffusion.shape      ) < len(sample.shape      ):
																snake_case        =     diffusion.unsqueeze(-1      )
										snake_case        =     drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										snake_case        =     randn_tensor(
										    sample.shape						,      layout=sample.layout						,      generator=lowerCAmelCase						,      device=sample.device						,      dtype=sample.dtype      )
										snake_case        =     sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										snake_case        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

										if not return_dict:
																return (prev_sample, prev_sample_mean)

										return SdeVeOutput(prev_sample=lowerCAmelCase						,      prev_sample_mean=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      ):






										"""simple docstring"""

										if self.timesteps is None:
																raise ValueError(
																    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'      )

										# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
										# sample noise for correction
										snake_case        =     randn_tensor(sample.shape						,      layout=sample.layout						,      generator=lowerCAmelCase      ).to(sample.device      )

										# compute step size from the model_output, the noise, and the snr
										snake_case        =     torch.norm(model_output.reshape(model_output.shape[0]						,      -1      )						,      dim=-1      ).mean()
										snake_case        =     torch.norm(noise.reshape(noise.shape[0]						,      -1      )						,      dim=-1      ).mean()
										snake_case        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
										snake_case        =     step_size * torch.ones(sample.shape[0]      ).to(sample.device      )
										# self.repeat_scalar(step_size, sample.shape[0])

										# compute corrected sample: model_output term and noise term
										snake_case        =     step_size.flatten()
										while len(step_size.shape      ) < len(sample.shape      ):
																snake_case        =     step_size.unsqueeze(-1      )
										snake_case        =     sample + step_size * model_output
										snake_case        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     timesteps.to(original_samples.device      )
										snake_case        =     self.discrete_sigmas.to(original_samples.device      )[timesteps]
										snake_case        =     (
										    noise * sigmas[:, None, None, None]
										    if noise is not None
										    else torch.randn_like(lowerCAmelCase      ) * sigmas[:, None, None, None]
										)
										snake_case        =     noise + original_samples
										return noisy_samples

				def __len__(			self      ):






										"""simple docstring"""

										return self.config.num_train_timesteps



"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Optional[int]=False   )   ->			Any:





						"""simple docstring"""






						try:
												snake_case        =     os.environ[key]
						except KeyError:
												# KEY isn't set, default to `default`.
												snake_case        =     default
						else:
												# KEY is set, convert it to True or False.
												try:
																		snake_case        =     strtobool(_UpperCamelCase   )
												except ValueError:
																		# More values are supported, but let's keep the message simple.
																		raise ValueError(f"""If set, {key} must be yes or no."""   )
						return _value


SCREAMING_SNAKE_CASE__   =					parse_flag_from_env("RUN_SLOW", default=False)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			str:





						"""simple docstring"""






						return unittest.skip('Test was skipped'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			List[str]:





						"""simple docstring"""






						return unittest.skipUnless(_run_slow_tests	,     'test is slow'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			int:





						"""simple docstring"""






						return unittest.skipUnless(not torch.cuda.is_available()	,     'test requires only a CPU'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			Tuple:





						"""simple docstring"""






						return unittest.skipUnless(torch.cuda.is_available()	,     'test requires a GPU'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict   )   ->			Optional[int]:





						"""simple docstring"""






						return unittest.skipUnless(is_xpu_available()	,     'test requires a XPU'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict   )   ->			Any:





						"""simple docstring"""






						return unittest.skipUnless(is_mps_available()	,     'test requires a `mps` backend support in `torch`'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			str:





						"""simple docstring"""






						return unittest.skipUnless(
						    is_transformers_available() and is_datasets_available()	,     'test requires the Hugging Face suite'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]   )   ->			Dict:





						"""simple docstring"""






						return unittest.skipUnless(is_bnb_available()	,     'test requires the bitsandbytes library'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]   )   ->			List[Any]:





						"""simple docstring"""






						return unittest.skipUnless(is_tpu_available()	,     'test requires TPU'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict   )   ->			int:





						"""simple docstring"""






						return unittest.skipUnless(torch.cuda.device_count() == 1	,     'test requires a GPU'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			List[Any]:





						"""simple docstring"""






						return unittest.skipUnless(torch.xpu.device_count() == 1	,     'test requires a XPU'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]   )   ->			List[Any]:





						"""simple docstring"""






						return unittest.skipUnless(torch.cuda.device_count() > 1	,     'test requires multiple GPUs'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			List[str]:





						"""simple docstring"""






						return unittest.skipUnless(torch.xpu.device_count() > 1	,     'test requires multiple XPUs'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			str:





						"""simple docstring"""






						return unittest.skipUnless(is_safetensors_available()	,     'test requires safetensors'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict   )   ->			List[str]:





						"""simple docstring"""






						return unittest.skipUnless(is_deepspeed_available()	,     'test requires DeepSpeed'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			List[Any]:





						"""simple docstring"""






						return unittest.skipUnless(is_torch_version('>='	,     '1.12.0'   )	,     'test requires torch version >= 1.12.0'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict=None	,     _UpperCamelCase    :			Dict=None   )   ->			int:





						"""simple docstring"""






						if test_case is None:
												return partial(_UpperCamelCase	,     version=_UpperCamelCase   )
						return unittest.skipUnless(is_torch_version('>='	,     _UpperCamelCase   )	,     f"""test requires torch version >= {version}"""   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]   )   ->			List[str]:





						"""simple docstring"""






						return unittest.skipUnless(is_tensorboard_available()	,     'test requires Tensorboard'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			int:





						"""simple docstring"""






						return unittest.skipUnless(is_wandb_available()	,     'test requires wandb'   )(_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			Any:





						"""simple docstring"""






						return unittest.skipUnless(is_comet_ml_available()	,     'test requires comet_ml'   )(_UpperCamelCase   )


SCREAMING_SNAKE_CASE__   =					(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			List[str]:





						"""simple docstring"""






						return unittest.skipUnless(
						    _atleast_one_tracker_available	,     'test requires at least one tracker to be available and for `comet_ml` to not be installed'	,     )(_UpperCamelCase   )



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  True

				@classmethod
				def 						snake_case	(			cls      ):






										"""simple docstring"""

										snake_case        =     tempfile.mkdtemp()

				@classmethod
				def 						snake_case	(			cls      ):






										"""simple docstring"""

										if os.path.exists(cls.tmpdir      ):
																shutil.rmtree(cls.tmpdir      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										if self.clear_on_setup:
																for path in Path(self.tmpdir      ).glob('**/*'      ):
																						if path.is_file():
																												path.unlink()
																						elif path.is_dir():
																												shutil.rmtree(lowerCAmelCase      )





class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										super().tearDown()
										# Reset the state of the AcceleratorState singleton.
										AcceleratorState._reset_state()
										PartialState._reset_state()





class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     mocks if isinstance(lowerCAmelCase						,      (tuple, list)      ) else [mocks]
										for m in self.mocks:
																m.start()
																self.addCleanup(m.stop      )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int   )   ->			Any:





						"""simple docstring"""






						snake_case        =     AcceleratorState()
						snake_case        =     tensor[None].clone().to(state.device   )
						snake_case        =     gather(_UpperCamelCase   ).cpu()
						snake_case        =     tensor[0].cpu()
						for i in range(tensors.shape[0]   ):
												if not torch.equal(tensors[i]	,     _UpperCamelCase   ):
																		return False
						return True



class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     returncode
										snake_case        =     stdout
										snake_case        =     stderr





async def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Any   )   ->			List[Any]:





						"""simple docstring"""






						while True:
												snake_case        =     await stream.readline()
												if line:
																		callback(_UpperCamelCase   )
												else:
																		break





async def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Optional[Any]=None	,     _UpperCamelCase    :			List[Any]=None	,     _UpperCamelCase    :			Optional[Any]=None	,     _UpperCamelCase    :			List[str]=False	,     _UpperCamelCase    :			Optional[int]=False   )   ->			_RunOutput:





						"""simple docstring"""






						if echo:
												print('\nRunning: '	,     ' '.join(_UpperCamelCase   )   )

						snake_case        =     await asyncio.create_subprocess_exec(
						    cmd[0]	,     *cmd[1:]	,     stdin=_UpperCamelCase	,     stdout=asyncio.subprocess.PIPE	,     stderr=asyncio.subprocess.PIPE	,     env=_UpperCamelCase	,     )

						# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
						# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
						#
						# If it starts hanging, will need to switch to the following code. The problem is that no data
						# will be seen until it's done and if it hangs for example there will be no debug info.
						# out, err = await p.communicate()
						# return _RunOutput(p.returncode, out, err)

						snake_case        =     []
						snake_case        =     []

						def tee(_UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			List[str]=""   ):
												snake_case        =     line.decode('utf-8'   ).rstrip()
												sink.append(_UpperCamelCase   )
												if not quiet:
																		print(_UpperCamelCase	,     _UpperCamelCase	,     file=_UpperCamelCase   )

    # XXX: the timeout doesn't seem to make any difference here
						await asyncio.wait(
						    [
						        asyncio.create_task(_read_stream(p.stdout	,     lambda _UpperCamelCase   : tee(_UpperCamelCase	,     _UpperCamelCase	,     sys.stdout	,     label='stdout:'   )   )   ),
						        asyncio.create_task(_read_stream(p.stderr	,     lambda _UpperCamelCase   : tee(_UpperCamelCase	,     _UpperCamelCase	,     sys.stderr	,     label='stderr:'   )   )   ),
						    ]	,     timeout=_UpperCamelCase	,     )
						return _RunOutput(await p.wait()	,     _UpperCamelCase	,     _UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			str=None	,     _UpperCamelCase    :			List[str]=None	,     _UpperCamelCase    :			Tuple=1_8_0	,     _UpperCamelCase    :			Dict=False	,     _UpperCamelCase    :			Optional[Any]=True   )   ->			_RunOutput:





						"""simple docstring"""






						snake_case        =     asyncio.get_event_loop()
						snake_case        =     loop.run_until_complete(
						    _stream_subprocess(_UpperCamelCase	,     env=_UpperCamelCase	,     stdin=_UpperCamelCase	,     timeout=_UpperCamelCase	,     quiet=_UpperCamelCase	,     echo=_UpperCamelCase   )   )

						snake_case        =     ' '.join(_UpperCamelCase   )
						if result.returncode > 0:
												snake_case        =     '\n'.join(result.stderr   )
												raise RuntimeError(
												    f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
												    f"""The combined stderr from workers follows:\n{stderr}"""   )

						return result



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Optional[Any]=False   )   ->			Optional[Any]:





						"""simple docstring"""






						try:
												snake_case        =     subprocess.check_output(_UpperCamelCase	,     stderr=subprocess.STDOUT   )
												if return_stdout:
																		if hasattr(_UpperCamelCase	,     'decode'   ):
																								snake_case        =     output.decode('utf-8'   )
																		return output
						except subprocess.CalledProcessError as e:
												raise SubprocessCallException(
												    f"""Command `{" ".join(_UpperCamelCase   )}` failed with the following error:\n\n{e.output.decode()}"""   ) from e


