



"""simple docstring"""


import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
				from comet_ml import OfflineExperiment

if is_tensorboard_available():
				import struct

				import tensorboard.compat.proto.event_pba as event_pba

snake_case_								=			logging.getLogger(__name__)







@require_tensorboard
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     int:
												UpperCAmelCase			 =					'test_project_with_config'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																		accelerator.init_trackers(lowercase_  ,    lowercase_   )
																		accelerator.end_training()
																		for child in Path(f"""{dirpath}/{project_name}"""   ).glob('*/**'   ):
																								UpperCAmelCase			 =					list(filter(lambda lowercase_   : x.is_file()  ,    child.iterdir()   )   )[0]
																		self.assertNotEqual(str(lowercase_   )  ,    ''   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Tuple:
												UpperCAmelCase			 =					'test_project_with_log'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		accelerator.init_trackers(lowercase_   )
																		UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																		# Names are randomly generated each time
																		UpperCAmelCase			 =					list(filter(lambda lowercase_   : x.is_file()  ,    Path(f"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
																		self.assertNotEqual(str(lowercase_   )  ,    ''   )






						def       UpperCAmelCase__	(   self					:Any   )							->     List[Any]:
												UpperCAmelCase			 =					'test_project_with_log'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		accelerator.init_trackers(lowercase_   )
																		UpperCAmelCase			 =					{'tensor': torch.tensor(1   )}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																		# Names are randomly generated each time
																		UpperCAmelCase			 =					list(filter(lambda lowercase_   : x.is_file()  ,    Path(f"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
																		# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
																		with open(lowercase_  ,    'rb'   ) as f:
																								UpperCAmelCase			 =					f.read()
																		UpperCAmelCase			 =					False
																		while data:
																								UpperCAmelCase			 =					struct.unpack('Q'  ,    data[:8]   )

																								UpperCAmelCase			 =					data[12 : 12 + int(header[0]   )]  # 8+4
																								UpperCAmelCase			 =					data[12 + int(header[0]   ) + 4 :]
																								UpperCAmelCase			 =					event_pba.Event()

																								event.ParseFromString(lowercase_   )
																								if event.HasField('summary'   ):
																														for value in event.summary.value:
																																				if value.simple_value == 1.0 and value.tag == "tensor":
																																										UpperCAmelCase			 =					True
																		self.assertTrue(lowercase_  ,    'Converted tensor was not found in the log file!'   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     int:
												with self.assertRaisesRegex(lowercase_  ,    'Logging with `tensorboard` requires a `logging_dir`'   ):
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'   )
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												UpperCAmelCase			 =					ProjectConfiguration(total_limit=30   )
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_  ,    project_config=lowercase_   )







@require_wandb
@mock.patch.dict(os.environ      ,  {"""WANDB_MODE""": """offline"""}							)
class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Dict   )							->     Optional[int]:
												super().setUp()
												# wandb let's us override where logs are stored to via the WANDB_DIR env var
												self.add_mocks(mock.patch.dict(os.environ  ,    {'WANDB_DIR': self.tmpdir}   )   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str  ,    lowercase_					:str  ,    lowercase_					:bool = True   )							->     Dict:
												UpperCAmelCase			 =					Rf"""{section} ([\S\s]*?)\n\n"""
												if record:
																		UpperCAmelCase			 =					Rf"""Record: {pattern}"""
												UpperCAmelCase			 =					re.findall(lowercase_  ,    lowercase_   )[0]
												# A config
												if section == "config" or section == "history":
																		UpperCAmelCase			 =					re.findall(R'"([a-zA-Z0-9_.,]+)'  ,    lowercase_   )
																		return {key: val for key, val in zip(cleaned_record[0::2]  ,    cleaned_record[1::2]   )}
												# Everything else
												else:
																		return dict(re.findall(R'(\w+): "([^\s]+)"'  ,    lowercase_   )   )






						@skip
						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Any:
												UpperCAmelCase			 =					'test_project_with_config'
												UpperCAmelCase			 =					Accelerator(log_with='wandb'   )
												UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
												UpperCAmelCase			 =					{'wandb': {'tags': ['my_tag']}}
												accelerator.init_trackers(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
												accelerator.log(lowercase_  ,    step=0   )
												accelerator.end_training()
												# The latest offline log is stored at wandb/latest-run/*.wandb
												for child in Path(f"""{self.tmpdir}/wandb/latest-run"""   ).glob('*'   ):
																		if child.is_file() and child.suffix == ".wandb":
																								UpperCAmelCase			 =					subprocess.check_output(
																								    ['wandb', 'sync', '--view', '--verbose', str(lowercase_   )]  ,    env=os.environ.copy()   ).decode('utf8'  ,    'ignore'   )
																								break

        # Check HPS through careful parsing and cleaning
												UpperCAmelCase			 =					self.parse_log(lowercase_  ,    'config'   )
												self.assertEqual(logged_items['num_iterations']  ,    '12'   )
												self.assertEqual(logged_items['learning_rate']  ,    '0.01'   )
												self.assertEqual(logged_items['some_boolean']  ,    'false'   )
												self.assertEqual(logged_items['some_string']  ,    'some_value'   )
												self.assertEqual(logged_items['some_string']  ,    'some_value'   )

												# Run tags
												UpperCAmelCase			 =					self.parse_log(lowercase_  ,    'run'  ,    lowercase_   )
												self.assertEqual(logged_items['tags']  ,    'my_tag'   )

												# Actual logging
												UpperCAmelCase			 =					self.parse_log(lowercase_  ,    'history'   )
												self.assertEqual(logged_items['total_loss']  ,    '0.1'   )
												self.assertEqual(logged_items['iteration']  ,    '1'   )
												self.assertEqual(logged_items['my_text']  ,    'some_value'   )
												self.assertEqual(logged_items['_step']  ,    '0'   )


def 					_lowerCAmelCase (  self						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					run_name
						UpperCAmelCase			 =					OfflineExperiment(project_name=lowercase_						,      offline_directory=lowercase_		)
						logger.info(F"""Initialized offline CometML project {self.run_name}"""		)
						logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'		)







@require_comet_ml
@mock.patch.object(SCREAMING_SNAKE_CASE_      ,  """__init__"""      ,  SCREAMING_SNAKE_CASE_							)
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:bool = False   )							->     int:
												for log in log_list:
																		UpperCAmelCase			 =					json.loads(lowercase_   )['payload']
																		if is_param and "param" in j.keys():
																								if j["param"]["paramName"] == key:
																														return j["param"]["paramValue"]
																		if "log_other" in j.keys():
																								if j["log_other"]["key"] == key:
																														return j["log_other"]["val"]
																		if "metric" in j.keys():
																								if j["metric"]["metricName"] == key:
																														return j["metric"]["metricValue"]






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Tuple:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					CometMLTracker('test_project_with_config'  ,    lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																		accelerator.init_trackers(lowercase_  ,    lowercase_   )
																		accelerator.end_training()
																		UpperCAmelCase			 =					os.listdir(lowercase_   )[0]  # Comet is nice, it's just a zip file here
																		# We parse the raw logs
																		UpperCAmelCase			 =					os.path.join(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					zipfile.ZipFile(lowercase_  ,    'r'   )
																		UpperCAmelCase			 =					archive.open('messages.json'   ).read().decode('utf-8'   )
												UpperCAmelCase			 =					log.split('\n'   )[:-1]
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'num_iterations'  ,    lowercase_   )  ,    12   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'learning_rate'  ,    lowercase_   )  ,    0.01   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'some_boolean'  ,    lowercase_   )  ,    lowercase_   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'some_string'  ,    lowercase_   )  ,    'some_value'   )






						def       UpperCAmelCase__	(   self					:str   )							->     Optional[int]:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					CometMLTracker('test_project_with_config'  ,    lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		accelerator.init_trackers(lowercase_   )
																		UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		UpperCAmelCase			 =					os.listdir(lowercase_   )[0]  # Comet is nice, it's just a zip file here
																		# We parse the raw logs
																		UpperCAmelCase			 =					os.path.join(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					zipfile.ZipFile(lowercase_  ,    'r'   )
																		UpperCAmelCase			 =					archive.open('messages.json'   ).read().decode('utf-8'   )
												UpperCAmelCase			 =					log.split('\n'   )[:-1]
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'curr_step'  ,    lowercase_   )  ,    0   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'total_loss'   )  ,    0.1   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'iteration'   )  ,    1   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'my_text'   )  ,    'some_value'   )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				[
						    """total_loss""",
						    """iteration""",
						    """my_text""",
						    """learning_rate""",
						    """num_iterations""",
						    """some_boolean""",
						    """some_string""",
						]

						__UpperCamelCase													=				"""my_custom_tracker"""
						__UpperCamelCase													=				False






						def __init__(   self					:Optional[int]  ,    lowercase_					:str   )							->     Optional[int]:
												UpperCAmelCase			 =					open(f"""{dir}/log.csv"""  ,    'w+'   )
												UpperCAmelCase			 =					csv.DictWriter(self.f  ,    fieldnames=self._col_names   )
												self.writer.writeheader()






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[Any]:
												return self.writer






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:dict   )							->     Optional[int]:
												logger.info('Call init'   )
												self.writer.writerow(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:dict  ,    lowercase_					:Optional[int]   )							->     Optional[int]:
												logger.info('Call log'   )
												self.writer.writerow(lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[int]:
												self.f.close()







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					MyCustomTracker(lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																		accelerator.init_trackers('Some name'  ,    lowercase_   )
																		accelerator.end_training()
																		with open(f"""{d}/log.csv"""  ,    'r'   ) as f:
																								UpperCAmelCase			 =					csv.DictReader(lowercase_   )
																								UpperCAmelCase			 =					next(lowercase_   )
																								UpperCAmelCase			 =					{
																								    'total_loss': '',
																								    'iteration': '',
																								    'my_text': '',
																								    'learning_rate': '0.01',
																								    'num_iterations': '12',
																								    'some_boolean': 'False',
																								    'some_string': 'some_value',
																								}
																								self.assertDictEqual(lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Union[str, Any]:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					MyCustomTracker(lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		accelerator.init_trackers('Some name'   )
																		UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		with open(f"""{d}/log.csv"""  ,    'r'   ) as f:
																								UpperCAmelCase			 =					csv.DictReader(lowercase_   )
																								UpperCAmelCase			 =					next(lowercase_   )
																								UpperCAmelCase			 =					{
																								    'total_loss': '0.1',
																								    'iteration': '1',
																								    'my_text': 'some_value',
																								    'learning_rate': '',
																								    'num_iterations': '',
																								    'some_boolean': '',
																								    'some_string': '',
																								}
																								self.assertDictEqual(lowercase_  ,    lowercase_   )








'''simple docstring'''


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

import numpy as np
import torch

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


lowerCAmelCase__      				=			logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor
							_lowerCamelCase		:   Optional[torch.FloatTensor]= None







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=0.999 ,UpperCamelCase="cosine" ,)     ->     List[str]:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(UpperCamelCase	):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(UpperCamelCase	):
			return math.exp(t * -12.0	)

	else:
		raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

	UpperCAmelCase_		:   Optional[int]													=       []
	for i in range(UpperCamelCase	):
		UpperCAmelCase_		:   Optional[Any]													=       i / num_diffusion_timesteps
		UpperCAmelCase_		:   Any													=       (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(UpperCamelCase	) / alpha_bar_fn(UpperCamelCase	) ,UpperCamelCase	)	)
	return torch.tensor(UpperCamelCase ,dtype=torch.floataa	)







class        lowercase    (	a_,       a_			):
							_lowerCamelCase		:   List[str]= [e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase		:   Optional[int]= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 1000     ,      _snake_case = 0.0_001     ,      _snake_case = 0.02     ,      _snake_case = "linear"     ,      _snake_case = None     ,      _snake_case = "epsilon"     ,      _snake_case = "linspace"     ,      _snake_case = 0     ,      )				->					List[str]:
								if trained_betas is not None:
									UpperCAmelCase_		:   List[str]													=       torch.tensor(_snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "linear":
									UpperCAmelCase_		:   List[Any]													=       torch.linspace(_snake_case     ,      _snake_case     ,      _snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									UpperCAmelCase_		:   List[Any]													=       (
									    torch.linspace(beta_start**0.5     ,      beta_end**0.5     ,      _snake_case     ,      dtype=torch.floataa) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									UpperCAmelCase_		:   int													=       betas_for_alpha_bar(_snake_case)
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

								UpperCAmelCase_		:   List[Any]													=       1.0 - self.betas
								UpperCAmelCase_		:   Union[str, Any]													=       torch.cumprod(self.alphas     ,      dim=0)

								UpperCAmelCase_		:   Union[str, Any]													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   int													=       np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case)

								# setable values
								UpperCAmelCase_		:   List[str]													=       None
								UpperCAmelCase_		:   Union[str, Any]													=       np.linspace(0     ,      num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[::-1].copy()
								UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       False


							@property
							def     _snake_case				(      self)				->					Optional[Any]:
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   List[Any]													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   int													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[int]													=       num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
									UpperCAmelCase_		:   List[Any]													=       np.linspace(0     ,      self.config.num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									UpperCAmelCase_		:   Any													=       self.config.num_train_timesteps // self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									UpperCAmelCase_		:   List[Any]													=       (np.arange(0     ,      _snake_case) * step_ratio).round()[::-1].copy().astype(_snake_case)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									UpperCAmelCase_		:   List[Any]													=       self.config.num_train_timesteps / self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									UpperCAmelCase_		:   Any													=       (np.arange(self.config.num_train_timesteps     ,      0     ,      -step_ratio)).round().copy().astype(_snake_case)
									timesteps -= 1
								else:
									raise ValueError(
									    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

								UpperCAmelCase_		:   Any													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   Dict													=       np.interp(_snake_case     ,      np.arange(0     ,      len(_snake_case))     ,      _snake_case)
								UpperCAmelCase_		:   Optional[int]													=       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case).to(device=_snake_case)
								if str(_snake_case).startswith('mps'):
									# mps does not support float64
									UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case).to(_snake_case     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       torch.from_numpy(_snake_case).to(device=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

								if (
								    isinstance(_snake_case     ,      _snake_case)
								    or isinstance(_snake_case     ,      torch.IntTensor)
								    or isinstance(_snake_case     ,      torch.LongTensor)
								):
									raise ValueError(
									    (
									        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
									        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
									        ' one of the `scheduler.timesteps` as a timestep.'
									    )     ,      )

								if not self.is_scale_input_called:
									logger.warning(
									    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
									    'See `StableDiffusionPipeline` for a usage example.')

								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   Optional[int]													=       timestep.to(self.timesteps.device)

								UpperCAmelCase_		:   List[Any]													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   int													=       self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									UpperCAmelCase_		:   Tuple													=       sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									UpperCAmelCase_		:   str													=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
									raise NotImplementedError('prediction_type not implemented yet: sample')
								else:
									raise ValueError(
									    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

								UpperCAmelCase_		:   Dict													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index + 1]
								UpperCAmelCase_		:   str													=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								UpperCAmelCase_		:   Any													=       (sigma_to**2 - sigma_up**2) ** 0.5

								# 2. Convert to an ODE derivative
								UpperCAmelCase_		:   Dict													=       (sample - pred_original_sample) / sigma

								UpperCAmelCase_		:   Tuple													=       sigma_down - sigma

								UpperCAmelCase_		:   List[str]													=       sample + derivative * dt

								UpperCAmelCase_		:   Any													=       model_output.device
								UpperCAmelCase_		:   List[Any]													=       randn_tensor(model_output.shape     ,      dtype=model_output.dtype     ,      device=_snake_case     ,      generator=_snake_case)

								UpperCAmelCase_		:   Union[str, Any]													=       prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=_snake_case     ,      pred_original_sample=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase_		:   List[str]													=       self.sigmas.to(device=original_samples.device     ,      dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case):
									# mps does not support float64
									UpperCAmelCase_		:   str													=       self.timesteps.to(original_samples.device     ,      dtype=torch.floataa)
									UpperCAmelCase_		:   List[Any]													=       timesteps.to(original_samples.device     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       self.timesteps.to(original_samples.device)
									UpperCAmelCase_		:   str													=       timesteps.to(original_samples.device)

								UpperCAmelCase_		:   Optional[Any]													=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								UpperCAmelCase_		:   Any													=       sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									UpperCAmelCase_		:   Union[str, Any]													=       sigma.unsqueeze(-1)

								UpperCAmelCase_		:   int													=       original_samples + noise * sigma
								return noisy_samples





							def __len__(      self)				->					Optional[Any]:
								return self.config.num_train_timesteps






