





"""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

a__					:     Union[str, Any]							=				logging.getLogger(__name__)



@require_tensorboard
class      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_config"
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
         accelerator.end_training()
         for child in Path(F"{dirpath}/{project_name}"     ).glob("*/**"     ):
          snake_case__    							=				list(filter(lambda __SCREAMING_SNAKE_CASE     : x.is_file()   ,					child.iterdir()     )     )[0]
         self.assertNotEqual(str(__SCREAMING_SNAKE_CASE     )   ,					""     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_log"
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
         accelerator.log(__SCREAMING_SNAKE_CASE   ,					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
         snake_case__    							=				list(filter(lambda __SCREAMING_SNAKE_CASE     : x.is_file()   ,					Path(F"{dirpath}/{project_name}"     ).iterdir()     )     )[0]
         self.assertNotEqual(str(__SCREAMING_SNAKE_CASE     )   ,					""     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_log"
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )
         accelerator.init_trackers(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"tensor": torch.tensor(1     )}
         accelerator.log(__SCREAMING_SNAKE_CASE   ,					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
         snake_case__    							=				list(filter(lambda __SCREAMING_SNAKE_CASE     : 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(__SCREAMING_SNAKE_CASE   ,					"rb"     ) as f:
          snake_case__    							=				f.read()
         snake_case__    							=				False
         while data:
          snake_case__    							=				struct.unpack("Q"   ,					data[:8]     )

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

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with self.assertRaisesRegex(__SCREAMING_SNAKE_CASE   ,					"Logging with `tensorboard` requires a `logging_dir`"     ):
         snake_case__    							=				Accelerator(log_with="tensorboard"     )
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				ProjectConfiguration(total_limit=30     )
        with tempfile.TemporaryDirectory() as dirpath:
         snake_case__    							=				Accelerator(log_with="tensorboard"   ,					project_dir=__SCREAMING_SNAKE_CASE   ,					project_config=__SCREAMING_SNAKE_CASE     )



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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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 		_SCREAMING_SNAKE_CASE(			__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = True     ):






        '''simple docstring'''

        snake_case__    							=				RF"{section} ([\S\s]*?)\n\n"
        if record:
         snake_case__    							=				RF"Record: {pattern}"
        snake_case__    							=				re.findall(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )[0]
        # A config
        if section == "config" or section == "history":
         snake_case__    							=				re.findall(R"\"([a-zA-Z0-9_.,]+)"   ,					__SCREAMING_SNAKE_CASE     )
         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]+)\""   ,					__SCREAMING_SNAKE_CASE     )     )

       @skip
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				"test_project_with_config"
        snake_case__    							=				Accelerator(log_with="wandb"     )
        snake_case__    							=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
        snake_case__    							=				{"wandb": {"tags": ["my_tag"]}}
        accelerator.init_trackers(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
        accelerator.log(__SCREAMING_SNAKE_CASE   ,					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":
          snake_case__    							=				subprocess.check_output(
              ["wandb", "sync", "--view", "--verbose", str(__SCREAMING_SNAKE_CASE     )]   ,					env=os.environ.copy()     ).decode("utf8"   ,					"ignore"     )
          break

        # Check HPS through careful parsing and cleaning
        snake_case__    							=				self.parse_log(__SCREAMING_SNAKE_CASE   ,					"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
        snake_case__    							=				self.parse_log(__SCREAMING_SNAKE_CASE   ,					"run"   ,					__SCREAMING_SNAKE_CASE     )
        self.assertEqual(logged_items["tags"]   ,					"my_tag"     )

        # Actual logging
        snake_case__    							=				self.parse_log(__SCREAMING_SNAKE_CASE   ,					"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 							a__			(  self	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Union[str, Any]:

 '''simple docstring'''






 snake_case__    							=				run_name
 snake_case__    							=				OfflineExperiment(project_name=_UpperCAmelCase	,     offline_directory=_UpperCAmelCase	)
 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(__lowerCAmelCase				,"""__init__"""				,__lowerCAmelCase							)
class      lowercase_				(     unittest.TestCase							):

       @staticmethod
       def 		_SCREAMING_SNAKE_CASE(			__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = False     ):






        '''simple docstring'''

        for log in log_list:
         snake_case__    							=				json.loads(__SCREAMING_SNAKE_CASE     )["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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Dict											=     [
           """total_loss""",
           """iteration""",
           """my_text""",
           """learning_rate""",
           """num_iterations""",
           """some_boolean""",
           """some_string""",
       ]

       snake_case__				:							Optional[int]											=     """my_custom_tracker"""
       snake_case__				:							List[Any]											=     False

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				open(F"{dir}/log.csv"   ,					"w+"     )
        snake_case__    							=				csv.DictWriter(self.f   ,					fieldnames=self._col_names     )
        self.writer.writeheader()

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return self.writer

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        logger.info("Call init"     )
        self.writer.writerow(__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        logger.info("Call log"     )
        self.writer.writerow(__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        self.f.close()



class      lowercase_				(     unittest.TestCase							):

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with tempfile.TemporaryDirectory() as d:
         snake_case__    							=				MyCustomTracker(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				Accelerator(log_with=__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				{"num_iterations": 12, "learning_rate": 1e-2, "some_boolean": False, "some_string": "some_value"}
         accelerator.init_trackers("Some name"   ,					__SCREAMING_SNAKE_CASE     )
         accelerator.end_training()
         with open(F"{d}/log.csv"   ,					"r"     ) as f:
          snake_case__    							=				csv.DictReader(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				next(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				{
              "total_loss": "",
              "iteration": "",
              "my_text": "",
              "learning_rate": "0.01",
              "num_iterations": "12",
              "some_boolean": "False",
              "some_string": "some_value",
          }
          self.assertDictEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        with tempfile.TemporaryDirectory() as d:
         snake_case__    							=				MyCustomTracker(__SCREAMING_SNAKE_CASE     )
         snake_case__    							=				Accelerator(log_with=__SCREAMING_SNAKE_CASE     )
         accelerator.init_trackers("Some name"     )
         snake_case__    							=				{"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
         accelerator.log(__SCREAMING_SNAKE_CASE   ,					step=0     )
         accelerator.end_training()
         with open(F"{d}/log.csv"   ,					"r"     ) as f:
          snake_case__    							=				csv.DictReader(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				next(__SCREAMING_SNAKE_CASE     )
          snake_case__    							=				{
              "total_loss": "0.1",
              "iteration": "1",
              "my_text": "some_value",
              "learning_rate": "",
              "num_iterations": "",
              "some_boolean": "",
              "some_string": "",
          }
          self.assertDictEqual(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     )








"""simple docstring"""


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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class      lowercase_				:
       snake_case__				:							CommonSchedulerState

       # setable values
       snake_case__				:							jnp.ndarray
       snake_case__				:							jnp.ndarray
       snake_case__				:							jnp.ndarray
       snake_case__				:							Optional[int]											=     None

       # running values
       snake_case__				:							Optional[jnp.ndarray]											=     None

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(			cls   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return cls(common=__SCREAMING_SNAKE_CASE   ,					init_noise_sigma=__SCREAMING_SNAKE_CASE   ,					timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE     )



@dataclass
class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							LMSDiscreteSchedulerState



class      lowercase_				(     __lowerCAmelCase				,__lowerCAmelCase							):
       snake_case__				:							Any											=     [e.name for e in FlaxKarrasDiffusionSchedulers]

       snake_case__				:							jnp.dtype

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return True

       @register_to_config
       def __init__(			self   ,					__SCREAMING_SNAKE_CASE = 1000   ,					__SCREAMING_SNAKE_CASE = 0.0001   ,					__SCREAMING_SNAKE_CASE = 0.02   ,					__SCREAMING_SNAKE_CASE = "linear"   ,					__SCREAMING_SNAKE_CASE = None   ,					__SCREAMING_SNAKE_CASE = "epsilon"   ,					__SCREAMING_SNAKE_CASE = jnp.floataa   ,					):






        '''simple docstring'''

        snake_case__    							=				dtype

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE = None     ):






        '''simple docstring'''

        if common is None:
         snake_case__    							=				CommonSchedulerState.create(self     )

        snake_case__    							=				jnp.arange(0   ,					self.config.num_train_timesteps     ).round()[::-1]
        snake_case__    							=				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case__    							=				sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=__SCREAMING_SNAKE_CASE   ,					init_noise_sigma=__SCREAMING_SNAKE_CASE   ,					timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        ((snake_case__)							,   )    							=				jnp.where(state.timesteps == timestep   ,					size=1     )
        snake_case__    							=				step_index[0]

        snake_case__    							=				state.sigmas[step_index]
        snake_case__    							=				sample / ((sigma**2 + 1) ** 0.5)
        return sample

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        def lms_derivative(__SCREAMING_SNAKE_CASE     ):
         snake_case__    							=				1.0
         for k in range(__SCREAMING_SNAKE_CASE     ):
          if current_order == k:
           continue
          prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

        snake_case__    							=				integrate.quad(__SCREAMING_SNAKE_CASE   ,					state.sigmas[t]   ,					state.sigmas[t + 1]   ,					epsrel=1e-4     )[0]

        return integrated_coeff

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = ()     ):






        '''simple docstring'''

        snake_case__    							=				jnp.linspace(self.config.num_train_timesteps - 1   ,					0   ,					__SCREAMING_SNAKE_CASE   ,					dtype=self.dtype     )

        snake_case__    							=				jnp.floor(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )
        snake_case__    							=				jnp.ceil(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )

        snake_case__    							=				jnp.mod(__SCREAMING_SNAKE_CASE   ,					1.0     )

        snake_case__    							=				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case__    							=				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case__    							=				jnp.concatenate([sigmas, jnp.array([0.0]   ,					dtype=self.dtype     )]     )

        snake_case__    							=				timesteps.astype(jnp.intaa     )

        # initial running values
        snake_case__    							=				jnp.zeros((0,) + shape   ,					dtype=self.dtype     )

        return state.replace(
            timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					num_inference_steps=__SCREAMING_SNAKE_CASE   ,					derivatives=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = 4   ,					__SCREAMING_SNAKE_CASE = True   ,					):






        '''simple docstring'''

        if state.num_inference_steps is None:
         raise ValueError(
             "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"     )

        snake_case__    							=				state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         snake_case__    							=				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         snake_case__    							=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
         raise ValueError(
             F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"     )

        # 2. Convert to an ODE derivative
        snake_case__    							=				(sample - pred_original_sample) / sigma
        snake_case__    							=				state.replace(derivatives=jnp.append(state.derivatives   ,					__SCREAMING_SNAKE_CASE     )     )
        if len(state.derivatives     ) > order:
         snake_case__    							=				state.replace(derivatives=jnp.delete(state.derivatives   ,					0     )     )

        # 3. Compute linear multistep coefficients
        snake_case__    							=				min(timestep + 1   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ) for curr_order in range(__SCREAMING_SNAKE_CASE     )]

        # 4. Compute previous sample based on the derivatives path
        snake_case__    							=				sample + sum(
            coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE   ,					reversed(state.derivatives     )     )     )

        if not return_dict:
         return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE   ,					state=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        snake_case__    							=				state.sigmas[timesteps].flatten()
        snake_case__    							=				broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE   ,					noise.shape     )

        snake_case__    							=				original_samples + noise * sigma

        return noisy_samples

       def __len__(			self     ):






        '''simple docstring'''

        return self.config.num_train_timesteps

