




"""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.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
			import botoa  # noqa: F401







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  Union[str, Any]								=       botoa.client('''iam'''	)

			__lowercase			:  Union[str, Any]								=       {
			    '''Version''': '''2012-10-17''',
			    '''Statement''': [
			        {'''Effect''': '''Allow''', '''Principal''': {'''Service''': '''sagemaker.amazonaws.com'''}, '''Action''': '''sts:AssumeRole'''}
			    ],
			}
			try:
						# create the role, associated with the chosen trust policy
						iam_client.create_role(
						    RoleName=__UpperCamelCase	,				AssumeRolePolicyDocument=json.dumps(__UpperCamelCase	,				indent=2	)	)
						__lowercase			:  Union[str, Any]								=       {
						    '''Version''': '''2012-10-17''',
						    '''Statement''': [
						        {
						            '''Effect''': '''Allow''',
						            '''Action''': [
						                '''sagemaker:*''',
						                '''ecr:GetDownloadUrlForLayer''',
						                '''ecr:BatchGetImage''',
						                '''ecr:BatchCheckLayerAvailability''',
						                '''ecr:GetAuthorizationToken''',
						                '''cloudwatch:PutMetricData''',
						                '''cloudwatch:GetMetricData''',
						                '''cloudwatch:GetMetricStatistics''',
						                '''cloudwatch:ListMetrics''',
						                '''logs:CreateLogGroup''',
						                '''logs:CreateLogStream''',
						                '''logs:DescribeLogStreams''',
						                '''logs:PutLogEvents''',
						                '''logs:GetLogEvents''',
						                '''s3:CreateBucket''',
						                '''s3:ListBucket''',
						                '''s3:GetBucketLocation''',
						                '''s3:GetObject''',
						                '''s3:PutObject''',
						            ],
						            '''Resource''': '''*''',
						        }
						    ],
						}
						# attach policy to role
						iam_client.put_role_policy(
						    RoleName=__UpperCamelCase	,				PolicyName=f"""{role_name}_policy_permission"""	,				PolicyDocument=json.dumps(__UpperCamelCase	,				indent=2	)	,				)
			except iam_client.exceptions.EntityAlreadyExistsException:
						print(f"""role {role_name} already exists. Using existing one"""	)







def      __UpperCAmelCase     ( __UpperCamelCase	):
			__lowercase			:  str								=       botoa.client('''iam'''	)
			return iam_client.get_role(RoleName=__UpperCamelCase	)["Role"]["Arn"]







def      __UpperCAmelCase     ( ):
			__lowercase			:  int								=       _ask_options(
			    '''How do you want to authorize?'''	,				['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ''']	,				__UpperCamelCase	,				)
			__lowercase			:  Tuple								=       None
			if credentials_configuration == 0:
						__lowercase			:  List[Any]								=       _ask_field('''Enter your AWS Profile name: [default] '''	,				default='''default'''	)
						__lowercase			:  Any								=       aws_profile
			else:
						print(
						    '''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
						    '''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`'''	)
						__lowercase			:  int								=       _ask_field('''AWS Access Key ID: '''	)
						__lowercase			:  Any								=       aws_access_key_id

						__lowercase			:  int								=       _ask_field('''AWS Secret Access Key: '''	)
						__lowercase			:  List[str]								=       aws_secret_access_key

			__lowercase			:  Union[str, Any]								=       _ask_field('''Enter your AWS Region: [us-east-1]'''	,				default='''us-east-1'''	)
			__lowercase			:  str								=       aws_region

			__lowercase			:  List[Any]								=       _ask_options(
			    '''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?'''	,				['''Provide IAM Role name''', '''Create new IAM role using credentials''']	,				__UpperCamelCase	,				)
			if role_management == 0:
						__lowercase			:  List[Any]								=       _ask_field('''Enter your IAM role name: '''	)
			else:
						__lowercase			:  Dict								=       '''accelerate_sagemaker_execution_role'''
						print(f"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"""	)
						_create_iam_role_for_sagemaker(__UpperCamelCase	)

			__lowercase			:  str								=       _ask_field(
			    '''Do you want to use custom Docker image? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  List[Any]								=       None
			if is_custom_docker_image:
						__lowercase			:  Dict								=       _ask_field('''Enter your Docker image: '''	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	)

			__lowercase			:  Optional[int]								=       _ask_field(
			    '''Do you want to provide SageMaker input channels with data locations? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  str								=       None
			if is_sagemaker_inputs_enabled:
						__lowercase			:  Any								=       _ask_field(
						    '''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '''	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	,				)

			__lowercase			:  List[str]								=       _ask_field(
			    '''Do you want to enable SageMaker metrics? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  Optional[int]								=       None
			if is_sagemaker_metrics_enabled:
						__lowercase			:  Dict								=       _ask_field(
						    '''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '''	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	,				)

			__lowercase			:  int								=       _ask_options(
			    '''What is the distributed mode?'''	,				['''No distributed training''', '''Data parallelism''']	,				_convert_sagemaker_distributed_mode	,				)
			__lowercase			:  Optional[int]								=       {}
			__lowercase			:  Dict								=       _ask_field(
			    '''Do you wish to optimize your script with torch dynamo?[yes/NO]:'''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			if use_dynamo:
						__lowercase			:  Tuple								=       '''dynamo_'''
						__lowercase			:  Dict								=       _ask_options(
						    '''Which dynamo backend would you like to use?'''	,				[x.lower() for x in DYNAMO_BACKENDS]	,				_convert_dynamo_backend	,				default=2	,				)
						__lowercase			:  List[Any]								=       _ask_field(
						    '''Do you want to customize the defaults sent to torch.compile? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)

						if use_custom_options:
									__lowercase			:  Optional[int]								=       _ask_options(
									    '''Which mode do you want to use?'''	,				__UpperCamelCase	,				lambda __UpperCamelCase	: TORCH_DYNAMO_MODES[int(__UpperCamelCase	)]	,				default='''default'''	,				)
									__lowercase			:  str								=       _ask_field(
									    '''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
									__lowercase			:  Union[str, Any]								=       _ask_field(
									    '''Do you want to enable dynamic shape tracing? [yes/NO]: '''	,				_convert_yes_no_to_bool	,				default=__UpperCamelCase	,				error_message='''Please enter yes or no.'''	,				)
			__lowercase			:  str								=       '''Which EC2 instance type you want to use for your training?'''
			if distributed_type != SageMakerDistributedType.NO:
						__lowercase			:  Union[str, Any]								=       _ask_options(
						    __UpperCamelCase	,				__UpperCamelCase	,				lambda __UpperCamelCase	: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__UpperCamelCase	)]	)
			else:
						eca_instance_query += "? [ml.p3.2xlarge]:"
						__lowercase			:  Tuple								=       _ask_field(__UpperCamelCase	,				lambda __UpperCamelCase	: str(__UpperCamelCase	).lower()	,				default='''ml.p3.2xlarge'''	)

			__lowercase			:  int								=       1
			if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
						__lowercase			:  Optional[Any]								=       _ask_field(
						    '''How many machines do you want use? [1]: '''	,				__UpperCamelCase	,				default=1	,				)

			__lowercase			:  List[Any]								=       _ask_options(
			    '''Do you wish to use FP16 or BF16 (mixed precision)?'''	,				['''no''', '''fp16''', '''bf16''', '''fp8''']	,				_convert_mixed_precision	,				)

			if use_dynamo and mixed_precision == "no":
						print(
						    '''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.'''	)

			return SageMakerConfig(
			    image_uri=__UpperCamelCase	,				compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER	,				distributed_type=__UpperCamelCase	,				use_cpu=__UpperCamelCase	,				dynamo_config=__UpperCamelCase	,				eca_instance_type=__UpperCamelCase	,				profile=__UpperCamelCase	,				region=__UpperCamelCase	,				iam_role_name=__UpperCamelCase	,				mixed_precision=__UpperCamelCase	,				num_machines=__UpperCamelCase	,				sagemaker_inputs_file=__UpperCamelCase	,				sagemaker_metrics_file=__UpperCamelCase	,				)





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Tuple       						=(DEISMultistepScheduler,)
 SCREAMING_SNAKE_CASE_				:					List[str]       						=(("num_inference_steps", 25),)

 def      __lowerCAmelCase  (						self			:			Tuple					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**SCREAMING_SNAKE_CASE__   )
      return config

 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			Dict=0					, **SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           # copy over dummy past residuals
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase    ,		UpperCamelCase						    =					sample, sample
           for t in range(SCREAMING_SNAKE_CASE__					, time_step + scheduler.config.solver_order + 1   ):
                UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
                UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

                assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0					, **SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )
      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.dummy_sample
      UpperCamelCase						    =					0.1 * sample
      UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )
           scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__   )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

                # copy over dummy past residual (must be after setting timesteps)
                UpperCamelCase						    =					dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					new_scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      if scheduler is None:
           UpperCamelCase						    =					self.scheduler_classes[0]
           UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      return sample

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					dict(self.forward_default_kwargs   )

      UpperCamelCase						    =					kwargs.pop('num_inference_steps'					, SCREAMING_SNAKE_CASE__   )

      for scheduler_class in self.scheduler_classes:
           UpperCamelCase						    =					self.get_scheduler_config()
           UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

           UpperCamelCase						    =					self.dummy_sample
           UpperCamelCase						    =					0.1 * sample

           if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
           elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__					, 'set_timesteps'   ):
                UpperCamelCase						    =					num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase						    =					[residual + 0.2, residual + 0.15, residual + 0.10]
           UpperCamelCase						    =					dummy_past_residuals[: scheduler.config.solver_order]

           UpperCamelCase						    =					scheduler.timesteps[5]
           UpperCamelCase						    =					scheduler.timesteps[6]

           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   ).prev_sample

           self.assertEqual(output_a.shape					, sample.shape   )
           self.assertEqual(output_a.shape					, output_a.shape   )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					DEISMultistepScheduler(**self.get_scheduler_config()   )
      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

      UpperCamelCase						    =					DPMSolverSinglestepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DPMSolverMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					UniPCMultistepScheduler.from_config(scheduler.config   )
      UpperCamelCase						    =					DEISMultistepScheduler.from_config(scheduler.config   )

      UpperCamelCase						    =					self.full_loop(scheduler=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__   )
      for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
                for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, sample_max_value=SCREAMING_SNAKE_CASE__					, algorithm_type='deis'					, solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
                for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          UpperCamelCase						    =					self.full_loop(
                              solver_order=SCREAMING_SNAKE_CASE__					, solver_type=SCREAMING_SNAKE_CASE__					, prediction_type=SCREAMING_SNAKE_CASE__					, algorithm_type=SCREAMING_SNAKE_CASE__					, )
                          assert not torch.isnan(SCREAMING_SNAKE_CASE__   ).any(), "Samples have nan numbers"

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )
      self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
           self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__					, time_step=0   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop()
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.23916   ) < 1e-3

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.full_loop(prediction_type='v_prediction'   )
      UpperCamelCase						    =					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__   )   )

      assert abs(result_mean.item() - 0.091   ) < 1e-3






 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.scheduler_classes[0]
      UpperCamelCase						    =					self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__					, dynamic_thresholding_ratio=0   )
      UpperCamelCase						    =					scheduler_class(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.dummy_model()
      UpperCamelCase						    =					self.dummy_sample_deter.half()
      scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

      for i, t in enumerate(scheduler.timesteps   ):
           UpperCamelCase						    =					model(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					scheduler.step(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   ).prev_sample

      assert sample.dtype == torch.floataa


