"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Optional[Any]   =  (DDPMScheduler,)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					str:
             lowercase__			:		int       =	{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0_001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'variance_type': 'fixed_small',
                 'clip_sample': True,
             }

             config.update(**a							)
             return config


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]		,			[0.002, 0.02, 0.2, 2]							):
                    self.check_over_configs(beta_start=a		,			beta_end=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=a							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             for variance in ["fixed_small", "fixed_large", "other"]:
                    self.check_over_configs(variance_type=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             self.check_over_configs(thresholding=a							)
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=a		,			prediction_type=a		,			sample_max_value=a		,			)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(prediction_type=a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             for t in [0, 5_0_0, 9_9_9]:
                    self.check_over_forward(time_step=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	self.scheduler_classes[0]
             lowercase__			:		List[Any]       =	self.get_scheduler_config()
             lowercase__			:		Union[str, Any]       =	scheduler_class(**a							)

             assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.00_979							)							) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.02							)							) < 1e-5


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		Optional[Any]       =	self.scheduler_classes[0]
             lowercase__			:		Optional[Any]       =	self.get_scheduler_config()
             lowercase__			:		Union[str, Any]       =	scheduler_class(**a							)

             lowercase__			:		List[Any]       =	len(a							)

             lowercase__			:		List[Any]       =	self.dummy_model()
             lowercase__			:		str       =	self.dummy_sample_deter
             lowercase__			:		List[Any]       =	torch.manual_seed(0							)

             for t in reversed(range(a							)							):
                    # 1. predict noise residual
                    lowercase__			:		Dict       =	model(a		,			a							)

                    # 2. predict previous mean of sample x_t-1
                    lowercase__			:		Optional[int]       =	scheduler.step(a		,			a		,			a		,			generator=a							).prev_sample

                    # if t > 0:
                    #     noise = self.dummy_sample_deter
                    #     variance = scheduler.get_variance(t) ** (0.5) * noise
                    #
                    # sample = pred_prev_sample + variance
                    lowercase__			:		List[str]       =	pred_prev_sample

             lowercase__			:		Any       =	torch.sum(torch.abs(a							)							)
             lowercase__			:		str       =	torch.mean(torch.abs(a							)							)

             assert abs(result_sum.item() - 258.9_606							) < 1e-2
             assert abs(result_mean.item() - 0.3_372							) < 1e-3


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Any       =	self.scheduler_classes[0]
             lowercase__			:		Optional[int]       =	self.get_scheduler_config(prediction_type='v_prediction'							)
             lowercase__			:		Optional[Any]       =	scheduler_class(**a							)

             lowercase__			:		int       =	len(a							)

             lowercase__			:		Dict       =	self.dummy_model()
             lowercase__			:		Optional[int]       =	self.dummy_sample_deter
             lowercase__			:		List[Any]       =	torch.manual_seed(0							)

             for t in reversed(range(a							)							):
                    # 1. predict noise residual
                    lowercase__			:		Optional[Any]       =	model(a		,			a							)

                    # 2. predict previous mean of sample x_t-1
                    lowercase__			:		Optional[int]       =	scheduler.step(a		,			a		,			a		,			generator=a							).prev_sample

                    # if t > 0:
                    #     noise = self.dummy_sample_deter
                    #     variance = scheduler.get_variance(t) ** (0.5) * noise
                    #
                    # sample = pred_prev_sample + variance
                    lowercase__			:		List[Any]       =	pred_prev_sample

             lowercase__			:		Dict       =	torch.sum(torch.abs(a							)							)
             lowercase__			:		List[str]       =	torch.mean(torch.abs(a							)							)

             assert abs(result_sum.item() - 202.0_296							) < 1e-2
             assert abs(result_mean.item() - 0.2_631							) < 1e-3


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Optional[Any]       =	self.scheduler_classes[0]
             lowercase__			:		Any       =	self.get_scheduler_config()
             lowercase__			:		List[Any]       =	scheduler_class(**a							)

             lowercase__			:		Any       =	[1_0_0, 8_7, 5_0, 1, 0]

             scheduler.set_timesteps(timesteps=a							)

             lowercase__			:		List[Any]       =	scheduler.timesteps

             for i, timestep in enumerate(a							):
                    if i == len(a							) - 1:
                           lowercase__			:		Union[str, Any]       =	-1
                    else:
                           lowercase__			:		int       =	timesteps[i + 1]

                    lowercase__			:		Tuple       =	scheduler.previous_timestep(a							)
                    lowercase__			:		List[Any]       =	prev_t.item()

                    self.assertEqual(a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Union[str, Any]       =	self.scheduler_classes[0]
             lowercase__			:		List[str]       =	self.get_scheduler_config()
             lowercase__			:		Any       =	scheduler_class(**a							)

             lowercase__			:		Optional[Any]       =	[1_0_0, 8_7, 5_0, 5_1, 0]

             with self.assertRaises(a		,			msg='`custom_timesteps` must be in descending order.'							):
                    scheduler.set_timesteps(timesteps=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Tuple       =	self.scheduler_classes[0]
             lowercase__			:		int       =	self.get_scheduler_config()
             lowercase__			:		int       =	scheduler_class(**a							)

             lowercase__			:		Dict       =	[1_0_0, 8_7, 5_0, 1, 0]
             lowercase__			:		Dict       =	len(a							)

             with self.assertRaises(a		,			msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'							):
                    scheduler.set_timesteps(num_inference_steps=a		,			timesteps=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Union[str, Any]       =	self.scheduler_classes[0]
             lowercase__			:		str       =	self.get_scheduler_config()
             lowercase__			:		List[Any]       =	scheduler_class(**a							)

             lowercase__			:		Union[str, Any]       =	[scheduler.config.num_train_timesteps]

             with self.assertRaises(
                 a		,			msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'		,			):
                    scheduler.set_timesteps(timesteps=a							)






"""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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser

def       _snake_case						(						):
 UpperCAmelCase	:					List[str]	=	ArgumentParser("""Accelerate CLI tool"""			,		usage="""accelerate <command> [<args>]"""			,		allow_abbrev=UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	parser.add_subparsers(help="""accelerate command helpers"""						)

 # Register commands
 get_config_parser(subparsers=UpperCamelCase						)
 env_command_parser(subparsers=UpperCamelCase						)
 launch_command_parser(subparsers=UpperCamelCase						)
 tpu_command_parser(subparsers=UpperCamelCase						)
 test_command_parser(subparsers=UpperCamelCase						)

 # Let's go
 UpperCAmelCase	:					Optional[int]	=	parser.parse_args()

 if not hasattr(UpperCamelCase			,		"""func"""						):
  parser.print_help()
  exit(1						)

 # Run
 args.func(UpperCamelCase						)


if __name__ == "__main__":
      main()
