



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		(CMStochasticIterativeScheduler,)
							lowerCamelCase__   			=		10







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    {
														    "num_train_timesteps": 201,
														    "sigma_min": 0.002,
														    "sigma_max": 80.0,
														}

														config.update(**__a)
														return config







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    10

														_lowerCAmelCase     :							int    =    self.get_scheduler_config()
														_lowerCAmelCase     :							Optional[int]    =    self.scheduler_classes[0](**__a)

														scheduler.set_timesteps(__a)

														_lowerCAmelCase     :							str    =    scheduler.timesteps[0]
														_lowerCAmelCase     :							Optional[Any]    =    scheduler.timesteps[1]

														_lowerCAmelCase     :							Optional[Any]    =    self.dummy_sample
														_lowerCAmelCase     :							Optional[int]    =    0.1 * sample

														_lowerCAmelCase     :							Union[str, Any]    =    scheduler.step(__a,			__a,			__a).prev_sample
														_lowerCAmelCase     :							Any    =    scheduler.step(__a,			__a,			__a).prev_sample

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







							def    snake_case__      (   self):

														'''simple docstring'''

														for timesteps in [10, 50, 100, 1000]:
																					self.check_over_configs(num_train_timesteps=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														for clip_denoised in [True, False]:
																					self.check_over_configs(clip_denoised=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    self.scheduler_classes[0]
														_lowerCAmelCase     :							Optional[Any]    =    self.get_scheduler_config()
														_lowerCAmelCase     :							List[Any]    =    scheduler_class(**__a)

														_lowerCAmelCase     :							Union[str, Any]    =    1
														scheduler.set_timesteps(__a)
														_lowerCAmelCase     :							Any    =    scheduler.timesteps

														_lowerCAmelCase     :							List[str]    =    torch.manual_seed(0)

														_lowerCAmelCase     :							str    =    self.dummy_model()
														_lowerCAmelCase     :							str    =    self.dummy_sample_deter * scheduler.init_noise_sigma

														for i, t in enumerate(__a):
																					# 1. scale model input
																					_lowerCAmelCase     :							Dict    =    scheduler.scale_model_input(__a,			__a)

																					# 2. predict noise residual
																					_lowerCAmelCase     :							Union[str, Any]    =    model(__a,			__a)

																					# 3. predict previous sample x_t-1
																					_lowerCAmelCase     :							Tuple    =    scheduler.step(__a,			__a,			__a,			generator=__a).prev_sample

																					_lowerCAmelCase     :							List[Any]    =    pred_prev_sample

														_lowerCAmelCase     :							Optional[Any]    =    torch.sum(torch.abs(__a))
														_lowerCAmelCase     :							List[str]    =    torch.mean(torch.abs(__a))

														assert abs(result_sum.item() - 192.7_614) < 1E-2
														assert abs(result_mean.item() - 0.2_510) < 1E-3







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.scheduler_classes[0]
														_lowerCAmelCase     :							Dict    =    self.get_scheduler_config()
														_lowerCAmelCase     :							Tuple    =    scheduler_class(**__a)

														_lowerCAmelCase     :							Dict    =    [106, 0]
														scheduler.set_timesteps(timesteps=__a)
														_lowerCAmelCase     :							Dict    =    scheduler.timesteps

														_lowerCAmelCase     :							str    =    torch.manual_seed(0)

														_lowerCAmelCase     :							Tuple    =    self.dummy_model()
														_lowerCAmelCase     :							str    =    self.dummy_sample_deter * scheduler.init_noise_sigma

														for t in timesteps:
																					# 1. scale model input
																					_lowerCAmelCase     :							Tuple    =    scheduler.scale_model_input(__a,			__a)

																					# 2. predict noise residual
																					_lowerCAmelCase     :							List[str]    =    model(__a,			__a)

																					# 3. predict previous sample x_t-1
																					_lowerCAmelCase     :							str    =    scheduler.step(__a,			__a,			__a,			generator=__a).prev_sample

																					_lowerCAmelCase     :							Tuple    =    pred_prev_sample

														_lowerCAmelCase     :							List[Any]    =    torch.sum(torch.abs(__a))
														_lowerCAmelCase     :							Tuple    =    torch.mean(torch.abs(__a))

														assert abs(result_sum.item() - 347.6_357) < 1E-2
														assert abs(result_mean.item() - 0.4_527) < 1E-3







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.scheduler_classes[0]
														_lowerCAmelCase     :							List[str]    =    self.get_scheduler_config()
														_lowerCAmelCase     :							Union[str, Any]    =    scheduler_class(**__a)

														_lowerCAmelCase     :							Tuple    =    [39, 30, 12, 15, 0]

														with self.assertRaises(__a,			msg="`timesteps` must be in descending order."):
																					scheduler.set_timesteps(timesteps=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    self.scheduler_classes[0]
														_lowerCAmelCase     :							Optional[int]    =    self.get_scheduler_config()
														_lowerCAmelCase     :							List[str]    =    scheduler_class(**__a)

														_lowerCAmelCase     :							Optional[int]    =    [39, 30, 12, 1, 0]
														_lowerCAmelCase     :							Dict    =    len(__a)

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.scheduler_classes[0]
														_lowerCAmelCase     :							Optional[Any]    =    self.get_scheduler_config()
														_lowerCAmelCase     :							Dict    =    scheduler_class(**__a)

														_lowerCAmelCase     :							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)





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


_snake_case		=  logging.get_logger(__name__)


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						from transformers import Trainer



						class   UpperCAmelCase_    (       a):







													def __init__(   self,			__a = 101):

																				'''simple docstring'''

																				_lowerCAmelCase     :							str    =    length







													def __len__(   self):

																				'''simple docstring'''

																				return self.length







													def __getitem__(   self,			__a):

																				'''simple docstring'''

																				return i



						class   UpperCAmelCase_    :







													def __call__(   self,			__a):

																				'''simple docstring'''

																				return {"input_ids": torch.tensor(__a), "labels": torch.tensor(__a)}



						class   UpperCAmelCase_    (       nn.Module):







													def __init__(   self):

																				'''simple docstring'''

																				super().__init__()
																				# Add some (unused) params otherwise DDP will complain.
																				_lowerCAmelCase     :							str    =    nn.Linear(120,			80)







													def    snake_case__      (   self,			__a,			__a=None):

																				'''simple docstring'''

																				if labels is not None:
																											return torch.tensor(0.0,			device=input_ids.device), input_ids
																				else:
																											return input_ids



class   UpperCAmelCase_    (       a):







							@require_torch_neuroncore
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
														_lowerCAmelCase     :							Tuple    =    self.get_auto_remove_tmp_dir()
														_lowerCAmelCase     :							Optional[int]    =    f"--output_dir {output_dir}".split()
														_lowerCAmelCase     :							List[Any]    =    ["torchrun"] + distributed_args + args
														execute_subprocess_async(__a,			env=self.get_env())
														# successful return here == success - any errors would have caused an error in the sub-call



class   UpperCAmelCase_    (       a):







							@require_torch_multi_gpu
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
														_lowerCAmelCase     :							Any    =    self.get_auto_remove_tmp_dir()
														_lowerCAmelCase     :							Optional[int]    =    f"--output_dir {output_dir}".split()
														_lowerCAmelCase     :							Any    =    ["torchrun"] + distributed_args + args
														execute_subprocess_async(__a,			env=self.get_env())
														# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
						# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
						#
						# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

						_snake_case		=  HfArgumentParser((TrainingArguments,))
						_snake_case		=  parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
						    f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
						)

						# Essentially, what we want to verify in the distributed case is that we get all samples back,
						# in the right order. (this is crucial for prediction for instance)
						for dataset_length in [101, 40, 7]:
												_snake_case		=  DummyDataset(dataset_length)






												def 				A				(					_lowerCamelCase  ):





																			'''simple docstring'''




																			_lowerCAmelCase     :							Dict    =    list(range(len(_lowerCamelCase  )  )  )
																			_lowerCAmelCase     :							Union[str, Any]    =    p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																			if not success and training_args.local_rank == 0:
																										logger.warning(
																										    "Predictions and/or labels do not match expected results:\n  - predictions: "
																										    F"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"  )
																			return {"success": success}

												_snake_case		=  Trainer(
												    model=DummyModel(),
												    args=training_args,
												    data_collator=DummyDataCollator(),
												    eval_dataset=dataset,
												    compute_metrics=compute_metrics,
												)
												_snake_case		=  trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												_snake_case		=  trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)

												_snake_case		=  2

												_snake_case		=  trainer.evaluate()
												logger.info(metrics)
												if metrics["eval_success"] is not True:
																		logger.error(metrics)
																		exit(1)

												_snake_case		=  trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)



												_snake_case		=  None
