



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= (DDIMScheduler,)
						_lowerCAmelCase								= (('''eta''', 0.0), ('''num_inference_steps''', 50))


						def   _lowerCamelCase			( self						:  Any      ,       **_a						:  int			):
													a__:       Union[str, Any]											={
													    "num_train_timesteps": 1_0_0_0,
													    "beta_start": 0.0_0_0_1,
													    "beta_end": 0.0_2,
													    "beta_schedule": "linear",
													    "clip_sample": True,
													}

													config.update(**_a			)
													return config


						def   _lowerCamelCase			( self						:  Tuple      ,       **_a						:  Any			):
													a__:       Union[str, Any]											=self.scheduler_classes[0]
													a__:       List[str]											=self.get_scheduler_config(**_a			)
													a__:       int											=scheduler_class(**_a			)

													a__							,			a__:       Tuple											=1_0, 0.0

													a__:       Dict											=self.dummy_model()
													a__:       Optional[Any]											=self.dummy_sample_deter

													scheduler.set_timesteps(_a			)

													for t in scheduler.timesteps:
																				a__:       str											=model(_a      ,       _a			)
																				a__:       List[Any]											=scheduler.step(_a      ,       _a      ,       _a      ,       _a			).prev_sample

													return sample


						def   _lowerCamelCase			( self						:  int			):
													for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
																				self.check_over_configs(num_train_timesteps=_a			)


						def   _lowerCamelCase			( self						:  int			):
													for steps_offset in [0, 1]:
																				self.check_over_configs(steps_offset=_a			)

													a__:       str											=self.scheduler_classes[0]
													a__:       Dict											=self.get_scheduler_config(steps_offset=1			)
													a__:       Any											=scheduler_class(**_a			)
													scheduler.set_timesteps(5			)
													assert torch.equal(scheduler.timesteps      ,       torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]			)			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]			):
																				self.check_over_configs(beta_start=_a      ,       beta_end=_a			)


						def   _lowerCamelCase			( self						:  int			):
													for schedule in ["linear", "squaredcos_cap_v2"]:
																				self.check_over_configs(beta_schedule=_a			)


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=_a			)


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													for clip_sample in [True, False]:
																				self.check_over_configs(clip_sample=_a			)


						def   _lowerCamelCase			( self						:  List[Any]			):
													for timestep_spacing in ["trailing", "leading"]:
																				self.check_over_configs(timestep_spacing=_a			)


						def   _lowerCamelCase			( self						:  List[str]			):
													for rescale_betas_zero_snr in [True, False]:
																				self.check_over_configs(rescale_betas_zero_snr=_a			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													self.check_over_configs(thresholding=_a			)
													for threshold in [0.5, 1.0, 2.0]:
																				for prediction_type in ["epsilon", "v_prediction"]:
																											self.check_over_configs(
																											    thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )


						def   _lowerCamelCase			( self						:  str			):
													for t in [1, 1_0, 4_9]:
																				self.check_over_forward(time_step=_a			)


						def   _lowerCamelCase			( self						:  Optional[int]			):
													for t, num_inference_steps in zip([1, 1_0, 5_0]      ,       [1_0, 5_0, 5_0_0]			):
																				self.check_over_forward(time_step=_a      ,       num_inference_steps=_a			)


						def   _lowerCamelCase			( self						:  str			):
													for t, eta in zip([1, 1_0, 4_9]      ,       [0.0, 0.5, 1.0]			):
																				self.check_over_forward(time_step=_a      ,       eta=_a			)


						def   _lowerCamelCase			( self						:  Any			):
													a__:       Any											=self.scheduler_classes[0]
													a__:       Optional[int]											=self.get_scheduler_config()
													a__:       Any											=scheduler_class(**_a			)

													assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0			) - 0.0			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      ,       4_0_0			) - 0.1_4_7_7_1			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      ,       9_6_0			) - 0.3_2_4_6_0			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0			) - 0.0			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ,       4_8_6			) - 0.0_0_9_7_9			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ,       9_9_8			) - 0.0_2			)			) < 1e-5


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													a__:       Tuple											=self.full_loop()

													a__:       Optional[int]											=torch.sum(torch.abs(_a			)			)
													a__:       Optional[int]											=torch.mean(torch.abs(_a			)			)

													assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1e-2
													assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3


						def   _lowerCamelCase			( self						:  Any			):
													a__:       Union[str, Any]											=self.full_loop(prediction_type="v_prediction"			)

													a__:       Dict											=torch.sum(torch.abs(_a			)			)
													a__:       Union[str, Any]											=torch.mean(torch.abs(_a			)			)

													assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1e-2
													assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3


						def   _lowerCamelCase			( self						:  Any			):
													# We specify different beta, so that the first alpha is 0.99
													a__:       List[Any]											=self.full_loop(set_alpha_to_one=_a      ,       beta_start=0.0_1			)
													a__:       Optional[int]											=torch.sum(torch.abs(_a			)			)
													a__:       Any											=torch.mean(torch.abs(_a			)			)

													assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1e-2
													assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3







						def   _lowerCamelCase			( self						:  Tuple			):
													# We specify different beta, so that the first alpha is 0.99
													a__:       Any											=self.full_loop(set_alpha_to_one=_a      ,       beta_start=0.0_1			)
													a__:       str											=torch.sum(torch.abs(_a			)			)
													a__:       Union[str, Any]											=torch.mean(torch.abs(_a			)			)

													assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1e-2
													assert abs(result_mean.item() - 0.1_9_4_1			) < 1e-3










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase						  =     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 	lowerCamelCase__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
							    F"tpu_num_cores: {training_args.tpu_num_cores}"     , )

							# 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 [1_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														a__:       Union[str, Any]											=Trainer(
														    model=DummyModel()     , args=__magic_name__     , data_collator=DummyDataCollator()     , eval_dataset=__magic_name__     , compute_metrics=__magic_name__     , )
														a__:       str											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

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

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





