



# 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   data




	def __iter__(self		):
						"""simple docstring"""

						for element in self.data:
											yield element






def     a(      A       :    str=True       )		->       Any:
					"""simple docstring"""





					a					     =   Accelerator(even_batches=A       )
					assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
					return accelerator






def     a(      A       :    Accelerator ,     A       :    int ,     A       :    int ,     A       :    bool = False       )		->       List[Any]:
					"""simple docstring"""





					if iterable:
										a					     =   DummyIterableDataset(torch.as_tensor(range(A       )       )       )
					else:
										a					     =   TensorDataset(torch.as_tensor(range(A       )       )       )

					a					     =   DataLoader(A ,     batch_size=A       )
					a					     =   accelerator.prepare(A       )

					return dl






def     a(      A       :    Accelerator ,     A       :    int ,     A       :    int ,     A       :    List[int] ,     A       :    List[int] ,     )		->       int:
					"""simple docstring"""





					a					     =   create_dataloader(accelerator=A ,     dataset_size=A ,     batch_size=A       )

					a					     =   [len(batch[0]       ) for batch in dl]

					if accelerator.process_index == 0:
										assert batch_sizes == process_0_expected_batch_sizes
					elif accelerator.process_index == 1:
										assert batch_sizes == process_1_expected_batch_sizes






def     a(      )		->       Optional[Any]:
					"""simple docstring"""





					a					     =   create_accelerator()

					# without padding, we would expect a different number of batches
					verify_dataloader_batch_sizes(
					    A ,     dataset_size=3 ,     batch_size=1 ,     process_0_expected_batch_sizes=[1, 1] ,     process_1_expected_batch_sizes=[1, 1] ,     )

					# without padding, we would expect the same number of batches, but different sizes
					verify_dataloader_batch_sizes(
					    A ,     dataset_size=7 ,     batch_size=2 ,     process_0_expected_batch_sizes=[2, 2] ,     process_1_expected_batch_sizes=[2, 2] ,     )






def     a(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   create_accelerator(even_batches=A       )

					verify_dataloader_batch_sizes(
					    A ,     dataset_size=3 ,     batch_size=1 ,     process_0_expected_batch_sizes=[1, 1] ,     process_1_expected_batch_sizes=[1] ,     )

					verify_dataloader_batch_sizes(
					    A ,     dataset_size=7 ,     batch_size=2 ,     process_0_expected_batch_sizes=[2, 2] ,     process_1_expected_batch_sizes=[2, 1] ,     )






def     a(      )		->       Dict:
					"""simple docstring"""





					a					     =   create_accelerator(even_batches=A       )

					a					     =   torch.nn.Linear(1 ,     1       )
					a					     =   accelerator.prepare(A       )

					a					     =   create_dataloader(A ,     dataset_size=3 ,     batch_size=1       )

					a					     =   []
					with accelerator.join_uneven_inputs([ddp_model]       ):
										for batch_idx, batch in enumerate(A       ):
															a					     =   ddp_model(batch[0].float()       )
															a					     =   output.sum()
															loss.backward()
															batch_idxs.append(A       )

					accelerator.wait_for_everyone()

					if accelerator.process_index == 0:
										assert batch_idxs == [0, 1]
					elif accelerator.process_index == 1:
										assert batch_idxs == [0]






def     a(      A       :    str       )		->       Union[str, Any]:
					"""simple docstring"""





					with warnings.catch_warnings(record=A       ) as w:
										with accelerator.join_uneven_inputs([Mock()]       ):
															pass

										assert issubclass(w[-1].category ,     A       )
										assert "only supported for multi-GPU" in str(w[-1].message       )






def     a(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   True
					a					     =   False
					a					     =   create_accelerator(even_batches=A       )
					a					     =   torch.nn.Linear(1 ,     1       )
					a					     =   accelerator.prepare(A       )
					a					     =   create_dataloader(A ,     dataset_size=3 ,     batch_size=1       )
					a					     =   create_dataloader(A ,     dataset_size=3 ,     batch_size=1       )

					with accelerator.join_uneven_inputs([ddp_model] ,     even_batches=A       ):
										a					     =   train_dl.batch_sampler.even_batches
										a					     =   valid_dl.batch_sampler.even_batches

					assert train_dl_overridden_value == overridden_even_batches
					assert valid_dl_overridden_value == overridden_even_batches
					assert train_dl.batch_sampler.even_batches == default_even_batches
					assert valid_dl.batch_sampler.even_batches == default_even_batches






def     a(      )		->       List[Any]:
					"""simple docstring"""





					a					     =   True
					a					     =   False
					a					     =   create_accelerator(even_batches=A       )
					a					     =   torch.nn.Linear(1 ,     1       )
					a					     =   accelerator.prepare(A       )
					create_dataloader(A ,     dataset_size=3 ,     batch_size=1 ,     iterable=A       )
					a					     =   create_dataloader(A ,     dataset_size=3 ,     batch_size=1       )

					with warnings.catch_warnings():
										warnings.filterwarnings("ignore"       )
										try:
															with accelerator.join_uneven_inputs([ddp_model] ,     even_batches=A       ):
																				a					     =   batch_dl.batch_sampler.even_batches
										except AttributeError:
															# ensure attribute error is not raised when processing iterable dl
															raise AssertionError

					assert batch_dl_overridden_value == overridden_even_batches
					assert batch_dl.batch_sampler.even_batches == default_even_batches






def     a(      )		->       str:
					"""simple docstring"""





					a					     =   create_accelerator()
					a					     =   torch.nn.Linear(1 ,     1       )
					a					     =   accelerator.prepare(A       )
					create_dataloader(A ,     dataset_size=3 ,     batch_size=1 ,     iterable=A       )

					with warnings.catch_warnings(record=A       ) as w:
										with accelerator.join_uneven_inputs([ddp_model] ,     even_batches=A       ):
															pass

										assert issubclass(w[-1].category ,     A       )
										assert "only supported for map-style datasets" in str(w[-1].message       )






def     a(      )		->       int:
					"""simple docstring"""





					a					     =   create_accelerator()

					accelerator.print("Test that even_batches variable ensures uniform batches across processes"       )
					test_default_ensures_even_batch_sizes()

					accelerator.print("Run tests with even_batches disabled"       )
					test_can_disable_even_batches()

					accelerator.print("Test joining uneven inputs"       )
					test_can_join_uneven_inputs()

					accelerator.print("Test overriding even_batches when joining uneven inputs"       )
					test_join_can_override_even_batches()

					accelerator.print("Test overriding even_batches for mixed dataloader types"       )
					test_join_can_override_for_mixed_type_dataloaders()

					accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"       )
					test_join_raises_warning_for_iterable_when_overriding_even_batches()

					accelerator.print("Test join with non DDP distributed raises warning"       )
					a					     =   accelerator.state.distributed_type
					a					     =   DistributedType.FSDP
					test_join_raises_warning_for_non_ddp_distributed(A       )
					a					     =   original_state


if __name__ == "__main__":
				main()





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(DDPMScheduler,)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "variance_type": "fixed_small",
						    "clip_sample": True,
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,       [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=lowerCamelCase_		,       beta_end=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.check_over_configs(thresholding=lowerCamelCase_		)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase_		,       prediction_type=lowerCamelCase_		,       sample_max_value=lowerCamelCase_		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

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

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 258.9606		) < 1E-2
						assert abs(result_mean.item() - 0.3372		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(prediction_type="v_prediction"		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

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

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 202.0296		) < 1E-2
						assert abs(result_mean.item() - 0.2631		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=lowerCamelCase_		)

						a					     =   scheduler.timesteps

						for i, timestep in enumerate(lowerCamelCase_		):
											if i == len(lowerCamelCase_		) - 1:
																a					     =   -1
											else:
																a					     =   timesteps[i + 1]

											a					     =   scheduler.previous_timestep(lowerCamelCase_		)
											a					     =   prev_t.item()

											self.assertEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 51, 0]

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]
						a					     =   len(lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [scheduler.config.num_train_timesteps]

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