




'''simple docstring'''

import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def    snake_case__     (  )			->       Tuple:





			'''simple docstring'''






			snake_case__          =						Dataset.from_dict({"""i""": range(17	)}	)
			snake_case__          =						len(a	)
			snake_case__          =						3
			snake_case__          =						[
			    split_dataset_by_node(a       ,      rank=a       ,      world_size=a	) for rank in range(a	)
			]
			assert sum(len(a	) for ds in datasets_per_rank	) == full_size
			assert len({tuple(x.values()	) for ds in datasets_per_rank for x in ds}	) == full_size




def    snake_case__     (  )			->       List[Any]:





			'''simple docstring'''






			def gen():
						return ({"i": i} for i in range(17	))

			snake_case__          =						3
			snake_case__          =						IterableDataset.from_generator(a	)
			snake_case__          =						len(list(a	)	)
			snake_case__          =						[
			    split_dataset_by_node(a       ,      rank=a       ,      world_size=a	) for rank in range(a	)
			]
			assert sum(len(list(a	)	) for ds in datasets_per_rank	) == full_size
			assert len({tuple(x.values()	) for ds in datasets_per_rank for x in ds}	) == full_size




@pytest.mark.parametrize("""shards_per_node"""       ,      [1, 2, 3]	)
def    snake_case__     (  a	)			->       int:





			'''simple docstring'''






			def gen(a	):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(17	))

			snake_case__          =						3
			snake_case__          =						shards_per_node * world_size
			snake_case__          =						{"""shards""": [F"""shard_{shard_idx}.txt""" for shard_idx in range(a	)]}
			snake_case__          =						IterableDataset.from_generator(a       ,      gen_kwargs=a	)
			snake_case__          =						len(list(a	)	)
			assert full_ds.n_shards == world_size * shards_per_node
			snake_case__          =						[
			    split_dataset_by_node(a       ,      rank=a       ,      world_size=a	) for rank in range(a	)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(a	)	) for ds in datasets_per_rank	) == full_size
			assert len({tuple(x.values()	) for ds in datasets_per_rank for x in ds}	) == full_size




def    snake_case__     (  )			->       List[str]:





			'''simple docstring'''






			def gen():
						return ({"i": i} for i in range(17	))

			snake_case__          =						2
			snake_case__          =						IterableDataset.from_generator(a	)
			snake_case__          =						len(list(a	)	)

			snake_case__          =						split_dataset_by_node(a       ,      rank=0       ,      world_size=a	).shuffle(seed=42	)
			assert len(list(a	)	) == 1 + full_size // world_size
			with pytest.raises(a	):
						split_dataset_by_node(a       ,      rank=0       ,      world_size=a	).shuffle()

			snake_case__          =						split_dataset_by_node(full_ds.shuffle(seed=42	)       ,      rank=0       ,      world_size=a	)
			assert len(list(a	)	) == 1 + full_size // world_size
			with pytest.raises(a	):
						split_dataset_by_node(full_ds.shuffle()       ,      rank=0       ,      world_size=a	)




@pytest.mark.parametrize("""streaming"""       ,      [False, True]	)
@require_torch
@pytest.mark.skipif(os.name == """nt"""       ,      reason="""execute_subprocess_async doesn't support windows"""	)
@pytest.mark.integration
def    snake_case__     (  a	)			->       str:





			'''simple docstring'''






			snake_case__          =						2
			snake_case__          =						get_torch_dist_unique_port()
			snake_case__          =						Path(a	).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
			snake_case__          =						F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			snake_case__          =						F"""
        --streaming={streaming}
    """.split()
			snake_case__          =						[sys.executable] + distributed_args + args
			execute_subprocess_async(a       ,      env=os.environ.copy()	)




@pytest.mark.parametrize(
    """nproc_per_node, num_workers"""       ,      [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]       ,      )
@require_torch
@pytest.mark.skipif(os.name == """nt"""       ,      reason="""execute_subprocess_async doesn't support windows"""	)
@pytest.mark.integration
def    snake_case__     (  a       ,      a	)			->       int:





			'''simple docstring'''






			snake_case__          =						True
			snake_case__          =						get_torch_dist_unique_port()
			snake_case__          =						Path(a	).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
			snake_case__          =						F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
			snake_case__          =						F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
			snake_case__          =						[sys.executable] + distributed_args + args
			execute_subprocess_async(a       ,      env=os.environ.copy()	)



"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Tuple     =		(DDIMScheduler,)
     A_							:						Tuple     =		(('eta', 0.0), ('num_inference_steps', 50))


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				str:
          _a     						=					{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''clip_sample''': True,
          }

          config.update(**__UpperCAmelCase				)
          return config


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				List[Any]:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config(**__UpperCAmelCase				)
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          _a						,   _a     						=					10, 0.0

          _a     						=					self.dummy_model()
          _a     						=					self.dummy_sample_deter

          scheduler.set_timesteps(__UpperCAmelCase				)

          for t in scheduler.timesteps:
               _a     						=					model(__UpperCAmelCase     ,					__UpperCAmelCase				)
               _a     						=					scheduler.step(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				).prev_sample

          return sample


     def 				_UpperCAmelCase   (   self				)							->				str:
          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__UpperCAmelCase				)

          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config(steps_offset=1				)
          _a     						=					scheduler_class(**__UpperCAmelCase				)
          scheduler.set_timesteps(5				)
          assert torch.equal(scheduler.timesteps     ,					torch.LongTensor([801, 601, 401, 201, 1]				)				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          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=__UpperCAmelCase     ,					beta_end=__UpperCAmelCase				)


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


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


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


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          self.check_over_configs(thresholding=__UpperCAmelCase				)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__UpperCAmelCase     ,					prediction_type=__UpperCAmelCase     ,					sample_max_value=__UpperCAmelCase     ,					)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for t in [1, 10, 49]:
               self.check_over_forward(time_step=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          for t, num_inference_steps in zip([1, 10, 50]     ,					[10, 50, 500]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					num_inference_steps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          for t, eta in zip([1, 10, 49]     ,					[0.0, 0.5, 1.0]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					eta=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config()
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420     ,					400				) - 0.14771				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980     ,					960				) - 0.32460				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487     ,					486				) - 0.00979				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999     ,					998				) - 0.02				)				) < 1e-5


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					self.full_loop()

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 172.0067				) < 1e-2
          assert abs(result_mean.item() - 0.223967				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					self.full_loop(prediction_type='''v_prediction'''				)

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 52.5302				) < 1e-2
          assert abs(result_mean.item() - 0.0684				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.8295				) < 1e-2
          assert abs(result_mean.item() - 0.1951				) < 1e-3





     def 				_UpperCAmelCase   (   self				)							->				int:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.0784				) < 1e-2
          assert abs(result_mean.item() - 0.1941				) < 1e-3