



import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam


class 	lowerCamelCase__	(       datasets.BeamBasedBuilder):



 """simple docstring"""

 def     snake_case_      (    self					:					List[Any]      )   ->	List[str]:
    return datasets.DatasetInfo(
        features=datasets.Features({'''content''': datasets.Value('''string'''      )}      )			,						supervised_keys=__lowerCAmelCase			,						)

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Dict      )   ->	List[Any]:
    return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,						gen_kwargs={'''examples''': get_test_dummy_examples()}      )]

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Tuple      )   ->	List[str]:
    import apache_beam as beam

    return pipeline | "Load Examples" >> beam.Create(__lowerCAmelCase      )


class 	lowerCamelCase__	(       datasets.BeamBasedBuilder):



 """simple docstring"""

 def     snake_case_      (    self					:					Tuple      )   ->	int:
    return datasets.DatasetInfo(
        features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''      )}      )}      )			,						supervised_keys=__lowerCAmelCase			,						)

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Optional[int]      )   ->	Dict:
    return [
        datasets.SplitGenerator(name=datasets.Split.TRAIN			,						gen_kwargs={'''examples''': get_test_nested_examples()}      )
    ]

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Any      )   ->	str:
    import apache_beam as beam

    return pipeline | "Load Examples" >> beam.Create(__lowerCAmelCase      )

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Any:
   return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']				)]

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']				)]


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 @require_beam
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	List[str]:
    _A      =       len(get_test_dummy_examples()      )
    with tempfile.TemporaryDirectory() as tmp_cache_dir:
       _A      =       DummyBeamDataset(cache_dir=__lowerCAmelCase			,						beam_runner='''DirectRunner'''      )
       builder.download_and_prepare()
       self.assertTrue(
           os.path.exists(
               os.path.join(__lowerCAmelCase			,						builder.name			,						'''default'''			,						'''0.0.0'''			,						f'''{builder.name}-train.arrow'''      )      )      )
       self.assertDictEqual(builder.info.features			,						datasets.Features({'''content''': datasets.Value('''string'''      )}      )      )
       _A      =       builder.as_dataset()
       self.assertEqual(dset['''train'''].num_rows			,						__lowerCAmelCase      )
       self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples			,						__lowerCAmelCase      )
       self.assertDictEqual(dset['''train'''][0]			,						get_test_dummy_examples()[0][1]      )
       self.assertDictEqual(
           dset['''train'''][expected_num_examples - 1]			,						get_test_dummy_examples()[expected_num_examples - 1][1]      )
       self.assertTrue(
           os.path.exists(os.path.join(__lowerCAmelCase			,						builder.name			,						'''default'''			,						'''0.0.0'''			,						'''dataset_info.json'''      )      )      )
       del dset

 @require_beam
 def     snake_case_      (    self					:					int      )   ->	str:
    import apache_beam as beam

    _A      =       beam.io.parquetio.WriteToParquet

    _A      =       len(get_test_dummy_examples()      )
    with tempfile.TemporaryDirectory() as tmp_cache_dir:
       _A      =       DummyBeamDataset(cache_dir=__lowerCAmelCase			,						beam_runner='''DirectRunner'''      )
       with patch('''apache_beam.io.parquetio.WriteToParquet'''      ) as write_parquet_mock:
          _A      =       partial(__lowerCAmelCase			,						num_shards=2      )
          builder.download_and_prepare()
       self.assertTrue(
           os.path.exists(
               os.path.join(
                   __lowerCAmelCase			,						builder.name			,						'''default'''			,						'''0.0.0'''			,						f'''{builder.name}-train-00000-of-00002.arrow'''      )      )      )
       self.assertTrue(
           os.path.exists(
               os.path.join(
                   __lowerCAmelCase			,						builder.name			,						'''default'''			,						'''0.0.0'''			,						f'''{builder.name}-train-00000-of-00002.arrow'''      )      )      )
       self.assertDictEqual(builder.info.features			,						datasets.Features({'''content''': datasets.Value('''string'''      )}      )      )
       _A      =       builder.as_dataset()
       self.assertEqual(dset['''train'''].num_rows			,						__lowerCAmelCase      )
       self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples			,						__lowerCAmelCase      )
       # Order is not preserved when sharding, so we just check that all the elements are there
       self.assertListEqual(sorted(dset['''train''']['''content''']      )			,						sorted(['''foo''', '''bar''', '''foobar''']      )      )
       self.assertTrue(
           os.path.exists(os.path.join(__lowerCAmelCase			,						builder.name			,						'''default'''			,						'''0.0.0'''			,						'''dataset_info.json'''      )      )      )
       del dset

 @require_beam
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    with tempfile.TemporaryDirectory() as tmp_cache_dir:
       _A      =       DummyBeamDataset(cache_dir=__lowerCAmelCase      )
       self.assertRaises(datasets.builder.MissingBeamOptions			,						builder.download_and_prepare      )

 @require_beam
 def     snake_case_      (    self					:					Any      )   ->	int:
    _A      =       len(get_test_nested_examples()      )
    with tempfile.TemporaryDirectory() as tmp_cache_dir:
       _A      =       NestedBeamDataset(cache_dir=__lowerCAmelCase			,						beam_runner='''DirectRunner'''      )
       builder.download_and_prepare()
       self.assertTrue(
           os.path.exists(
               os.path.join(__lowerCAmelCase			,						builder.name			,						'''default'''			,						'''0.0.0'''			,						f'''{builder.name}-train.arrow'''      )      )      )
       self.assertDictEqual(
           builder.info.features			,						datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''      )}      )}      )      )
       _A      =       builder.as_dataset()
       self.assertEqual(dset['''train'''].num_rows			,						__lowerCAmelCase      )
       self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples			,						__lowerCAmelCase      )
       self.assertDictEqual(dset['''train'''][0]			,						get_test_nested_examples()[0][1]      )
       self.assertDictEqual(
           dset['''train'''][expected_num_examples - 1]			,						get_test_nested_examples()[expected_num_examples - 1][1]      )
       self.assertTrue(
           os.path.exists(os.path.join(__lowerCAmelCase			,						builder.name			,						'''default'''			,						'''0.0.0'''			,						'''dataset_info.json'''      )      )      )
       del dset





"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







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

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass