
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	1






	@register_to_config
	def __init__(			self   :					List[Any]				,A   :					str=20_00				,A   :					str=0.1				,A   :					List[str]=20				,A   :					int=1E-3					):
					__A       =					None
					__A       =					None
					__A       =					None






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					List[str]				,A   :					Union[str, torch.device] = None					):
					__A       =					torch.linspace(1				,self.config.sampling_eps				,A				,device=A					)






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					List[Any]				,A   :					Optional[Any]				,A   :					Union[str, Any]				,A   :					Union[str, Any]=None					):
					if self.timesteps is None:
									raise ValueError(
									    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

					# TODO(Patrick) better comments + non-PyTorch
					# postprocess model score
					__A       =					(
					    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
					)
					__A       =					torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
					__A       =					std.flatten()
					while len(std.shape					) < len(score.shape					):
									__A       =					std.unsqueeze(-1					)
					__A       =					-score / std

					# compute
					__A       =					-1.0 / len(self.timesteps					)

					__A       =					self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
					__A       =					beta_t.flatten()
					while len(beta_t.shape					) < len(x.shape					):
									__A       =					beta_t.unsqueeze(-1					)
					__A       =					-0.5 * beta_t * x

					__A       =					torch.sqrt(A					)
					__A       =					drift - diffusion**2 * score
					__A       =					x + drift * dt

					# add noise
					__A       =					randn_tensor(x.shape				,layout=x.layout				,generator=A				,device=x.device				,dtype=x.dtype					)
					__A       =					x_mean + diffusion * math.sqrt(-dt					) * noise

					return x, x_mean







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




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 						_snake_case    ( datasets.BeamBasedBuilder				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return datasets.DatasetInfo(
           features=datasets.Features({'''content''': datasets.Value('''string'''       )}       )   ,    supervised_keys=_lowerCamelCase   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,    gen_kwargs={'''examples''': get_test_dummy_examples()}       )]




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       import apache_beam as beam

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







class 						_snake_case    ( datasets.BeamBasedBuilder				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return datasets.DatasetInfo(
           features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''       )}       )}       )   ,    supervised_keys=_lowerCamelCase   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       return [
           datasets.SplitGenerator(name=datasets.Split.TRAIN   ,    gen_kwargs={'''examples''': get_test_nested_examples()}       )
       ]




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       import apache_beam as beam

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







def 							__lowerCamelCase		(					):




      """simple docstring"""





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




      """simple docstring"""





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







class 						_snake_case    ( _snake_case				):




 @require_beam
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			len(get_test_dummy_examples()       )
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:Optional[Any]									=			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		:str									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       import apache_beam as beam

       a		:Any									=			beam.io.parquetio.WriteToParquet

       a		:Any									=			len(get_test_dummy_examples()       )
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:Union[str, Any]									=			DummyBeamDataset(cache_dir=_lowerCamelCase   ,    beam_runner='''DirectRunner'''       )
             with patch('''apache_beam.io.parquetio.WriteToParquet'''       ) as write_parquet_mock:
                   a		:str									=			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		:List[str]									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:Dict									=			DummyBeamDataset(cache_dir=_lowerCamelCase       )
             self.assertRaises(datasets.builder.MissingBeamOptions   ,    builder.download_and_prepare       )




 @require_beam
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			len(get_test_nested_examples()       )
       with tempfile.TemporaryDirectory() as tmp_cache_dir:
             a		:List[Any]									=			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		:Any									=			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





