from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class      __lowercase					(    _A    ):

						def __init__(	self				:      Tuple							,							__lowerCamelCase				:      Distribution							,							__lowerCamelCase				:      Any=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      Union[str, Any]=0   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		1.0 if scale is None else scale
									lowercase							=		0.0 if loc is None else loc

									super().__init__(__lowerCamelCase							,							[AffineTransform(loc=self.loc							,							scale=self.scale							,							event_dim=__lowerCamelCase   )]   )

						@property
						def        __a		(	self				:      Optional[Any]   )							->		int:


									'''simple docstring'''

									return self.base_dist.mean * self.scale + self.loc

						@property
						def        __a		(	self				:      Dict   )							->		Any:


									'''simple docstring'''

									return self.base_dist.variance * self.scale**2



						@property
						def        __a		(	self				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									return self.variance.sqrt()






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Union[str, Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Dict[str, int]							,							__lowerCamelCase				:      Callable[..., Tuple[torch.Tensor]]							,							**__lowerCamelCase				:      Optional[Any]   )							->		None:


									'''simple docstring'''

									super().__init__(**__lowerCamelCase   )
									lowercase							=		args_dim
									lowercase							=		nn.ModuleList([nn.Linear(__lowerCamelCase							,							__lowerCamelCase   ) for dim in args_dim.values()]   )
									lowercase							=		domain_map



						def        __a		(	self				:      str							,							__lowerCamelCase				:      torch.Tensor   )							->		Tuple[torch.Tensor]:


									'''simple docstring'''

									lowercase							=		[proj(__lowerCamelCase   ) for proj in self.proj]

									return self.domain_map(*__lowerCamelCase   )






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		function



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[Any]							,							*__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									return self.function(__lowerCamelCase							,							*__lowerCamelCase   )






class      __lowercase					:
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42

						def __init__(	self				:      Union[str, Any]							,							__lowerCamelCase				:      int = 1   )							->		None:


									'''simple docstring'''

									lowercase							=		dim
									lowercase							=		{k: dim * self.args_dim[k] for k in self.args_dim}

						def        __a		(	self				:      str							,							__lowerCamelCase				:      str   )							->		Optional[int]:


									'''simple docstring'''

									if self.dim == 1:
												return self.distribution_class(*__lowerCamelCase   )
									else:
												return Independent(self.distribution_class(*__lowerCamelCase   )							,							1   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[torch.Tensor] = None							,							__lowerCamelCase				:      Optional[torch.Tensor] = None							,							)							->		Distribution:


									'''simple docstring'''

									lowercase							=		self._base_distribution(__lowerCamelCase   )
									if loc is None and scale is None:
												return distr
									else:
												return AffineTransformed(__lowerCamelCase							,							loc=__lowerCamelCase							,							scale=__lowerCamelCase							,							event_dim=self.event_dim   )

						@property
						def        __a		(	self				:      Any   )							->		Tuple:


									'''simple docstring'''

									return () if self.dim == 1 else (self.dim,)

						@property
						def        __a		(	self				:      Dict   )							->		int:


									'''simple docstring'''

									return len(self.event_shape   )

						@property
						def        __a		(	self				:      Any   )							->		float:


									'''simple docstring'''

									return 0.0

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      int   )							->		nn.Module:


									'''simple docstring'''

									return ParameterProjection(
									    in_features=__lowerCamelCase							,							args_dim=self.args_dim							,							domain_map=LambdaLayer(self.domain_map   )							,							)

						def        __a		(	self				:      Optional[int]							,							*__lowerCamelCase				:      torch.Tensor   )							->		List[Any]:


									'''simple docstring'''

									raise NotImplementedError()



						@staticmethod
						def        __a		(	__lowerCamelCase				:      torch.Tensor   )							->		torch.Tensor:


									'''simple docstring'''

									return (x + torch.sqrt(torch.square(__lowerCamelCase   ) + 4.0   )) / 2.0






class      __lowercase					(    _A    ):
						lowercase					 =     {'df': 1, 'loc': 1, 'scale': 1}
						lowercase					 =     StudentT

						@classmethod
						def        __a		(	cls				:      Optional[Any]							,							__lowerCamelCase				:      torch.Tensor							,							__lowerCamelCase				:      torch.Tensor							,							__lowerCamelCase				:      torch.Tensor   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		cls.squareplus(__lowerCamelCase   ).clamp_min(torch.finfo(scale.dtype   ).eps   )
									lowercase							=		2.0 + cls.squareplus(__lowerCamelCase   )
									return df.squeeze(-1   ), loc.squeeze(-1   ), scale.squeeze(-1   )






class      __lowercase					(    _A    ):
						lowercase					 =     {'loc': 1, 'scale': 1}
						lowercase					 =     Normal

						@classmethod
						def        __a		(	cls				:      List[Any]							,							__lowerCamelCase				:      torch.Tensor							,							__lowerCamelCase				:      torch.Tensor   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		cls.squareplus(__lowerCamelCase   ).clamp_min(torch.finfo(scale.dtype   ).eps   )
									return loc.squeeze(-1   ), scale.squeeze(-1   )






class      __lowercase					(    _A    ):
						lowercase					 =     {'total_count': 1, 'logits': 1}
						lowercase					 =     NegativeBinomial

						@classmethod
						def        __a		(	cls				:      List[Any]							,							__lowerCamelCase				:      torch.Tensor							,							__lowerCamelCase				:      torch.Tensor   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		cls.squareplus(__lowerCamelCase   )
									return total_count.squeeze(-1   ), logits.squeeze(-1   )

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Tuple   )							->		Distribution:


									'''simple docstring'''

									lowercase		,lowercase							=		distr_args
									if self.dim == 1:
												return self.distribution_class(total_count=__lowerCamelCase							,							logits=__lowerCamelCase   )
									else:
												return Independent(self.distribution_class(total_count=__lowerCamelCase							,							logits=__lowerCamelCase   )							,							1   )



						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[torch.Tensor] = None							,							__lowerCamelCase				:      Optional[torch.Tensor] = None   )							->		Distribution:


									'''simple docstring'''

									lowercase		,lowercase							=		distr_args

									if scale is not None:
												# See scaling property of Gamma.
												logits += scale.log()

									return self._base_distribution((total_count, logits)   )






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      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									return datasets.DatasetInfo(
									    features=datasets.Features({'''content''': datasets.Value('''string'''   )}   )							,							supervised_keys=__lowerCamelCase							,							)

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple   )							->		int:


									'''simple docstring'''

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



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									import apache_beam as beam

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






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		int:


									'''simple docstring'''

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



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

									import apache_beam as beam

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






def        __UpperCAmelCase    (       )->       Dict:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







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






class      __lowercase					(    _A    ):

						@require_beam
						def        __a		(	self				:      Optional[int]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		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'''   )}   )   )
												lowercase							=		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        __a		(	self				:      Union[str, Any]   )							->		List[Any]:


									'''simple docstring'''

									import apache_beam as beam

									lowercase							=		beam.io.parquetio.WriteToParquet

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												with patch('''apache_beam.io.parquetio.WriteToParquet'''   ) as write_parquet_mock:
															lowercase							=		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'''   )}   )   )
												lowercase							=		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        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase   )
												self.assertRaises(datasets.builder.MissingBeamOptions							,							builder.download_and_prepare   )



						@require_beam
						def        __a		(	self				:      str   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_nested_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		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'''   )}   )}   )   )
												lowercase							=		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





