

import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def       snake_case_ (__A	:		Union[str, Any]   ,  __A	:		Union[str, Any]	)     ->      Optional[int]:
   assert isinstance(__A   ,  __A	)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""   ,  [False, True]	)
def       snake_case_ (__A	:		Union[str, Any]   ,  __A	:		Optional[Any]   ,  __A	:		Tuple	)     ->      Tuple:
   __lowerCAmelCase    :     Dict          =		tmp_path / """cache"""
   __lowerCAmelCase    :     int          =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase    :     Dict          =		ParquetDatasetReader(__A   ,  cache_dir=__A   ,  keep_in_memory=__A	).read()
   _check_parquet_dataset(__A   ,  __A	)





@pytest.mark.parametrize(
    """features"""   ,  [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]   ,  )
def       snake_case_ (__A	:		Tuple   ,  __A	:		List[Any]   ,  __A	:		Optional[int]	)     ->      List[str]:
   __lowerCAmelCase    :     Union[str, Any]          =		tmp_path / """cache"""
   __lowerCAmelCase    :     Dict          =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase    :     List[str]          =		features.copy() if features else default_expected_features
   __lowerCAmelCase    :     Any          =		(
       Features({feature: Value(__A	) for feature, dtype in features.items()}	) if features is not None else None
   )
   __lowerCAmelCase    :     Dict          =		ParquetDatasetReader(__A   ,  features=__A   ,  cache_dir=__A	).read()
   _check_parquet_dataset(__A   ,  __A	)





@pytest.mark.parametrize("""split"""   ,  [None, NamedSplit("""train"""	), """train""", """test"""]	)
def       snake_case_ (__A	:		Tuple   ,  __A	:		int   ,  __A	:		List[Any]	)     ->      Union[str, Any]:
   __lowerCAmelCase    :     str          =		tmp_path / """cache"""
   __lowerCAmelCase    :     List[Any]          =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase    :     Optional[Any]          =		ParquetDatasetReader(__A   ,  cache_dir=__A   ,  split=__A	).read()
   _check_parquet_dataset(__A   ,  __A	)
   assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""   ,  [str, list]	)
def       snake_case_ (__A	:		int   ,  __A	:		Any   ,  __A	:		Tuple	)     ->      List[Any]:
   if issubclass(__A   ,  __A	):
      __lowerCAmelCase    :     Dict          =		parquet_path
   elif issubclass(__A   ,  __A	):
      __lowerCAmelCase    :     str          =		[parquet_path]
   __lowerCAmelCase    :     str          =		tmp_path / """cache"""
   __lowerCAmelCase    :     Tuple          =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase    :     List[str]          =		ParquetDatasetReader(__A   ,  cache_dir=__A	).read()
   _check_parquet_dataset(__A   ,  __A	)





def       snake_case_ (__A	:		int   ,  __A	:		str   ,  __A	:		Dict=("train",)	)     ->      Any:
   assert isinstance(__A   ,  __A	)
   for split in splits:
      __lowerCAmelCase    :     Tuple          =		dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""   ,  [False, True]	)
def       snake_case_ (__A	:		Optional[int]   ,  __A	:		Optional[int]   ,  __A	:		Any	)     ->      int:
   __lowerCAmelCase    :     Optional[int]          =		tmp_path / """cache"""
   __lowerCAmelCase    :     Any          =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase    :     str          =		ParquetDatasetReader(
          {"""train""": parquet_path}   ,  cache_dir=__A   ,  keep_in_memory=__A	).read()
   _check_parquet_datasetdict(__A   ,  __A	)





@pytest.mark.parametrize(
    """features"""   ,  [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]   ,  )
def       snake_case_ (__A	:		Dict   ,  __A	:		List[Any]   ,  __A	:		int	)     ->      Tuple:
   __lowerCAmelCase    :     Optional[int]          =		tmp_path / """cache"""
   __lowerCAmelCase    :     List[Any]          =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase    :     List[str]          =		features.copy() if features else default_expected_features
   __lowerCAmelCase    :     Optional[Any]          =		(
       Features({feature: Value(__A	) for feature, dtype in features.items()}	) if features is not None else None
   )
   __lowerCAmelCase    :     Union[str, Any]          =		ParquetDatasetReader({"""train""": parquet_path}   ,  features=__A   ,  cache_dir=__A	).read()
   _check_parquet_datasetdict(__A   ,  __A	)





@pytest.mark.parametrize("""split"""   ,  [None, NamedSplit("""train"""	), """train""", """test"""]	)
def       snake_case_ (__A	:		str   ,  __A	:		int   ,  __A	:		str	)     ->      List[Any]:
   if split:
      __lowerCAmelCase    :     Optional[int]          =		{split: parquet_path}
   else:
      __lowerCAmelCase    :     int          =		"""train"""
      __lowerCAmelCase    :     Dict          =		{"""train""": parquet_path, """test""": parquet_path}
   __lowerCAmelCase    :     Dict          =		tmp_path / """cache"""
   __lowerCAmelCase    :     Optional[Any]          =		{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase    :     List[str]          =		ParquetDatasetReader(__A   ,  cache_dir=__A	).read()
   _check_parquet_datasetdict(__A   ,  __A   ,  splits=list(path.keys()	)	)
   assert all(dataset[split].split == split for split in path.keys()	)





def       snake_case_ (__A	:		Any   ,  __A	:		List[str]	)     ->      Optional[int]:
   __lowerCAmelCase    :     str          =		ParquetDatasetWriter(__A   ,  tmp_path / """foo.parquet"""	)
   assert writer.write() > 0
   __lowerCAmelCase    :     Any          =		pq.ParquetFile(tmp_path / """foo.parquet"""	)
   __lowerCAmelCase    :     Optional[int]          =		pf.read()
   assert dataset.data.table == output_table





def       snake_case_ (__A	:		int   ,  __A	:		Union[str, Any]	)     ->      int:
   __lowerCAmelCase    :     str          =		str(shared_datadir / """test_image_rgb.jpg"""	)
   __lowerCAmelCase    :     Tuple          =		{"""image""": [image_path]}
   __lowerCAmelCase    :     str          =		Features({"""image""": Image()}	)
   __lowerCAmelCase    :     Any          =		Dataset.from_dict(__A   ,  features=__A	)
   __lowerCAmelCase    :     int          =		ParquetDatasetWriter(__A   ,  tmp_path / """foo.parquet"""	)
   assert writer.write() > 0

   __lowerCAmelCase    :     List[str]          =		Dataset.from_parquet(str(tmp_path / """foo.parquet"""	)	)
   assert dataset.features == reloaded_dataset.features

   __lowerCAmelCase    :     Any          =		ParquetDatasetReader(str(tmp_path / """foo.parquet"""	)   ,  streaming=__A	).read()
   assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    """feature, expected"""   ,  [
        (Features({"""foo""": Value("""int32"""	)}	), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""	)}	), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()	)}	), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]   ,  )
def       snake_case_ (__A	:		str   ,  __A	:		Any	)     ->      Union[str, Any]:
   assert get_writer_batch_size(__A	) == expected








# 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 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										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
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

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

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




