




import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
		import sqlitea

		import sqlalchemy




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  List[str]      ,   __lowerCamelCase       :  Union[str, "sqlalchemy.sql.Selectable"]      ,   __lowerCamelCase       :  Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]      ,   __lowerCamelCase       :  Optional[Features] = None      ,   __lowerCamelCase       :  str = None      ,   __lowerCamelCase       :  bool = False      ,   **__lowerCamelCase       :  int      ,   )      ->  Optional[Any]:
							super().__init__(features=__lowerCamelCase      ,   cache_dir=__lowerCamelCase      ,   keep_in_memory=__lowerCamelCase      ,   **__lowerCamelCase							)
							a					=    Sql(
							    cache_dir=__lowerCamelCase      ,   features=__lowerCamelCase      ,   sql=__lowerCamelCase      ,   con=__lowerCamelCase      ,   **__lowerCamelCase      ,   )





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Union[str, Any]:
							a					=    None
							a					=    None
							a					=    None
							a					=    None

							self.builder.download_and_prepare(
							    download_config=__lowerCamelCase      ,   download_mode=__lowerCamelCase      ,   verification_mode=__lowerCamelCase      ,   base_path=__lowerCamelCase      ,   )

							# Build dataset for splits
							a					=    self.builder.as_dataset(
							    split="train"      ,   verification_mode=__lowerCamelCase      ,   in_memory=self.keep_in_memory							)
							return dataset




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  Dataset      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  Optional[int] = None      ,   **__lowerCamelCase       :  List[Any]      ,   )      ->  Union[str, Any]:
							if num_proc is not None and num_proc <= 0:
										raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""							)

							a					=    dataset
							a					=    name
							a					=    con
							a					=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							a					=    num_proc
							a					=    to_sql_kwargs





				def 							__UpperCAmelCase							(   self       :  str							)      ->  int:
							a					=    self.to_sql_kwargs.pop("sql"      ,   __lowerCamelCase							)
							a					=    self.to_sql_kwargs.pop("con"      ,   __lowerCamelCase							)
							a					=    self.to_sql_kwargs.pop("index"      ,   __lowerCamelCase							)

							a					=    self._write(index=__lowerCamelCase      ,   **self.to_sql_kwargs							)
							return written





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Tuple							)      ->  Optional[int]:
							a					,	a					,	a					=    args
							a					=    {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
							a					=    query_table(
							    table=self.dataset.data      ,   key=slice(__lowerCamelCase      ,   offset + self.batch_size							)      ,   indices=self.dataset._indices      ,   )
							a					=    batch.to_pandas()
							a					=    df.to_sql(self.name      ,   self.con      ,   index=__lowerCamelCase      ,   **__lowerCamelCase							)
							return num_rows or len(__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  int      ,   **__lowerCamelCase       :  int							)      ->  int:
							a					=    0

							if self.num_proc is None or self.num_proc == 1:
										for offset in logging.tqdm(
										    range(0      ,   len(self.dataset							)      ,   self.batch_size							)      ,   unit="ba"      ,   disable=not logging.is_progress_bar_enabled()      ,   desc="Creating SQL from Arrow format"      ,   ):
													written += self._batch_sql((offset, index, to_sql_kwargs)							)
							else:
										a					,	a					=    len(self.dataset							), self.batch_size
										with multiprocessing.Pool(self.num_proc							) as pool:
													for num_rows in logging.tqdm(
													    pool.imap(
													        self._batch_sql      ,   [(offset, index, to_sql_kwargs) for offset in range(0      ,   __lowerCamelCase      ,   __lowerCamelCase							)]      ,   )      ,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size      ,   unit="ba"      ,   disable=not logging.is_progress_bar_enabled()      ,   desc="Creating SQL from Arrow format"      ,   ):
																written += num_rows

							return written












import math

import flax.linen as nn
import jax.numpy as jnp




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      float = 1.0,						):






			'''simple docstring'''







			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
			a					=    float(embedding_dim // 2     )
			a					=    math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			a					=    min_timescale * jnp.exp(jnp.arange(A,						dtype=jnp.floataa     ) * -log_timescale_increment     )
			a					=    jnp.expand_dims(A,						1     ) * jnp.expand_dims(A,						0     )

			# scale embeddings
			a					=    scale * emb

			if flip_sin_to_cos:
						a					=    jnp.concatenate([jnp.cos(A     ), jnp.sin(A     )],						axis=1     )
			else:
						a					=    jnp.concatenate([jnp.sin(A     ), jnp.cos(A     )],						axis=1     )
			a					=    jnp.reshape(A,						[jnp.shape(A     )[0], embedding_dim]     )
			return signal




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






