

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 					A		:







    """simple docstring"""





    lowerCamelCase						      =				42

    # setable values
    lowerCamelCase						      =				42
    lowerCamelCase						      =				42
    lowerCamelCase						      =				42
    lowerCamelCase						      =				None

    # running values
    lowerCamelCase						      =				None


    @classmethod
    def 		snake_case__	( cls					:			List[Any],lowercase_					:			CommonSchedulerState,lowercase_					:			jnp.ndarray,lowercase_					:			jnp.ndarray,lowercase_					:			jnp.ndarray						)->       Union[str, Any]:





        '''simple docstring'''







        return cls(common=lowercase_,init_noise_sigma=lowercase_,timesteps=lowercase_,sigmas=lowercase_						)




@dataclass
class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				42




class 					A		(				_UpperCAmelCase       ,				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

    lowerCamelCase						      =				42


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





        '''simple docstring'''







        return True


    @register_to_config
    def __init__( self					:			int,lowercase_					:			int = 1_0_0_0,lowercase_					:			float = 0.0_001,lowercase_					:			float = 0.02,lowercase_					:			str = "linear",lowercase_					:			Optional[jnp.ndarray] = None,lowercase_					:			str = "epsilon",lowercase_					:			jnp.dtype = jnp.floataa,)->       int:





        '''simple docstring'''







        A__        =  dtype


    def 		snake_case__	( self					:			Tuple,lowercase_					:			Optional[CommonSchedulerState] = None						)->       LMSDiscreteSchedulerState:





        '''simple docstring'''







        if common is None:
            A__        =  CommonSchedulerState.create(self						)

        A__        =  jnp.arange(0,self.config.num_train_timesteps						).round()[::-1]
        A__        =  ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        A__        =  sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowercase_,init_noise_sigma=lowercase_,timesteps=lowercase_,sigmas=lowercase_,)


    def 		snake_case__	( self					:			Optional[int],lowercase_					:			LMSDiscreteSchedulerState,lowercase_					:			jnp.ndarray,lowercase_					:			int						)->       jnp.ndarray:





        '''simple docstring'''







        ((A__) ,		)        =  jnp.where(state.timesteps == timestep,size=1						)
        A__        =  step_index[0]

        A__        =  state.sigmas[step_index]
        A__        =  sample / ((sigma**2 + 1) ** 0.5)
        return sample


    def 		snake_case__	( self					:			List[Any],lowercase_					:			LMSDiscreteSchedulerState,lowercase_					:			Optional[Any],lowercase_					:			List[str],lowercase_					:			Union[str, Any]						)->       List[str]:





        '''simple docstring'''







        def lms_derivative(lowercase_					:			List[Any]						):
            A__        =  1.0
            for k in range(lowercase_						):
                if current_order == k:
                    continue
                prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
            return prod

        A__        =  integrate.quad(lowercase_,state.sigmas[t],state.sigmas[t + 1],epsrel=1E-4						)[0]

        return integrated_coeff


    def 		snake_case__	( self					:			List[Any],lowercase_					:			LMSDiscreteSchedulerState,lowercase_					:			int,lowercase_					:			Tuple = ()						)->       LMSDiscreteSchedulerState:





        '''simple docstring'''







        A__        =  jnp.linspace(self.config.num_train_timesteps - 1,0,lowercase_,dtype=self.dtype						)

        A__        =  jnp.floor(lowercase_						).astype(jnp.intaa						)
        A__        =  jnp.ceil(lowercase_						).astype(jnp.intaa						)

        A__        =  jnp.mod(lowercase_,1.0						)

        A__        =  ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        A__        =  (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        A__        =  jnp.concatenate([sigmas, jnp.array([0.0],dtype=self.dtype						)]						)

        A__        =  timesteps.astype(jnp.intaa						)

        # initial running values
        A__        =  jnp.zeros((0,) + shape,dtype=self.dtype						)

        return state.replace(
            timesteps=lowercase_,sigmas=lowercase_,num_inference_steps=lowercase_,derivatives=lowercase_,)


    def 		snake_case__	( self					:			Any,lowercase_					:			LMSDiscreteSchedulerState,lowercase_					:			jnp.ndarray,lowercase_					:			int,lowercase_					:			jnp.ndarray,lowercase_					:			int = 4,lowercase_					:			bool = True,)->       Union[FlaxLMSSchedulerOutput, Tuple]:





        '''simple docstring'''







        if state.num_inference_steps is None:
            raise ValueError(
                'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

        A__        =  state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            A__        =  sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
            # * c_out + input * c_skip
            A__        =  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
            raise ValueError(
                F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'						)

        # 2. Convert to an ODE derivative
        A__        =  (sample - pred_original_sample) / sigma
        A__        =  state.replace(derivatives=jnp.append(state.derivatives,lowercase_						)						)
        if len(state.derivatives						) > order:
            A__        =  state.replace(derivatives=jnp.delete(state.derivatives,0						)						)

        # 3. Compute linear multistep coefficients
        A__        =  min(timestep + 1,lowercase_						)
        A__        =  [self.get_lms_coefficient(lowercase_,lowercase_,lowercase_,lowercase_						) for curr_order in range(lowercase_						)]

        # 4. Compute previous sample based on the derivatives path
        A__        =  sample + sum(
            coeff * derivative for coeff, derivative in zip(lowercase_,reversed(state.derivatives						)						)						)

        if not return_dict:
            return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowercase_,state=lowercase_						)


    def 		snake_case__	( self					:			Union[str, Any],lowercase_					:			LMSDiscreteSchedulerState,lowercase_					:			jnp.ndarray,lowercase_					:			jnp.ndarray,lowercase_					:			jnp.ndarray,)->       jnp.ndarray:





        '''simple docstring'''







        A__        =  state.sigmas[timesteps].flatten()
        A__        =  broadcast_to_shape_from_left(lowercase_,noise.shape						)

        A__        =  original_samples + noise * sigma

        return noisy_samples


    def __len__( self					:			Dict						)->       Any:





        '''simple docstring'''







        return self.config.num_train_timesteps




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


lowercase_									=    datasets.utils.logging.get_logger(__name__)


@dataclass
class 					A		(				datasets.BuilderConfig					):







    """simple docstring"""





    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				True
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				1_00_00
    lowerCamelCase						      =				None


    def 		snake_case__	( self					:			Tuple						)->       Optional[int]:





        '''simple docstring'''







        if self.sql is None:
            raise ValueError('sql must be specified'						)
        if self.con is None:
            raise ValueError('con must be specified'						)


    def 		snake_case__	( self					:			Dict,lowercase_					:			dict,lowercase_					:			Optional[datasets.Features] = None,)->       str:





        '''simple docstring'''







        A__        =  config_kwargs.copy()
        # We need to stringify the Selectable object to make its hash deterministic

        # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
        A__        =  config_kwargs['sql']
        if not isinstance(lowercase_,lowercase_						):
            if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                import sqlalchemy

                if isinstance(lowercase_,sqlalchemy.sql.Selectable						):
                    A__        =  sqlalchemy.create_engine(config_kwargs['con'].split('://'						)[0] + '://'						)
                    A__        =  str(sql.compile(dialect=engine.dialect						)						)
                    A__        =  sql_str
                else:
                    raise TypeError(
                        F'Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowercase_						)}: {sql}'						)
            else:
                raise TypeError(
                    F'Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowercase_						)}: {sql}'						)
        A__        =  config_kwargs['con']
        if not isinstance(lowercase_,lowercase_						):
            A__        =  id(lowercase_						)
            logger.info(
                F'SQL connection \'con\' of type {type(lowercase_						)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'						)

        return super().create_config_id(lowercase_,custom_features=lowercase_						)


    @property
    def 		snake_case__	( self					:			Union[str, Any]						)->       Any:





        '''simple docstring'''







        A__        =  {
            'index_col': self.index_col,
            'columns': self.columns,
            'params': self.params,
            'coerce_float': self.coerce_float,
            'parse_dates': self.parse_dates,
        }
        return pd_read_sql_kwargs




class 					A		(				datasets.ArrowBasedBuilder					):







    """simple docstring"""





    lowerCamelCase						      =				SqlConfig


    def 		snake_case__	( self					:			Any						)->       Any:





        '''simple docstring'''







        return datasets.DatasetInfo(features=self.config.features						)


    def 		snake_case__	( self					:			Tuple,lowercase_					:			List[Any]						)->       Union[str, Any]:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Any,lowercase_					:			pa.Table						)->       pa.Table:





        '''simple docstring'''







        if self.config.features is not None:
            A__        =  self.config.features.arrow_schema
            if all(not require_storage_cast(lowercase_						) for feature in self.config.features.values()						):
                # cheaper cast
                A__        =  pa.Table.from_arrays([pa_table[field.name] for field in schema],schema=lowercase_						)
            else:
                # more expensive cast; allows str <-> int/float or str to Audio for example
                A__        =  table_cast(lowercase_,lowercase_						)
        return pa_table


    def 		snake_case__	( self					:			List[str]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  self.config.chunksize
        A__        =  pd.read_sql(
            self.config.sql,self.config.con,chunksize=lowercase_,**self.config.pd_read_sql_kwargs						)
        A__        =  [sql_reader] if chunksize is None else sql_reader
        for chunk_idx, df in enumerate(lowercase_						):
            A__        =  pa.Table.from_pandas(lowercase_						)
            yield chunk_idx, self._cast_table(lowercase_						)

