


import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


_snake_case   = "none"
_snake_case   = "none"







def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				Dict		  =			value.split(A__ )
     return len(A__ ) > 1 and any((len(A__ ) == 3 for x in split) )







def   A   (		A__ ):
     if isinstance(A__		,		A__ ):
          return value
     if isinstance(A__		,		A__ ):
          return float(A__ )
     if not isinstance(A__		,		A__ ):
          raise ValueError("Argument value is not a string. Can't parse it as float" )
     UpperCAmelCase						:				List[Any]		  =			value

     try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
               return float(sanitized.replace(","		,		"" ) )
          # 1,000
          if "," in sanitized and _split_thousands(","		,		A__ ):
               return float(sanitized.replace(","		,		"" ) )
          # 5,5556
          if "," in sanitized and sanitized.count("," ) == 1 and not _split_thousands(","		,		A__ ):
               return float(sanitized.replace(","		,		"." ) )
          # 0.0.0.1
          if sanitized.count("." ) > 1:
               return float(sanitized.replace("."		,		"" ) )
          # 0,0,0,1
          if sanitized.count("," ) > 1:
               return float(sanitized.replace(","		,		"" ) )
          return float(A__ )
     except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError("Unable to convert value to float" )







def   A   (		A__ ):
     if answer is None:
          return None
     try:
          UpperCAmelCase						:				int		  =			convert_to_float(A__ )
          if isinstance(A__		,		A__ ) and math.isnan(A__ ):
               return None
          return value
     except ValueError:
          return answer.lower()


_snake_case   = {
    "text": lambda x: x,
    "real": convert_to_float,
}




class    A_   ( enum.Enum     ):







 """simple docstring"""





 _UpperCamelCase				: List[str]      =					0
 _UpperCamelCase				: Union[str, Any]      =					1
 _UpperCamelCase				: List[str]      =					2
 _UpperCamelCase				: str      =					3
 _UpperCamelCase				: List[str]      =					4
 _UpperCamelCase				: Tuple      =					5




class    A_   ( enum.Enum     ):







 """simple docstring"""





 _UpperCamelCase				: List[str]      =					0
 _UpperCamelCase				: Dict      =					1
 _UpperCamelCase				: Optional[Any]      =					2




@dataclasses.dataclass
class    A_   :







 """simple docstring"""





 _UpperCamelCase				: Text
 _UpperCamelCase				: _Operator
 _UpperCamelCase				: Any
_snake_case   = re.compile(r"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)







def   A   (		A__ ):
     return list(_TOKENIZER.findall(x.lower() ) )







def   A   (		A__		,		A__		,		A__ ):
     if operator == _Operator.EQUALS:
          return src == tgt
     elif operator == _Operator.GREATER:
          return src > tgt
     elif operator == _Operator.LESSER:
          return src < tgt
     raise ValueError(f'Unknown operator: {operator}' )







def   A   (		A__		,		A__		,		A__ ):
     UpperCAmelCase						:				Optional[int]		  =			table["types"]
     return _TYPE_CONVERTER[types[column]](A__ )







def   A   (		A__ ):
     return isinstance(A__		,		A__ )







def   A   (		A__		,		A__		,		A__ ):
     for cond in conditions:
          UpperCAmelCase						:				str		  =			row[cond.column]

          UpperCAmelCase						:				Optional[int]		  =			_parse_value(A__		,		cond.column		,		cond.cmp_value )

          if _is_string(A__ ) and _is_string(A__ ):
               UpperCAmelCase						:				str		  =			_normalize_for_match(A__ )
               UpperCAmelCase						:				List[Any]		  =			_normalize_for_match(A__ )

          if not isinstance(A__		,		type(A__ ) ):
               raise ValueError("Type difference {} != {}".format(type(A__ )		,		type(A__ ) ) )

          if not _compare(cond.operator		,		A__		,		A__ ):
               return False
     return True







def   A   (		A__		,		A__		,		A__ ):
     if not answer_coordinates:
          if aggregation_op == _Aggregation.COUNT:
               return 0.0
          else:
               return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
     if aggregation_op == _Aggregation.COUNT:
          return float(len(A__ ) )

     # If we have just one answer, if float returns it or try a conversion.
     UpperCAmelCase						:				str		  =			[table["rows"][i][j] for (i, j) in answer_coordinates]
     if len(A__ ) == 1:
          try:
               return convert_to_float(values[0] )
          except ValueError as e:
               if aggregation_op != _Aggregation.NONE:
                    raise e

     if aggregation_op == _Aggregation.NONE:
          return None

     # Other aggregation only support numeric values. Bail out if we have strings.
     if not all((isinstance(A__		,		(int, float) ) for v in values) ):
          return None

     if aggregation_op == _Aggregation.SUM:
          return float(sum(A__ ) )
     elif aggregation_op == _Aggregation.AVERAGE:
          return sum(A__ ) / len(A__ )
     else:
          raise ValueError(f'Unknown aggregation: {aggregation_op}' )







def   A   (		A__		,		A__ ):
     UpperCAmelCase						:				Optional[Any]		  =			sql_query["agg"]
     if aggregation_op_index >= 3:
          UpperCAmelCase						:				List[str]		  =			_Aggregation(A__ )
     else:
          UpperCAmelCase						:				Union[str, Any]		  =			_Aggregation.NONE

     UpperCAmelCase						:				str		  =			sql_query["sel"]
     UpperCAmelCase						:				Any		  =			[
         _Condition(A__		,		_Operator(A__ )		,		A__ )
         for column, operator, cmp_value in zip(
             sql_query["conds"]["column_index"]		,		sql_query["conds"]["operator_index"]		,		sql_query["conds"]["condition"] )
     ]

     UpperCAmelCase						:				Any		  =			[]
     for row in range(len(table["rows"] ) ):
          if _respect_conditions(A__		,		table["rows"][row]		,		A__ ):
               indices.append((row, target_column) )

     if not indices:
          return [], aggregation_op

     if len(A__ ) == 1:
          return indices, aggregation_op

     # Parsing of MIN/MAX.
     if aggregation_op_index in (1, 2):
          UpperCAmelCase						:				List[str]		  =			{2: min, 1: max}
          UpperCAmelCase						:				int		  =			[(table["rows"][i][j], index) for index, (i, j) in enumerate(A__ )]
          UpperCAmelCase						:				Any		  =			functools.reduce(operators[sql_query["agg"]]		,		A__ )

          UpperCAmelCase						:				Optional[int]		  =			[indices[reduced[1]]]
          return ret, _Aggregation.NONE

     return indices, aggregation_op







def   A   (		A__		,		A__		,		A__ ):
     if float_answer is not None:
          return [str(A__ )]
     return [str(table["real_rows"][r][c] ) for r, c in answer_coordinates]







def   A   (		A__		,		A__ ):
     UpperCAmelCase  , UpperCAmelCase						:				Optional[int]		  =			_get_answer_coordinates(A__		,		A__ )
     UpperCAmelCase						:				int		  =			_get_float_answer(A__		,		A__		,		A__ )
     UpperCAmelCase						:				List[Any]		  =			_get_answer_text(A__		,		A__		,		A__ )
     # keep the original data the same with TaPas
     if len(A__ ) == 0:
          UpperCAmelCase						:				List[str]		  =			[EMPTY_ANSWER]
     return answer_text







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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    A_   :







 """simple docstring"""





 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 @classmethod
 def   _UpperCamelCase    (			cls					: Union[str, Any]				)    ->			int:
      return cls()




@dataclass
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: ScoreSdeVeSchedulerState
 _UpperCamelCase				: jnp.ndarray
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 @property
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[str]:
      return True
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 2_0_0_0       ,    _a					: float = 0.1_5       ,    _a					: float = 0.0_1       ,    _a					: float = 1_3_4_8.0       ,    _a					: float = 1E-5       ,    _a					: int = 1       ,    )    ->			List[Any]:
      pass
 def   _UpperCamelCase    (			self					: int				)    ->			List[Any]:
      UpperCAmelCase						:				str		  =			ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _a       ,    self.config.num_train_timesteps       ,    self.config.sigma_min       ,    self.config.sigma_max       ,    self.config.sampling_eps       ,    )
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: Tuple = ()       ,    _a					: float = None				)    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase						:				Dict		  =			jnp.linspace(1       ,    _a       ,    _a				)
      return state.replace(timesteps=_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: float = None       ,    _a					: float = None       ,    _a					: float = None       ,    )    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				str		  =			sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase						:				Any		  =			sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase						:				Any		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase						:				List[Any]		  =			self.set_timesteps(_a       ,    _a       ,    _a				)

      UpperCAmelCase						:				List[Any]		  =			jnp.exp(jnp.linspace(jnp.log(_a				)       ,    jnp.log(_a				)       ,    _a				)				)
      UpperCAmelCase						:				List[str]		  =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

      return state.replace(discrete_sigmas=_a       ,    sigmas=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			List[str]:
      return jnp.where(timesteps == 0       ,    jnp.zeros_like(_a				)       ,    state.discrete_sigmas[timesteps - 1]				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: int       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      UpperCAmelCase						:				Union[str, Any]		  =			timestep * jnp.ones(
          sample.shape[0]       ,    )
      UpperCAmelCase						:				int		  =			(timestep * (len(state.timesteps				) - 1)).long()

      UpperCAmelCase						:				List[str]		  =			state.discrete_sigmas[timesteps]
      UpperCAmelCase						:				Optional[int]		  =			self.get_adjacent_sigma(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Tuple		  =			jnp.zeros_like(_a				)
      UpperCAmelCase						:				List[Any]		  =			(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      UpperCAmelCase						:				Union[str, Any]		  =			diffusion.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				Tuple		  =			drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase						:				List[str]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)
      UpperCAmelCase						:				Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase						:				str		  =			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
           return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    prev_sample_mean=_a       ,    state=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      UpperCAmelCase						:				Optional[int]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)

      # compute step size from the model_output, the noise, and the snr
      UpperCAmelCase						:				str		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				Optional[int]		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				int		  =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      UpperCAmelCase						:				List[Any]		  =			step_size * jnp.ones(sample.shape[0]				)

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase						:				Optional[Any]		  =			step_size.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				int		  =			sample + step_size * model_output
      UpperCAmelCase						:				int		  =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    state=_a				)

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



