code: 
  
  
  
  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
  
  
  __A       					=		'''none'''
  __A       					=		'''none'''
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      Tuple       )   ->				Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     value.split(lowerCAmelCase_       )
  						return len(lowerCAmelCase_       ) > 1 and any((len(lowerCAmelCase_       ) == 3 for x in split)       )
  
  
  def  __a  (					lowerCAmelCase_     :      str       )   ->				Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if isinstance(lowerCAmelCase_    ,lowerCAmelCase_       ):
  												return value
  						if isinstance(lowerCAmelCase_    ,lowerCAmelCase_       ):
  												return float(lowerCAmelCase_       )
  						if not isinstance(lowerCAmelCase_    ,lowerCAmelCase_       ):
  												raise ValueError("""Argument value is not a string. Can't parse it as float"""       )
  						UpperCAmelCase_=     value
  
  						try:
  												# Example: 1,000.7
  												if "." in sanitized and "," in sanitized:
  																		return float(sanitized.replace(""","""    ,""""""       )       )
  												# 1,000
  												if "," in sanitized and _split_thousands(""","""    ,lowerCAmelCase_       ):
  																		return float(sanitized.replace(""","""    ,""""""       )       )
  												# 5,5556
  												if "," in sanitized and sanitized.count(""","""       ) == 1 and not _split_thousands(""","""    ,lowerCAmelCase_       ):
  																		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(lowerCAmelCase_       )
  						except ValueError:
  												# Avoid adding the sanitized value in the error message.
  												raise ValueError("""Unable to convert value to float"""       )
  
  
  def  __a  (					lowerCAmelCase_     :      Any       )   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if answer is None:
  												return None
  						try:
  												UpperCAmelCase_=     convert_to_float(lowerCAmelCase_       )
  												if isinstance(lowerCAmelCase_    ,lowerCAmelCase_       ) and math.isnan(lowerCAmelCase_       ):
  																		return None
  												return value
  						except ValueError:
  												return answer.lower()
  
  
  __A       					=		{
      '''text''': lambda x: x,
      '''real''': convert_to_float,
  }
  
  
  
  
  
  
  class 	lowercase		(				enum.Enum):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[str]	     =							0
  	a__   :      Optional[int]	     =							1
  	a__   :      int	     =							2
  	a__   :      Union[str, Any]	     =							3
  	a__   :      int	     =							4
  	a__   :      Optional[int]	     =							5
  
  
  
  
  
  
  class 	lowercase		(				enum.Enum):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      int	     =							0
  	a__   :      Optional[int]	     =							1
  	a__   :      Tuple	     =							2
  
  
  
  
  
  
  @dataclasses.dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Text
  	a__   :      _Operator
  	a__   :      Any
  
  __A       					=		re.compile(r'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[int]       )   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						return list(_TOKENIZER.findall(x.lower()       )       )
  
  
  def  __a  (					lowerCAmelCase_     :      Any    ,lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      Optional[Any]       )   ->				Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						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  (					lowerCAmelCase_     :      Union[str, Any]    ,lowerCAmelCase_     :      Dict    ,lowerCAmelCase_     :      Tuple       )   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     table["""types"""]
  						return _TYPE_CONVERTER[types[column]](lowerCAmelCase_       )
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[int]       )   ->				List[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						return isinstance(lowerCAmelCase_    ,lowerCAmelCase_       )
  
  
  def  __a  (					lowerCAmelCase_     :      Any    ,lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      str       )   ->				Dict:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						for cond in conditions:
  												UpperCAmelCase_=     row[cond.column]
  
  												UpperCAmelCase_=     _parse_value(lowerCAmelCase_    ,cond.column    ,cond.cmp_value       )
  
  												if _is_string(lowerCAmelCase_       ) and _is_string(lowerCAmelCase_       ):
  																		UpperCAmelCase_=     _normalize_for_match(lowerCAmelCase_       )
  																		UpperCAmelCase_=     _normalize_for_match(lowerCAmelCase_       )
  
  												if not isinstance(lowerCAmelCase_    ,type(lowerCAmelCase_       )       ):
  																		raise ValueError("""Type difference {} != {}""".format(type(lowerCAmelCase_       )    ,type(lowerCAmelCase_       )       )       )
  
  												if not _compare(cond.operator    ,lowerCAmelCase_    ,lowerCAmelCase_       ):
  																		return False
  						return True
  
  
  def  __a  (					lowerCAmelCase_     :      int    ,lowerCAmelCase_     :      List[Any]    ,lowerCAmelCase_     :      str       )   ->				str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						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(lowerCAmelCase_       )       )
  
  						# If we have just one answer, if float returns it or try a conversion.
  						UpperCAmelCase_=     [table["""rows"""][i][j] for (i, j) in answer_coordinates]
  						if len(lowerCAmelCase_       ) == 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(lowerCAmelCase_    ,(int, float)       ) for v in values)       ):
  												return None
  
  						if aggregation_op == _Aggregation.SUM:
  												return float(sum(lowerCAmelCase_       )       )
  						elif aggregation_op == _Aggregation.AVERAGE:
  												return sum(lowerCAmelCase_       ) / len(lowerCAmelCase_       )
  						else:
  												raise ValueError(F"""Unknown aggregation: {aggregation_op}"""       )
  
  
  def  __a  (					lowerCAmelCase_     :      Union[str, Any]    ,lowerCAmelCase_     :      Optional[int]       )   ->				List[str]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     sql_query["""agg"""]
  						if aggregation_op_index >= 3:
  												UpperCAmelCase_=     _Aggregation(lowerCAmelCase_       )
  						else:
  												UpperCAmelCase_=     _Aggregation.NONE
  
  						UpperCAmelCase_=     sql_query["""sel"""]
  						UpperCAmelCase_=     [
  						    _Condition(lowerCAmelCase_    ,_Operator(lowerCAmelCase_       )    ,lowerCAmelCase_       )
  						    for column, operator, cmp_value in zip(
  						        sql_query["""conds"""]["""column_index"""]    ,sql_query["""conds"""]["""operator_index"""]    ,sql_query["""conds"""]["""condition"""]       )
  						]
  
  						UpperCAmelCase_=     []
  						for row in range(len(table["""rows"""]       )       ):
  												if _respect_conditions(lowerCAmelCase_    ,table["""rows"""][row]    ,lowerCAmelCase_       ):
  																		indices.append((row, target_column)       )
  
  						if not indices:
  												return [], aggregation_op
  
  						if len(lowerCAmelCase_       ) == 1:
  												return indices, aggregation_op
  
  						# Parsing of MIN/MAX.
  						if aggregation_op_index in (1, 2):
  												UpperCAmelCase_=     {2: min, 1: max}
  												UpperCAmelCase_=     [(table["""rows"""][i][j], index) for index, (i, j) in enumerate(lowerCAmelCase_       )]
  												UpperCAmelCase_=     functools.reduce(operators[sql_query["""agg"""]]    ,lowerCAmelCase_       )
  
  												UpperCAmelCase_=     [indices[reduced[1]]]
  												return ret, _Aggregation.NONE
  
  						return indices, aggregation_op
  
  
  def  __a  (					lowerCAmelCase_     :      List[Any]    ,lowerCAmelCase_     :      Tuple    ,lowerCAmelCase_     :      int       )   ->				str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if float_answer is not None:
  												return [str(lowerCAmelCase_       )]
  						return [str(table["""real_rows"""][r][c]       ) for r, c in answer_coordinates]
  
  
  
  def  __a  (					lowerCAmelCase_     :      Union[str, Any]    ,lowerCAmelCase_     :      str       )   ->				List:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_,     UpperCAmelCase_=     _get_answer_coordinates(lowerCAmelCase_    ,lowerCAmelCase_       )
  						UpperCAmelCase_=     _get_float_answer(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
  						UpperCAmelCase_=     _get_answer_text(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
  						# keep the original data the same with TaPas
  						if len(lowerCAmelCase_       ) == 0:
  												UpperCAmelCase_=     [EMPTY_ANSWER]
  						return answer_text
  
  
  
  
  
style_context: 
  
  
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  
  
  
  
  @flax.struct.dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      CommonSchedulerState
  	a__   :      jnp.ndarray
  
  	# setable values
  	a__   :      jnp.ndarray
  	a__   :      jnp.ndarray
  	a__   :      Optional[int]	     =							None
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
  							return cls(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      DDIMSchedulerState
  
  
  
  
  
  
  class 	lowercase		(				snake_case__				,					snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  	a__   :      jnp.dtype
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							return True
  
  
  
  
  	@register_to_config
  	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     dtype
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
  							if common is None:
  													UpperCAmelCase_=     CommonSchedulerState.create(self  )
  
  							# At every step in ddim, we are looking into the previous alphas_cumprod
  							# For the final step, there is no previous alphas_cumprod because we are already at 0
  							# `set_alpha_to_one` decides whether we set this parameter simply to one or
  							# whether we use the final alpha of the "non-previous" one.
  							UpperCAmelCase_=     (
  							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  							)
  
  							# standard deviation of the initial noise distribution
  							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )
  
  							UpperCAmelCase_=     jnp.arange(0		,						self.config.num_train_timesteps  ).round()[::-1]
  
  							return DDIMSchedulerState.create(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
  							return sample
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
  							UpperCAmelCase_=     self.config.num_train_timesteps // num_inference_steps
  							# creates integer timesteps by multiplying by ratio
  							# rounding to avoid issues when num_inference_step is power of 3
  							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  							return state.replace(
  							    num_inference_steps=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
  							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(
  							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
  							UpperCAmelCase_=     1 - alpha_prod_t
  							UpperCAmelCase_=     1 - alpha_prod_t_prev
  
  							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  							return variance
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
  							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"""  )
  
  							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  							# Ideally, read DDIM paper in-detail understanding
  
  							# Notation (<variable name> -> <name in paper>
  							# - pred_noise_t -> e_theta(x_t, t)
  							# - pred_original_sample -> f_theta(x_t, t) or x_0
  							# - std_dev_t -> sigma_t
  							# - eta -> η
  							# - pred_sample_direction -> "direction pointing to x_t"
  							# - pred_prev_sample -> "x_t-1"
  
  							# 1. get previous step value (=t-1)
  							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  							UpperCAmelCase_=     state.common.alphas_cumprod
  							UpperCAmelCase_=     state.final_alpha_cumprod
  
  							# 2. compute alphas, betas
  							UpperCAmelCase_=     alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     1 - alpha_prod_t
  
  							# 3. compute predicted original sample from predicted noise also called
  							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							if self.config.prediction_type == "epsilon":
  													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  													UpperCAmelCase_=     model_output
  							elif self.config.prediction_type == "sample":
  													UpperCAmelCase_=     model_output
  													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  							elif self.config.prediction_type == "v_prediction":
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  							else:
  													raise ValueError(
  													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
  													    """ `v_prediction`"""  )
  
  							# 4. compute variance: "sigma_t(η)" -> see formula (16)
  							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     eta * variance ** 0.5
  
  							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  							if not return_dict:
  													return (prev_sample, state)
  
  							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
  							return self.config.num_train_timesteps
  
  
  
  
  