





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'''
    UpperCamelCase			   =			42

    # setable values
    UpperCamelCase			   =			42
    UpperCamelCase			   =			42
    UpperCamelCase			   =			42
    UpperCamelCase			   =			None

    # running values
    UpperCamelCase			   =			None


    @classmethod
    def       snake_case_(		cls			,		A			,		A			,		A			,		A	)		->       Optional[int]:
        return cls(common=snake_case_			,		init_noise_sigma=snake_case_			,		timesteps=snake_case_			,		sigmas=snake_case_	)




@dataclass
class  a_					(    A__	):

    '''simple docstring'''
    UpperCamelCase			   =			42




class  a_					(    A__				,   A__	):

    '''simple docstring'''
    UpperCamelCase			   =			[e.name for e in FlaxKarrasDiffusionSchedulers]

    UpperCamelCase			   =			42


    @property
    def       snake_case_(		self	)		->       int:
        return True


    @register_to_config
    def __init__(		self			,		A = 1000			,		A = 0.0001			,		A = 0.02			,		A = "linear"			,		A = None			,		A = "epsilon"			,		A = jnp.floataa			,		)		->       int:
        _SCREAMING_SNAKE_CASE     =      dtype


    def       snake_case_(		self			,		A = None	)		->       LMSDiscreteSchedulerState:
        if common is None:
            _SCREAMING_SNAKE_CASE     =      CommonSchedulerState.create(self	)

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

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

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


    def       snake_case_(		self			,		A			,		A			,		A	)		->       jnp.ndarray:
        ((_SCREAMING_SNAKE_CASE      )    ,	      )     =      jnp.where(state.timesteps == timestep			,		size=1	)
        _SCREAMING_SNAKE_CASE     =      step_index[0]

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


    def       snake_case_(		self			,		A			,		A			,		A			,		A	)		->       Any:

        def lms_derivative(A	):
            _SCREAMING_SNAKE_CASE     =      1.0
            for k in range(snake_case_	):
                if current_order == k:
                    continue
                prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
            return prod

        _SCREAMING_SNAKE_CASE     =      integrate.quad(snake_case_			,		state.sigmas[t]			,		state.sigmas[t + 1]			,		epsrel=1e-4	)[0]

        return integrated_coeff


    def       snake_case_(		self			,		A			,		A			,		A = ()	)		->       LMSDiscreteSchedulerState:
        _SCREAMING_SNAKE_CASE     =      jnp.linspace(self.config.num_train_timesteps - 1			,		0			,		snake_case_			,		dtype=self.dtype	)

        _SCREAMING_SNAKE_CASE     =      jnp.floor(snake_case_	).astype(jnp.intaa	)
        _SCREAMING_SNAKE_CASE     =      jnp.ceil(snake_case_	).astype(jnp.intaa	)

        _SCREAMING_SNAKE_CASE     =      jnp.mod(snake_case_			,		1.0	)

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

        _SCREAMING_SNAKE_CASE     =      timesteps.astype(jnp.intaa	)

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

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


    def       snake_case_(		self			,		A			,		A			,		A			,		A			,		A = 4			,		A = True			,		)		->       Union[FlaxLMSSchedulerOutput, 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"""	)

        _SCREAMING_SNAKE_CASE     =      state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            _SCREAMING_SNAKE_CASE     =      sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
            # * c_out + input * c_skip
            _SCREAMING_SNAKE_CASE     =      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
        _SCREAMING_SNAKE_CASE     =      (sample - pred_original_sample) / sigma
        _SCREAMING_SNAKE_CASE     =      state.replace(derivatives=jnp.append(state.derivatives			,		snake_case_	)	)
        if len(state.derivatives	) > order:
            _SCREAMING_SNAKE_CASE     =      state.replace(derivatives=jnp.delete(state.derivatives			,		0	)	)

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

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

        if not return_dict:
            return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=snake_case_			,		state=snake_case_	)


    def       snake_case_(		self			,		A			,		A			,		A			,		A			,		)		->       jnp.ndarray:
        _SCREAMING_SNAKE_CASE     =      state.sigmas[timesteps].flatten()
        _SCREAMING_SNAKE_CASE     =      broadcast_to_shape_from_left(snake_case_			,		noise.shape	)

        _SCREAMING_SNAKE_CASE     =      original_samples + noise * sigma

        return noisy_samples





    def __len__(		self	)		->       Union[str, Any]:
        return self.config.num_train_timesteps










'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->int:
    return len_board * len_board_column * row + column





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   set[int] ,	)						->bool:

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__lowerCamelCase   ) - 1:
        return True

    _SCREAMING_SNAKE_CASE     =      [(0, 1), (0, -1), (-1, 0), (1, 0)]
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for direction in traverts_directions:
        _SCREAMING_SNAKE_CASE     =      row + direction[0]
        _SCREAMING_SNAKE_CASE     =      column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        _SCREAMING_SNAKE_CASE     =      get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
        if key in visited_points_set:
            continue

        visited_points_set.add(__lowerCamelCase   )
        if exits_word(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	word_index + 1 ,	__lowerCamelCase   ):
            return True

        visited_points_set.remove(__lowerCamelCase   )

    return False





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str   )						->bool:
    _SCREAMING_SNAKE_CASE     =      (
        """The board should be a non empty matrix of single chars strings."""
    )

    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(__lowerCamelCase   )

    for row in board:
        if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
            raise ValueError(__lowerCamelCase   )

        for item in row:
            if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) != 1:
                raise ValueError(__lowerCamelCase   )

    # Validate word
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(
            """The word parameter should be a string of length greater than 0."""   )

    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for i in range(__lowerCamelCase   ):
        for j in range(__lowerCamelCase   ):
            if exits_word(
                __lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	0 ,	{get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )}   ):
                return True

    return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()



