


'''simple docstring'''




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 _snake_case :
							"""simple docstring"""





							__a				       =  42

							# setable values
							__a				       =  42
							__a				       =  42
							__a				       =  42
							__a				       =  None

							# running values
							__a				       =  None







							@classmethod
							def 		__SCREAMING_SNAKE_CASE( cls      ,					__A      ,					__A      ,					__A      ,					__A						)  ->Tuple:



											"""simple docstring"""







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





@dataclass
class _snake_case (			a__ ):
							"""simple docstring"""





							__a				       =  42





class _snake_case (			a__     , a__ ):
							"""simple docstring"""





							__a				       =  [e.name for e in FlaxKarrasDiffusionSchedulers]

							__a				       =  42







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->Optional[int]:



											"""simple docstring"""







											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      ,					)  ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:   int           =       dtype







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A = None						)  ->LMSDiscreteSchedulerState:



											"""simple docstring"""







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

											__lowerCamelCase		:   Optional[Any]           =       jnp.arange(0      ,					self.config.num_train_timesteps						).round()[::-1]
											__lowerCamelCase		:   List[str]           =       ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

											# standard deviation of the initial noise distribution
											__lowerCamelCase		:   Dict           =       sigmas.max()

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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A						)  ->jnp.ndarray:



											"""simple docstring"""







											((__lowerCamelCase)    ,       )		:   Optional[Any]           =       jnp.where(state.timesteps == timestep      ,					size=1						)
											__lowerCamelCase		:   List[Any]           =       step_index[0]

											__lowerCamelCase		:   Any           =       state.sigmas[step_index]
											__lowerCamelCase		:   str           =       sample / ((sigma**2 + 1) ** 0.5)
											return sample







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A						)  ->List[str]:



											"""simple docstring"""







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

											__lowerCamelCase		:   int           =       integrate.quad(__A      ,					state.sigmas[t]      ,					state.sigmas[t + 1]      ,					epsrel=1E-4						)[0]

											return integrated_coeff







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A = ()						)  ->LMSDiscreteSchedulerState:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[Any]           =       jnp.linspace(self.config.num_train_timesteps - 1      ,					0      ,					__A      ,					dtype=self.dtype						)

											__lowerCamelCase		:   Any           =       jnp.floor(__A						).astype(jnp.intaa						)
											__lowerCamelCase		:   Optional[int]           =       jnp.ceil(__A						).astype(jnp.intaa						)

											__lowerCamelCase		:   Tuple           =       jnp.mod(__A      ,					1.0						)

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

											__lowerCamelCase		:   int           =       timesteps.astype(jnp.intaa						)

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

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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A = 4      ,					__A = 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"						)

											__lowerCamelCase		:   Optional[int]           =       state.sigmas[timestep]

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

											# 3. Compute linear multistep coefficients
											__lowerCamelCase		:   Any           =       min(timestep + 1      ,					__A						)
											__lowerCamelCase		:   Optional[int]           =       [self.get_lms_coefficient(__A      ,					__A      ,					__A      ,					__A						) for curr_order in range(__A						)]

											# 4. Compute previous sample based on the derivatives path
											__lowerCamelCase		:   Union[str, Any]           =       sample + sum(
											    coeff * derivative for coeff, derivative in zip(__A      ,					reversed(state.derivatives						)						)						)

											if not return_dict:
															return (prev_sample, state)

											return FlaxLMSSchedulerOutput(prev_sample=__A      ,					state=__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					)  ->jnp.ndarray:



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       state.sigmas[timesteps].flatten()
											__lowerCamelCase		:   Optional[Any]           =       broadcast_to_shape_from_left(__A      ,					noise.shape						)

											__lowerCamelCase		:   Dict           =       original_samples + noise * sigma

											return noisy_samples







							def __len__( self						)  ->List[Any]:



											"""simple docstring"""







											return self.config.num_train_timesteps









'''simple docstring'''




from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       word
											__lowerCamelCase		:   Optional[Any]           =       count







							def __eq__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count == other.count







							def __lt__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count < other.count





def 				a_							(    lowercase				,    lowercase     )      ->list[str]:






				'''simple docstring'''

				__lowerCamelCase		:   Heap[WordCount]           =       Heap()
				__lowerCamelCase		:   int           =       Counter(lowercase     )
				heap.build_max_heap(
				    [WordCount(lowercase				,    lowercase     ) for word, count in count_by_word.items()]     )
				return [heap.extract_max().word for _ in range(min(lowercase				,    len(lowercase     )     )     )]


if __name__ == "__main__":
				import doctest

				doctest.testmod()





