
"""simple docstring"""




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


a		:       Any     						=       'scheduler_config.json'







class     lowercase(_lowercase       ):
     __snake_case:	List[str]						=     1
     __snake_case:	Optional[Any]						=     2
     __snake_case:	List[Any]						=     3
     __snake_case:	Union[str, Any]						=     4
     __snake_case:	Tuple						=     5







@dataclass
class     lowercase(_lowercase       ):
     __snake_case:	jnp.ndarray







class     lowercase:
     __snake_case:	List[str]						=     SCHEDULER_CONFIG_NAME
     __snake_case:	Dict						=     ['dtype']
     __snake_case:	Any						=     []
     __snake_case:	Dict						=     True



     @classmethod
     def 			lowercase__  (							cls     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE=False     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     List[Any]:







         """simple docstring"""



         a__	,     a__		=			cls.load_config(
             pretrained_model_name_or_path=__SCREAMING_SNAKE_CASE     ,				subfolder=__SCREAMING_SNAKE_CASE     ,				return_unused_kwargs=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)
         a__	,     a__		=			cls.from_config(__SCREAMING_SNAKE_CASE     ,				return_unused_kwargs=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )

         if hasattr(__SCREAMING_SNAKE_CASE     ,				'create_state'   ) and getattr(__SCREAMING_SNAKE_CASE     ,				'has_state'     ,				__SCREAMING_SNAKE_CASE   ):
             a__		=			scheduler.create_state()

         if return_unused_kwargs:
             return scheduler, state, unused_kwargs

         return scheduler, state



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				**__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         self.save_config(save_directory=__SCREAMING_SNAKE_CASE     ,				push_to_hub=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )



     @property
     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         return self._get_compatibles()




     @classmethod
     def 			lowercase__  (							cls   )		->     List[str]:







         """simple docstring"""



         a__		=			list(set([cls.__name__] + cls._compatibles   )   )
         a__		=			importlib.import_module(__name__.split('.'   )[0]   )
         a__		=			[
             getattr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) for c in compatible_classes_str if hasattr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         ]
         return compatible_classes




def     __magic_name__		(					UpperCamelCase     :		jnp.ndarray							,		UpperCamelCase     :		Tuple[int]     )     ->						jnp.ndarray:
    assert len(UpperCamelCase     ) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase     ) - x.ndim)     )							,		UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		int=0.999							,		UpperCamelCase     :		int=jnp.floataa     )     ->						jnp.ndarray:

    def alpha_bar(UpperCamelCase     :		Any     ):
        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2     ) ** 2

    a__		=			[]
    for i in range(UpperCamelCase     ):
        a__		=			i / num_diffusion_timesteps
        a__		=			(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(UpperCamelCase     ) / alpha_bar(UpperCamelCase     )							,		UpperCamelCase     )     )
    return jnp.array(UpperCamelCase							,		dtype=UpperCamelCase     )







@flax.struct.dataclass
class     lowercase:
     __snake_case:	jnp.ndarray
     __snake_case:	jnp.ndarray
     __snake_case:	jnp.ndarray



     @classmethod
     def 			lowercase__  (							cls     ,				__SCREAMING_SNAKE_CASE   )		->     Dict:







         """simple docstring"""



         a__		=			scheduler.config

         if config.trained_betas is not None:
             a__		=			jnp.asarray(config.trained_betas     ,				dtype=scheduler.dtype   )
         elif config.beta_schedule == "linear":
             a__		=			jnp.linspace(config.beta_start     ,				config.beta_end     ,				config.num_train_timesteps     ,				dtype=scheduler.dtype   )
         elif config.beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             a__		=			(
                 jnp.linspace(
                     config.beta_start**0.5     ,				config.beta_end**0.5     ,				config.num_train_timesteps     ,				dtype=scheduler.dtype   )
                 ** 2
             )
         elif config.beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             a__		=			betas_for_alpha_bar(config.num_train_timesteps     ,				dtype=scheduler.dtype   )
         else:
             raise NotImplementedError(
                 f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'   )

         a__		=			1.0 - betas

         a__		=			jnp.cumprod(__SCREAMING_SNAKE_CASE     ,				axis=0   )

         return cls(
             alphas=__SCREAMING_SNAKE_CASE     ,				betas=__SCREAMING_SNAKE_CASE     ,				alphas_cumprod=__SCREAMING_SNAKE_CASE     ,				)




def     __magic_name__		(					UpperCamelCase     :		CommonSchedulerState							,		UpperCamelCase     :		jnp.ndarray							,		UpperCamelCase     :		jnp.ndarray							,		UpperCamelCase     :		jnp.ndarray     )     ->						List[Any]:
    a__		=			state.alphas_cumprod

    a__		=			alphas_cumprod[timesteps] ** 0.5
    a__		=			sqrt_alpha_prod.flatten()
    a__		=			broadcast_to_shape_from_left(UpperCamelCase							,		original_samples.shape     )

    a__		=			(1 - alphas_cumprod[timesteps]) ** 0.5
    a__		=			sqrt_one_minus_alpha_prod.flatten()
    a__		=			broadcast_to_shape_from_left(UpperCamelCase							,		original_samples.shape     )

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def     __magic_name__		(					UpperCamelCase     :		CommonSchedulerState							,		UpperCamelCase     :		jnp.ndarray							,		UpperCamelCase     :		jnp.ndarray							,		UpperCamelCase     :		jnp.ndarray     )     ->						int:
    a__	,     a__		=			get_sqrt_alpha_prod(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    a__		=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def     __magic_name__		(					UpperCamelCase     :		CommonSchedulerState							,		UpperCamelCase     :		jnp.ndarray							,		UpperCamelCase     :		jnp.ndarray							,		UpperCamelCase     :		jnp.ndarray     )     ->						Any:
    a__	,     a__		=			get_sqrt_alpha_prod(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    a__		=			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity




"""simple docstring"""




from __future__ import annotations



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		list[str] | None = None     )     ->						list[list[str]]:
    a__		=			word_bank or []
    # create a table
    a__		=			len(UpperCamelCase     ) + 1

    a__		=			[]
    for _ in range(UpperCamelCase     ):
        table.append([]     )
    # seed value
    a__		=			[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase     ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase     )] == word:
                    a__		=			[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase     )]:
        combination.reverse()

    return table[len(UpperCamelCase     )]


if __name__ == "__main__":
    print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
    print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
    print(
        all_construct(
            'hexagonosaurus',
            ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
        )
    )


