


'''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   :   Optional[int]												=   "scheduler_config.json"




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  List[str]          =	1
   _SCREAMING_SNAKE_CASE  :  Any          =	2
   _SCREAMING_SNAKE_CASE  :  int          =	3
   _SCREAMING_SNAKE_CASE  :  str          =	4
   _SCREAMING_SNAKE_CASE  :  int          =	5




@dataclass
class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray




class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	SCHEDULER_CONFIG_NAME
   _SCREAMING_SNAKE_CASE  :  Any          =	["dtype"]
   _SCREAMING_SNAKE_CASE  :  Tuple          =	[]
   _SCREAMING_SNAKE_CASE  :  Any          =	True
   @classmethod
   def a  (						cls       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, Any] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[str] = None       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=False       ,   **SCREAMING_SNAKE_CASE_       :      Dict       ,   ) ->							Dict:
        __snake_case      ,       __snake_case           =						cls.load_config(
            pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_       ,   subfolder=SCREAMING_SNAKE_CASE_       ,   return_unused_kwargs=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_       ,   )
        __snake_case      ,       __snake_case           =						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_      ):
             __snake_case           =						scheduler.create_state()

        if return_unused_kwargs:
             return scheduler, state, unused_kwargs

        return scheduler, state
   def a  (						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Union[str, os.PathLike]       ,   SCREAMING_SNAKE_CASE_       :      bool = False       ,   **SCREAMING_SNAKE_CASE_       :      Union[str, Any]      ) ->							Union[str, Any]:
        self.save_config(save_directory=SCREAMING_SNAKE_CASE_       ,   push_to_hub=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   @property
   def a  (						self       :      Optional[Any]      ) ->							Optional[int]:
        return self._get_compatibles()





   @classmethod
   def a  (						cls       :      Any      ) ->							int:
        __snake_case           =						list(set([cls.__name__] + cls._compatibles      )      )
        __snake_case           =						importlib.import_module(__name__.split('.'      )[0]      )
        __snake_case           =						[
            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   _a		(lowercase__			:   jnp.ndarray							,   lowercase__			:   Tuple[int]						)			->			jnp.ndarray:






     """simple docstring"""
     assert len(lowercase__						) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase__						) - x.ndim)						)							,   lowercase__						)





def   _a		(lowercase__			:   int							,   lowercase__			:   Optional[Any]=0.9_99							,   lowercase__			:   Dict=jnp.floataa						)			->			jnp.ndarray:






     """simple docstring"""

     def alpha_bar(lowercase__			:   Union[str, Any]						):
          return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

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




@flax.struct.dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray
   _SCREAMING_SNAKE_CASE  :  jnp.ndarray
   @classmethod
   def a  (						cls       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]      ) ->							Dict:
        __snake_case           =						scheduler.config

        if config.trained_betas is not None:
             __snake_case           =						jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype      )
        elif config.beta_schedule == "linear":
             __snake_case           =						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.
             __snake_case           =						(
                 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
             __snake_case           =						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__}'      )

        __snake_case           =						1.0 - betas

        __snake_case           =						jnp.cumprod(SCREAMING_SNAKE_CASE_       ,   axis=0      )

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





def   _a		(lowercase__			:   CommonSchedulerState							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray						)			->			Optional[int]:






     """simple docstring"""
     __snake_case           =						state.alphas_cumprod

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

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

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def   _a		(lowercase__			:   CommonSchedulerState							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray						)			->			int:






     """simple docstring"""
     __snake_case      ,       __snake_case           =						get_sqrt_alpha_prod(lowercase__							,   lowercase__							,   lowercase__							,   lowercase__						)
     __snake_case           =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples





def   _a		(lowercase__			:   CommonSchedulerState							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray							,   lowercase__			:   jnp.ndarray						)			->			List[Any]:






     """simple docstring"""
     __snake_case      ,       __snake_case           =						get_sqrt_alpha_prod(lowercase__							,   lowercase__							,   lowercase__							,   lowercase__						)
     __snake_case           =						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity






import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]   )
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]   )
def 							a		(				snake_case__:   int		,  snake_case__:   Dict		,  snake_case__:   Dict   ):


				'''simple docstring'''
				if input_in_memory_max_size != "default":
								monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  snake_case__   )
				lowercase_	    =   datasets.config.IN_MEMORY_MAX_SIZE
				if input_in_memory_max_size == "default":
								assert in_memory_max_size == 0
				else:
								assert in_memory_max_size == input_in_memory_max_size
				if dataset_size and in_memory_max_size:
								lowercase_	    =   dataset_size < in_memory_max_size
				else:
								lowercase_	    =   False
				lowercase_	    =   is_small_dataset(snake_case__   )
				assert result == expected




