






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


__lowerCamelCase :      Tuple										=				"""scheduler_config.json"""



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		1
					a_     =		2
					a_     =		3
					a_     =		4
					a_     =		5



@dataclass
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		42



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					a_     =		SCHEDULER_CONFIG_NAME
					a_     =		["dtype"]
					a_     =		[]
					a_     =		True



					@classmethod
					def 	_lowercase					( cls					:  str							,		__A					:  Dict[str, Any] = None							,		__A					:  Optional[str] = None							,		__A					:  Dict=False							,		**__A					:  Optional[Any]							,		):
						snake_case__,						snake_case__			:				Dict       =  cls.load_config(
						    pretrained_model_name_or_path=__A							,		subfolder=__A							,		return_unused_kwargs=__A							,		**__A							,		)
						snake_case__,						snake_case__			:				List[Any]       =  cls.from_config(__A							,		return_unused_kwargs=__A							,		**__A	)

						if hasattr(__A							,		"create_state"	) and getattr(__A							,		"has_state"							,		__A	):
							snake_case__			:				Tuple       =  scheduler.create_state()

						if return_unused_kwargs:
							return scheduler, state, unused_kwargs

						return scheduler, state



					def 	_lowercase					( self					:  Any							,		__A					:  Union[str, os.PathLike]							,		__A					:  bool = False							,		**__A					:  Dict	):
						self.save_config(save_directory=__A							,		push_to_hub=__A							,		**__A	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						return self._get_compatibles()






					@classmethod
					def 	_lowercase					( cls					:  List[str]	):
						snake_case__			:				str       =  list(set([cls.__name__] + cls._compatibles	)	)
						snake_case__			:				List[str]       =  importlib.import_module(__name__.split("."	)[0]	)
						snake_case__			:				Dict       =  [
						    getattr(__A							,		__A	) for c in compatible_classes_str if hasattr(__A							,		__A	)
						]
						return compatible_classes







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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							Any=0.9_99					,  snake_case_ :							int=jnp.floataa       ):

	def alpha_bar(snake_case_ :							int       ):
		return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2       ) ** 2

	snake_case__			:				int       =  []
	for i in range(snake_case_       ):
		snake_case__			:				List[Any]       =  i / num_diffusion_timesteps
		snake_case__			:				Dict       =  (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(snake_case_       ) / alpha_bar(snake_case_       )					,  snake_case_       )       )
	return jnp.array(snake_case_					,  dtype=snake_case_       )



@flax.struct.dataclass
class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					a_     =		42
					a_     =		42
					a_     =		42



					@classmethod
					def 	_lowercase					( cls					:  Optional[Any]							,		__A					:  Any	):
						snake_case__			:				List[Any]       =  scheduler.config

						if config.trained_betas is not None:
							snake_case__			:				Optional[int]       =  jnp.asarray(config.trained_betas							,		dtype=scheduler.dtype	)
						elif config.beta_schedule == "linear":
							snake_case__			:				int       =  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__			:				str       =  (
							    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__			:				Tuple       =  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__			:				List[Any]       =  1.0 - betas

						snake_case__			:				List[str]       =  jnp.cumprod(__A							,		axis=0	)

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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							CommonSchedulerState					,  snake_case_ :							jnp.ndarray					,  snake_case_ :							jnp.ndarray					,  snake_case_ :							jnp.ndarray       ):
	snake_case__			:				int       =  state.alphas_cumprod

	snake_case__			:				str       =  alphas_cumprod[timesteps] ** 0.5
	snake_case__			:				List[str]       =  sqrt_alpha_prod.flatten()
	snake_case__			:				Union[str, Any]       =  broadcast_to_shape_from_left(snake_case_					,  original_samples.shape       )

	snake_case__			:				Union[str, Any]       =  (1 - alphas_cumprod[timesteps]) ** 0.5
	snake_case__			:				Union[str, Any]       =  sqrt_one_minus_alpha_prod.flatten()
	snake_case__			:				List[Any]       =  broadcast_to_shape_from_left(snake_case_					,  original_samples.shape       )

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							CommonSchedulerState					,  snake_case_ :							jnp.ndarray					,  snake_case_ :							jnp.ndarray					,  snake_case_ :							jnp.ndarray       ):
	snake_case__,						snake_case__			:				Optional[int]       =  get_sqrt_alpha_prod(snake_case_					,  snake_case_					,  snake_case_					,  snake_case_       )
	snake_case__			:				Dict       =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							CommonSchedulerState					,  snake_case_ :							jnp.ndarray					,  snake_case_ :							jnp.ndarray					,  snake_case_ :							jnp.ndarray       ):
	snake_case__,						snake_case__			:				Union[str, Any]       =  get_sqrt_alpha_prod(snake_case_					,  snake_case_					,  snake_case_					,  snake_case_       )
	snake_case__			:				Optional[Any]       =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity











from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	for param in module.parameters():
		snake_case__			:				Tuple       =  False







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				Any       =  "cuda" if torch.cuda.is_available() else "cpu"
	if torch.backends.mps.is_available() and torch.backends.mps.is_built():
		snake_case__			:				List[Any]       =  "mps"
	if device == "mps":
		print(
		    "WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
		    " errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
		    " with generations."       )
	return device







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				List[str]       =  plt.imshow(snake_case_       )
	fig.axes.get_xaxis().set_visible(snake_case_       )
	fig.axes.get_yaxis().set_visible(snake_case_       )
	plt.show()







def 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  datetime.now()
	snake_case__			:				Optional[Any]       =  current_time.strftime("%H:%M:%S"       )
	return timestamp



