



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		:
							_a       =				42

							# setable values
							_a       =				42
							_a       =				42
							_a       =				42
							_a       =				None

							# running values
							_a       =				None




							@classmethod
							def      UpperCAmelCase__    ( cls  : Union[str, Any]       ,       A_  : CommonSchedulerState       ,       A_  : jnp.ndarray       ,       A_  : jnp.ndarray       ,       A_  : jnp.ndarray):
									return cls(common=A_       ,       init_noise_sigma=A_       ,       timesteps=A_       ,       sigmas=A_)





@dataclass
class        __snake_case		(  UpperCamelCase_		):
							_a       =				42





class        __snake_case		(  UpperCamelCase_		,UpperCamelCase_		):
							_a       =				[e.name for e in FlaxKarrasDiffusionSchedulers]

							_a       =				42




							@property
							def      UpperCAmelCase__    ( self  : int):
									return True




							@register_to_config
							def __init__( self  : str       ,       A_  : int = 1_0_0_0       ,       A_  : float = 0.0001       ,       A_  : float = 0.02       ,       A_  : str = "linear"       ,       A_  : Optional[jnp.ndarray] = None       ,       A_  : str = "epsilon"       ,       A_  : jnp.dtype = jnp.floataa       ,       ):
									lowerCAmelCase_    :  Union[str, Any]             =				dtype




							def      UpperCAmelCase__    ( self  : Optional[Any]       ,       A_  : Optional[CommonSchedulerState] = None):
									if common is None:
											lowerCAmelCase_    :  Optional[int]             =				CommonSchedulerState.create(self)

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

									# standard deviation of the initial noise distribution
									lowerCAmelCase_    :  List[Any]             =				sigmas.max()

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




							def      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : LMSDiscreteSchedulerState       ,       A_  : jnp.ndarray       ,       A_  : int):
									((lowerCAmelCase_)      ,						)    :  Tuple             =				jnp.where(state.timesteps == timestep       ,       size=1)
									lowerCAmelCase_    :  int             =				step_index[0]

									lowerCAmelCase_    :  Optional[int]             =				state.sigmas[step_index]
									lowerCAmelCase_    :  List[str]             =				sample / ((sigma**2 + 1) ** 0.5)
									return sample




							def      UpperCAmelCase__    ( self  : Union[str, Any]       ,       A_  : LMSDiscreteSchedulerState       ,       A_  : Dict       ,       A_  : Union[str, Any]       ,       A_  : Tuple):

									def lms_derivative(A_  : int):
											lowerCAmelCase_    :  List[str]             =				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_    :  Any             =				integrate.quad(A_       ,       state.sigmas[t]       ,       state.sigmas[t + 1]       ,       epsrel=1e-4)[0]

									return integrated_coeff




							def      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : LMSDiscreteSchedulerState       ,       A_  : int       ,       A_  : Tuple = ()):
									lowerCAmelCase_    :  str             =				jnp.linspace(self.config.num_train_timesteps - 1       ,       0       ,       A_       ,       dtype=self.dtype)

									lowerCAmelCase_    :  Union[str, Any]             =				jnp.floor(A_).astype(jnp.intaa)
									lowerCAmelCase_    :  str             =				jnp.ceil(A_).astype(jnp.intaa)

									lowerCAmelCase_    :  Any             =				jnp.mod(A_       ,       1.0)

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

									lowerCAmelCase_    :  List[Any]             =				timesteps.astype(jnp.intaa)

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

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




							def      UpperCAmelCase__    ( self  : str       ,       A_  : LMSDiscreteSchedulerState       ,       A_  : jnp.ndarray       ,       A_  : int       ,       A_  : jnp.ndarray       ,       A_  : int = 4       ,       A_  : bool = True       ,       ):
									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_    :  Union[str, Any]             =				state.sigmas[timestep]

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

									# 3. Compute linear multistep coefficients
									lowerCAmelCase_    :  Optional[int]             =				min(timestep + 1       ,       A_)
									lowerCAmelCase_    :  List[str]             =				[self.get_lms_coefficient(A_       ,       A_       ,       A_       ,       A_) for curr_order in range(A_)]

									# 4. Compute previous sample based on the derivatives path
									lowerCAmelCase_    :  List[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      UpperCAmelCase__    ( self  : Optional[Any]       ,       A_  : LMSDiscreteSchedulerState       ,       A_  : jnp.ndarray       ,       A_  : jnp.ndarray       ,       A_  : jnp.ndarray       ,       ):
									lowerCAmelCase_    :  int             =				state.sigmas[timesteps].flatten()
									lowerCAmelCase_    :  str             =				broadcast_to_shape_from_left(A_       ,       noise.shape)

									lowerCAmelCase_    :  List[Any]             =				original_samples + noise * sigma

									return noisy_samples







							def __len__( self  : Tuple):
									return self.config.num_train_timesteps










from __future__ import annotations

from typing import Generic, TypeVar

A__						:						Dict          =      TypeVar('''T''')



class        __snake_case		(  Generic[T]		):




							def __init__( self  : List[Any]       ,       A_  : T):
									lowerCAmelCase_    :  Optional[Any]             =				data  # Assign data
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize next as null
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize prev as null





class        __snake_case		(  Generic[T]		):




							def __init__( self  : Union[str, Any]):
									lowerCAmelCase_    :  Node[T] | None             =				None




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : T):
									if self.head is None:
											lowerCAmelCase_    :  List[Any]             =				Node(A_)
									else:
											lowerCAmelCase_    :  Tuple             =				Node(A_)
											lowerCAmelCase_    :  Optional[int]             =				new_node
											lowerCAmelCase_    :  Optional[int]             =				self.head
											lowerCAmelCase_    :  Optional[Any]             =				None
											lowerCAmelCase_    :  Dict             =				new_node




							def      UpperCAmelCase__    ( self  : List[str]):
									if self.head is None:
											return None
									else:
											assert self.head is not None
											lowerCAmelCase_    :  Tuple             =				self.head.data
											lowerCAmelCase_    :  Any             =				self.head.next
											if self.head is not None:
													lowerCAmelCase_    :  Optional[Any]             =				None
											return temp




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									return self.head.data if self.head is not None else None




							def __len__( self  : Dict):
									lowerCAmelCase_    :  List[str]             =				self.head
									lowerCAmelCase_    :  str             =				0
									while temp is not None:
											count += 1
											lowerCAmelCase_    :  Union[str, Any]             =				temp.next
									return count




							def      UpperCAmelCase__    ( self  : Dict):
									return self.head is None







							def      UpperCAmelCase__    ( self  : Dict):
									print('''stack elements are:''')
									lowerCAmelCase_    :  Optional[Any]             =				self.head
									while temp is not None:
											print(temp.data       ,       end='''->''')
											lowerCAmelCase_    :  List[str]             =				temp.next


# Code execution starts here
if __name__ == "__main__":
						# Start with the empty stack
						A__						:						Stack[int]          =      Stack()

						# Insert 4 at the beginning. So stack becomes 4->None
						print('''Stack operations using Doubly LinkedList''')
						stack.push(4)

						# Insert 5 at the beginning. So stack becomes 4->5->None
						stack.push(5)

						# Insert 6 at the beginning. So stack becomes 4->5->6->None
						stack.push(6)

						# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
						stack.push(7)

						# Print the stack
						stack.print_stack()

						# Print the top element
						print('''\nTop element is ''', stack.top())

						# Print the stack size
						print('''Size of the stack is ''', len(stack))

						# pop the top element
						stack.pop()

						# pop the top element
						stack.pop()

						# two elements have now been popped off
						stack.print_stack()

						# Print True if the stack is empty else False
						print('''\nstack is empty:''', stack.is_empty())





