
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        UpperCamelCase__:

	"""simple docstring"""

	_A  =		42

	# setable values
	_A  =		42
	_A  =		42
	_A  =		42
	_A  =		None

	# running values
	_A  =		None







	@classmethod
	def        _a					(      cls				:			Tuple							,			snake_case__				:			CommonSchedulerState							,			snake_case__				:			jnp.ndarray							,			snake_case__				:			jnp.ndarray							,			snake_case__				:			jnp.ndarray ):
			"""simple docstring"""





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


@dataclass
class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		42


class        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		[e.name for e in FlaxKarrasDiffusionSchedulers]

	_A  =		42







	@property
	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			return True







	@register_to_config
	def __init__(      self				:			List[Any]							,			snake_case__				:			int = 10_00							,			snake_case__				:			float = 0.0_001							,			snake_case__				:			float = 0.02							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[jnp.ndarray] = None							,			snake_case__				:			str = "epsilon"							,			snake_case__				:			jnp.dtype = jnp.floataa							,			):
			"""simple docstring"""





			A				=dtype







	def        _a					(      self				:			str							,			snake_case__				:			Optional[CommonSchedulerState] = None ):
			"""simple docstring"""





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

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

			# standard deviation of the initial noise distribution
			A				=sigmas.max()

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







	def        _a					(      self				:			List[str]							,			snake_case__				:			LMSDiscreteSchedulerState							,			snake_case__				:			jnp.ndarray							,			snake_case__				:			int ):
			"""simple docstring"""





			((A)			,    )				=jnp.where(state.timesteps == timestep							,			size=1 )
			A				=step_index[0]

			A				=state.sigmas[step_index]
			A				=sample / ((sigma**2 + 1) ** 0.5)
			return sample







	def        _a					(      self				:			List[str]							,			snake_case__				:			LMSDiscreteSchedulerState							,			snake_case__				:			List[Any]							,			snake_case__				:			int							,			snake_case__				:			str ):
			"""simple docstring"""





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

			A				=integrate.quad(snake_case__							,			state.sigmas[t]							,			state.sigmas[t + 1]							,			epsrel=1E-4 )[0]

			return integrated_coeff







	def        _a					(      self				:			str							,			snake_case__				:			LMSDiscreteSchedulerState							,			snake_case__				:			int							,			snake_case__				:			Tuple = () ):
			"""simple docstring"""





			A				=jnp.linspace(self.config.num_train_timesteps - 1							,			0							,			snake_case__							,			dtype=self.dtype )

			A				=jnp.floor(snake_case__ ).astype(jnp.intaa )
			A				=jnp.ceil(snake_case__ ).astype(jnp.intaa )

			A				=jnp.mod(snake_case__							,			1.0 )

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

			A				=timesteps.astype(jnp.intaa )

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

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







	def        _a					(      self				:			List[str]							,			snake_case__				:			LMSDiscreteSchedulerState							,			snake_case__				:			jnp.ndarray							,			snake_case__				:			int							,			snake_case__				:			jnp.ndarray							,			snake_case__				:			int = 4							,			snake_case__				:			bool = True							,			):
			"""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" )

			A				=state.sigmas[timestep]

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

			# 3. Compute linear multistep coefficients
			A				=min(timestep + 1							,			snake_case__ )
			A				=[self.get_lms_coefficient(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__ ) for curr_order in range(snake_case__ )]

			# 4. Compute previous sample based on the derivatives path
			A				=sample + sum(
			    coeff * derivative for coeff, derivative in zip(snake_case__							,			reversed(state.derivatives ) ) )

			if not return_dict:
					return (prev_sample, state)

			return FlaxLMSSchedulerOutput(prev_sample=snake_case__							,			state=snake_case__ )







	def        _a					(      self				:			Tuple							,			snake_case__				:			LMSDiscreteSchedulerState							,			snake_case__				:			jnp.ndarray							,			snake_case__				:			jnp.ndarray							,			snake_case__				:			jnp.ndarray							,			):
			"""simple docstring"""





			A				=state.sigmas[timesteps].flatten()
			A				=broadcast_to_shape_from_left(snake_case__							,			noise.shape )

			A				=original_samples + noise * sigma

			return noisy_samples







	def __len__(      self				:			int ):
			"""simple docstring"""





			return self.config.num_train_timesteps






import functools
from math import sqrt
from time import time

def    UpperCamelCase_ (	a_  ,				*a_  ,				**a_		)     ->List[Any]:
		A				=time()
		A				=func(*a_  ,				**a_		)
		A				=time()
		if int(end - start		) > 0:
				print(f'''{func.__name__} runtime: {(end - start):0.4f} s'''		)
		else:
				print(f'''{func.__name__} runtime: {(end - start) * 1000:0.4f} ms'''		)
		return output

def    UpperCamelCase_ (	a_		)     ->list[int]:
		if n < 0:
				raise Exception("n is negative"		)
		if n == 0:
				return [0]
		A				=[0, 1]
		for _ in range(n - 1		):
				fib.append(fib[-1] + fib[-2]		)
		return fib

def    UpperCamelCase_ (	a_		)     ->list[int]:

		def fib_recursive_term(a_		) -> int:
				if i < 0:
						raise Exception("n is negative"		)
				if i < 2:
						return i
				return fib_recursive_term(i - 1		) + fib_recursive_term(i - 2		)

		if n < 0:
				raise Exception("n is negative"		)
		return [fib_recursive_term(a_		) for i in range(n + 1		)]

def    UpperCamelCase_ (	a_		)     ->list[int]:

		@functools.cache
		def fib_recursive_term(a_		) -> int:
				if i < 0:
						raise Exception("n is negative"		)
				if i < 2:
						return i
				return fib_recursive_term(i - 1		) + fib_recursive_term(i - 2		)

		if n < 0:
				raise Exception("n is negative"		)
		return [fib_recursive_term(a_		) for i in range(n + 1		)]

def    UpperCamelCase_ (	a_		)     ->list[int]:
		if n < 0:
				raise Exception("n is negative"		)
		# Cache must be outside recursuive function
		# other it will reset every time it calls itself.
		A				={0: 0, 1: 1, 2: 1}  # Prefilled cache

		def rec_fn_memoized(a_		) -> int:
				if num in cache:
						return cache[num]

				A				=rec_fn_memoized(num - 1		) + rec_fn_memoized(num - 2		)
				A				=value
				return value

		return [rec_fn_memoized(a_		) for i in range(n + 1		)]

def    UpperCamelCase_ (	a_		)     ->list[int]:
		if n < 0:
				raise Exception("n is negative"		)
		if n >= 1475:
				raise Exception("n is too large"		)
		A				=sqrt(5		)
		A				=(1 + sqrt_a) / 2
		return [round(phi**i / sqrt_a		) for i in range(n + 1		)]


if __name__ == "__main__":
	__a							  =						3_0
	time_func(fib_iterative, num)
	time_func(fib_recursive, num)  # Around 3s runtime
	time_func(fib_recursive_cached, num)  # Around 0ms runtime
	time_func(fib_memoization, num)
	time_func(fib_binet, num)




