



'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 	_lowerCAmelCase							:
	# setable values
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					None
	__SCREAMING_SNAKE_CASE   :		Optional[jnp.ndarray]					=					None
	__SCREAMING_SNAKE_CASE   :		Optional[jnp.ndarray]					=					None  # sigma(t_i)


	@classmethod
	def     _a	(cls							):
				return cls()







@dataclass
class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		jnp.ndarray
	__SCREAMING_SNAKE_CASE   :		jnp.ndarray
	__SCREAMING_SNAKE_CASE   :		KarrasVeSchedulerState







class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase ):


	@property
	def     _a	(self							):
				return True


	@register_to_config
	def __init__(self       ,      lowercase = 0.02       ,      lowercase = 100       ,      lowercase = 1.0_07       ,      lowercase = 80       ,      lowercase = 0.05       ,      lowercase = 50       ,      ):
				pass


	def     _a	(self							):
				return KarrasVeSchedulerState.create()


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase = ()							):
				A_	:						Any	=     jnp.arange(0       ,      lowercase							)[::-1].copy()
				A_	:						Any	=     [
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowercase       ,      schedule=jnp.array(lowercase       ,      dtype=jnp.floataa							)       ,      timesteps=lowercase       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      ):
				if self.config.s_min <= sigma <= self.config.s_max:
							A_	:						Any	=     min(self.config.s_churn / state.num_inference_steps       ,      2**0.5 - 1							)
				else:
							A_	:						Tuple	=     0

				# sample eps ~ N(0, S_noise^2 * I)
				A_	:						Dict	=     random.split(lowercase       ,      num=1							)
				A_	:						Tuple	=     self.config.s_noise * random.normal(key=lowercase       ,      shape=sample.shape							)
				A_	:						str	=     sigma + gamma * sigma
				A_	:						str	=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase = True       ,      ):
				A_	:						Union[str, Any]	=     sample_hat + sigma_hat * model_output
				A_	:						List[str]	=     (sample_hat - pred_original_sample) / sigma_hat
				A_	:						int	=     sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowercase       ,      derivative=lowercase       ,      state=lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase = True       ,      ):
				A_	:						Dict	=     sample_prev + sigma_prev * model_output
				A_	:						Optional[Any]	=     (sample_prev - pred_original_sample) / sigma_prev
				A_	:						int	=     sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowercase       ,      derivative=lowercase       ,      state=lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase							):
				raise NotImplementedError()




'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()