
'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class       a		:
							lowercase_		:						Optional[Any]   							=  42

							# setable values
							lowercase_		:						List[Any]   							=  42
							lowercase_		:						Tuple   							=  42
							lowercase_		:						Any   							=  None







							@classmethod
							def       UpperCAmelCase__					(						cls  :  Optional[int]      ,				snake_case__  :  CommonSchedulerState      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  jnp.ndarray ):
								"""simple docstring"""







								return cls(common=__A      ,				init_noise_sigma=__A      ,				timesteps=__A )

@dataclass
class       a		(   UpperCamelCase_					):
							lowercase_		:						str   							=  42

class       a		(   UpperCamelCase_      ,	UpperCamelCase_					):
							lowercase_		:						List[str]   							=  [e.name for e in FlaxKarrasDiffusionSchedulers]

							lowercase_		:						List[Any]   							=  42







							@property
							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								return True







							@register_to_config
							def __init__(						self  :  Dict      ,				snake_case__  :  int = 1_000      ,				snake_case__  :  float = 0.0_0_0_1      ,				snake_case__  :  float = 0.0_2      ,				snake_case__  :  str = "linear"      ,				snake_case__  :  Optional[jnp.ndarray] = None      ,				snake_case__  :  str = "fixed_small"      ,				snake_case__  :  bool = True      ,				snake_case__  :  str = "epsilon"      ,				snake_case__  :  jnp.dtype = jnp.floataa      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							dtype







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







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

								# standard deviation of the initial noise distribution
								__lowerCAmelCase         =							jnp.array(1.0      ,				dtype=self.dtype )

								__lowerCAmelCase         =							jnp.arange(0      ,				self.config.num_train_timesteps ).round()[::-1]

								return DDPMSchedulerState.create(
								    common=__A      ,				init_noise_sigma=__A      ,				timesteps=__A      ,				)







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  DDPMSchedulerState      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  Optional[int] = None ):
								"""simple docstring"""







								return sample







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  DDPMSchedulerState      ,				snake_case__  :  int      ,				snake_case__  :  Tuple = () ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.config.num_train_timesteps // num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# rounding to avoid issues when num_inference_step is power of 3
								__lowerCAmelCase         =							(jnp.arange(0      ,				__A ) * step_ratio).round()[::-1]

								return state.replace(
								    num_inference_steps=__A      ,				timesteps=__A      ,				)







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  DDPMSchedulerState      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  List[Any]=None      ,				snake_case__  :  Optional[int]=None ):
								"""simple docstring"""







								__lowerCAmelCase         =							state.common.alphas_cumprod[t]
								__lowerCAmelCase         =							jnp.where(t > 0      ,				state.common.alphas_cumprod[t - 1]      ,				jnp.array(1.0      ,				dtype=self.dtype ) )

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								__lowerCAmelCase         =							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
									__lowerCAmelCase         =							self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small":
									__lowerCAmelCase         =							jnp.clip(__A      ,				a_min=1E-20 )
								# for rl-diffuser https://arxiv.org/abs/2205.09991
								elif variance_type == "fixed_small_log":
									__lowerCAmelCase         =							jnp.log(jnp.clip(__A      ,				a_min=1E-20 ) )
								elif variance_type == "fixed_large":
									__lowerCAmelCase         =							state.common.betas[t]
								elif variance_type == "fixed_large_log":
									# Glide max_log
									__lowerCAmelCase         =							jnp.log(state.common.betas[t] )
								elif variance_type == "learned":
									return predicted_variance
								elif variance_type == "learned_range":
									__lowerCAmelCase         =							variance
									__lowerCAmelCase         =							state.common.betas[t]
									__lowerCAmelCase         =							(predicted_variance + 1) / 2
									__lowerCAmelCase         =							frac * max_log + (1 - frac) * min_log

								return variance







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  DDPMSchedulerState      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  int      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  Optional[jax.random.KeyArray] = None      ,				snake_case__  :  bool = True      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							timestep

								if key is None:
									__lowerCAmelCase         =							jax.random.PRNGKey(0 )

								if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
									__lowerCAmelCase         =							jnp.split(__A      ,				sample.shape[1]      ,				axis=1 )
								else:
									__lowerCAmelCase         =							None

								# 1. compute alphas, betas
								__lowerCAmelCase         =							state.common.alphas_cumprod[t]
								__lowerCAmelCase         =							jnp.where(t > 0      ,				state.common.alphas_cumprod[t - 1]      ,				jnp.array(1.0      ,				dtype=self.dtype ) )
								__lowerCAmelCase         =							1 - alpha_prod_t
								__lowerCAmelCase         =							1 - alpha_prod_t_prev

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								if self.config.prediction_type == "epsilon":
									__lowerCAmelCase         =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
									__lowerCAmelCase         =							model_output
								elif self.config.prediction_type == "v_prediction":
									__lowerCAmelCase         =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
								else:
									raise ValueError(
									    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
									    " for the FlaxDDPMScheduler." )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
									__lowerCAmelCase         =							jnp.clip(__A      ,				-1      ,				1 )

								# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__lowerCAmelCase         =							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
								__lowerCAmelCase         =							state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

								# 5. Compute predicted previous sample µ_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__lowerCAmelCase         =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								def random_variance():
									__lowerCAmelCase         =							jax.random.split(__A      ,				num=1 )
									__lowerCAmelCase         =							jax.random.normal(__A      ,				shape=model_output.shape      ,				dtype=self.dtype )
									return (self._get_variance(__A      ,				__A      ,				predicted_variance=__A ) ** 0.5) * noise

								__lowerCAmelCase         =							jnp.where(t > 0      ,				random_variance()      ,				jnp.zeros(model_output.shape      ,				dtype=self.dtype ) )

								__lowerCAmelCase         =							pred_prev_sample + variance

								if not return_dict:
									return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=__A      ,				state=__A )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  DDPMSchedulerState      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  jnp.ndarray      ,				):
								"""simple docstring"""







								return add_noise_common(state.common      ,				__A      ,				__A      ,				__A )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  DDPMSchedulerState      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  jnp.ndarray      ,				snake_case__  :  jnp.ndarray      ,				):
								"""simple docstring"""







								return get_velocity_common(state.common      ,				__A      ,				__A      ,				__A )







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







								return self.config.num_train_timesteps








from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class       a		:







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							data
								__lowerCAmelCase         =							None

class       a		:







							def __init__(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							None







							def __iter__(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.head
								while self.head:
									yield node.data
									__lowerCAmelCase         =							node.next
									if node == self.head:
										break







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







								return sum(1 for _ in self )







							def __repr__(						self  :  str ):
								"""simple docstring"""







								return "->".join(str(snake_case__ ) for item in iter(self ) )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(len(self )      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								self.insert_nth(0      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  int      ,				snake_case__  :  Any ):
								"""simple docstring"""







								if index < 0 or index > len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							Node(snake_case__ )
								if self.head is None:
									__lowerCAmelCase         =							new_node  # first node points itself
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								elif index == 0:  # insert at head
									__lowerCAmelCase         =							self.head
									__lowerCAmelCase         =							__lowerCAmelCase         =							new_node
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							new_node
									if index == len(self ) - 1:  # insert at tail
										__lowerCAmelCase         =							new_node







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								return self.delete_nth(0 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return self.delete_nth(len(self ) - 1 )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int = 0 ):
								"""simple docstring"""







								if not 0 <= index < len(self ):
									raise IndexError("list index out of range." )
								__lowerCAmelCase         =							self.head
								if self.head == self.tail:  # just one node
									__lowerCAmelCase         =							__lowerCAmelCase         =							None
								elif index == 0:  # delete head node
									__lowerCAmelCase         =							self.tail.next.next
									__lowerCAmelCase         =							self.head.next
								else:
									__lowerCAmelCase         =							self.head
									for _ in range(index - 1 ):
										__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next
									__lowerCAmelCase         =							temp.next.next
									if index == len(self ) - 1:  # delete at tail
										__lowerCAmelCase         =							temp
								return delete_node.data







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								return len(self ) == 0






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							CircularLinkedList()
	assert len(UpperCamelCase  ) == 0
	assert circular_linked_list.is_empty() is True
	assert str(UpperCamelCase  ) == ""

	try:
		circular_linked_list.delete_front()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_tail()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_nth(-1  )
		raise AssertionError
	except IndexError:
		assert True

	try:
		circular_linked_list.delete_nth(0  )
		raise AssertionError
	except IndexError:
		assert True

	assert circular_linked_list.is_empty() is True
	for i in range(5  ):
		assert len(UpperCamelCase  ) == i
		circular_linked_list.insert_nth(UpperCamelCase			,							i + 1  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	circular_linked_list.insert_tail(6  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							7  )  )
	circular_linked_list.insert_head(0  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(0			,							7  )  )

	assert circular_linked_list.delete_front() == 0
	assert circular_linked_list.delete_tail() == 6
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )
	assert circular_linked_list.delete_nth(2  ) == 3

	circular_linked_list.insert_nth(2			,							3  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1			,							6  )  )

	assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
					import doctest

					doctest.testmod()






