"""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	:




    """simple docstring"""




    UpperCamelCase_	:					CommonSchedulerState

    # setable values
    UpperCamelCase_	:					jnp.ndarray
    UpperCamelCase_	:					jnp.ndarray
    UpperCamelCase_	:					Optional[int]							=					None
    @classmethod
    def  lowercase					(		cls							:     List[Any]	,      A_							:     CommonSchedulerState	,      A_							:     jnp.ndarray	,      A_							:     jnp.ndarray    )    ->       Optional[Any]:
         return cls(common=A_	,      init_noise_sigma=A_	,      timesteps=A_    )






@dataclass
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					DDPMSchedulerState






class      _A	(						_UpperCAmelCase							, _UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					int							=					[e.name for e in FlaxKarrasDiffusionSchedulers]

    UpperCamelCase_	:					jnp.dtype
    @property
    def  lowercase					(		self							:     Tuple    )    ->       int:
         return True
    @register_to_config
    def __init__(		self							:     Dict	,      A_							:     int = 1_000	,      A_							:     float = 0.00_01	,      A_							:     float = 0.02	,      A_							:     str = "linear"	,      A_							:     Optional[jnp.ndarray] = None	,      A_							:     str = "fixed_small"	,      A_							:     bool = True	,      A_							:     str = "epsilon"	,      A_							:     jnp.dtype = jnp.floataa	,      )    ->       Optional[Any]:
         __snake_case				=							dtype
    def  lowercase					(		self							:     List[Any]	,      A_							:     Optional[CommonSchedulerState] = None    )    ->       DDPMSchedulerState:
         if common is None:
              __snake_case				=							CommonSchedulerState.create(self    )

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

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

         return DDPMSchedulerState.create(
             common=A_	,      init_noise_sigma=A_	,      timesteps=A_	,      )
    def  lowercase					(		self							:     Dict	,      A_							:     DDPMSchedulerState	,      A_							:     jnp.ndarray	,      A_							:     Optional[int] = None    )    ->       jnp.ndarray:
         return sample
    def  lowercase					(		self							:     Optional[int]	,      A_							:     DDPMSchedulerState	,      A_							:     int	,      A_							:     Tuple = ()    )    ->       DDPMSchedulerState:
         __snake_case				=							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
         __snake_case				=							(jnp.arange(0	,      A_    ) * step_ratio).round()[::-1]

         return state.replace(
             num_inference_steps=A_	,      timesteps=A_	,      )
    def  lowercase					(		self							:     List[Any]	,      A_							:     DDPMSchedulerState	,      A_							:     Tuple	,      A_							:     Dict=None	,      A_							:     Optional[Any]=None    )    ->       Any:
         __snake_case				=							state.common.alphas_cumprod[t]
         __snake_case				=							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
         __snake_case				=							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

         # hacks - were probably added for training stability
         if variance_type == "fixed_small":
              __snake_case				=							jnp.clip(A_	,      a_min=1E-2_0    )
         # for rl-diffuser https://arxiv.org/abs/2205.09991
         elif variance_type == "fixed_small_log":
              __snake_case				=							jnp.log(jnp.clip(A_	,      a_min=1E-2_0    )    )
         elif variance_type == "fixed_large":
              __snake_case				=							state.common.betas[t]
         elif variance_type == "fixed_large_log":
              # Glide max_log
              __snake_case				=							jnp.log(state.common.betas[t]    )
         elif variance_type == "learned":
              return predicted_variance
         elif variance_type == "learned_range":
              __snake_case				=							variance
              __snake_case				=							state.common.betas[t]
              __snake_case				=							(predicted_variance + 1) / 2
              __snake_case				=							frac * max_log + (1 - frac) * min_log

         return variance
    def  lowercase					(		self							:     List[str]	,      A_							:     DDPMSchedulerState	,      A_							:     jnp.ndarray	,      A_							:     int	,      A_							:     jnp.ndarray	,      A_							:     Optional[jax.random.KeyArray] = None	,      A_							:     bool = True	,      )    ->       Union[FlaxDDPMSchedulerOutput, Tuple]:
         __snake_case				=							timestep

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

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

         # 1. compute alphas, betas
         __snake_case				=							state.common.alphas_cumprod[t]
         __snake_case				=							jnp.where(t > 0	,      state.common.alphas_cumprod[t - 1]	,      jnp.array(1.0	,      dtype=self.dtype    )    )
         __snake_case				=							1 - alpha_prod_t
         __snake_case				=							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":
              __snake_case				=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
              __snake_case				=							model_output
         elif self.config.prediction_type == "v_prediction":
              __snake_case				=							(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:
              __snake_case				=							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
         __snake_case				=							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
         __snake_case				=							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
         __snake_case				=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

         __snake_case				=							pred_prev_sample + variance

         if not return_dict:
              return (pred_prev_sample, state)

         return FlaxDDPMSchedulerOutput(prev_sample=A_	,      state=A_    )
    def  lowercase					(		self							:     int	,      A_							:     DDPMSchedulerState	,      A_							:     jnp.ndarray	,      A_							:     jnp.ndarray	,      A_							:     jnp.ndarray	,      )    ->       jnp.ndarray:
         return add_noise_common(state.common	,      A_	,      A_	,      A_    )
    def  lowercase					(		self							:     Any	,      A_							:     DDPMSchedulerState	,      A_							:     jnp.ndarray	,      A_							:     jnp.ndarray	,      A_							:     jnp.ndarray	,      )    ->       jnp.ndarray:
         return get_velocity_common(state.common	,      A_	,      A_	,      A_    )







    def __len__(		self							:     Optional[Any]    )    ->       str:
         return self.config.num_train_timesteps
"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Tuple	,      A_							:     Any    )    ->       Union[str, Any]:
         __snake_case				=							data
         __snake_case				=							None






class      _A	:




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							None
         __snake_case				=							None
    def __iter__(		self							:     Optional[Any]    )    ->       Iterator[Any]:
         __snake_case				=							self.head
         while self.head:
              yield node.data
              __snake_case				=							node.next
              if node == self.head:
                   break
    def __len__(		self							:     List[Any]    )    ->       int:
         return sum(1 for _ in self    )
    def __repr__(		self							:     Optional[int]    )    ->       Union[str, Any]:
         return "->".join(str(A_    ) for item in iter(self    )    )
    def  lowercase					(		self							:     int	,      A_							:     Any    )    ->       None:
         self.insert_nth(len(self    )	,      A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any    )    ->       None:
         self.insert_nth(0	,      A_    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     int	,      A_							:     Any    )    ->       None:
         if index < 0 or index > len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							Node(A_    )
         if self.head is None:
              __snake_case				=							new_node  # first node points itself
              __snake_case				=							__snake_case				=							new_node
         elif index == 0:  # insert at head
              __snake_case				=							self.head
              __snake_case				=							__snake_case				=							new_node
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							new_node
              if index == len(self    ) - 1:  # insert at tail
                   __snake_case				=							new_node
    def  lowercase					(		self							:     int    )    ->       str:
         return self.delete_nth(0    )
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         return self.delete_nth(len(self    ) - 1    )
    def  lowercase					(		self							:     List[str]	,      A_							:     int = 0    )    ->       Any:
         if not 0 <= index < len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							self.head
         if self.head == self.tail:  # just one node
              __snake_case				=							__snake_case				=							None
         elif index == 0:  # delete head node
              __snake_case				=							self.tail.next.next
              __snake_case				=							self.head.next
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							temp.next.next
              if index == len(self    ) - 1:  # delete at tail
                   __snake_case				=							temp
         return delete_node.data







    def  lowercase					(		self							:     Dict    )    ->       bool:
         return len(self    ) == 0






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							CircularLinkedList()
     assert len(snake_case) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case) == ""

     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(snake_case) == i
          circular_linked_list.insert_nth(snake_case,      i + 1)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))

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

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

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

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
       import doctest

       doctest.testmod()