
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class     UpperCAmelCase						(		A_					):
   A__   :						torch.FloatTensor
   A__   :						torch.FloatTensor






class     UpperCAmelCase						(		A_			,A_					):
   A__   :						str			=	1



   @register_to_config
   def __init__(self						:   Optional[int]       ,		snake_case__						:   int = 20_00       ,		snake_case__						:   float = 0.15       ,		snake_case__						:   float = 0.01       ,		snake_case__						:   float = 1348.0       ,		snake_case__						:   float = 1e-5       ,		snake_case__						:   int = 1       ,		)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  List[Any]     							= sigma_max

          # setable values
          snake_case			:  Optional[Any]     							= None

          self.set_sigmas(snake_case__       ,		snake_case__       ,		snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   torch.FloatTensor       ,		snake_case__						:   Optional[int] = None					)						->      torch.FloatTensor:


          '''simple docstring'''


          return sample



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   int       ,		snake_case__						:   float = None       ,		snake_case__						:   Union[str, torch.device] = None					)						->      Dict:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= sampling_eps if sampling_eps is not None else self.config.sampling_eps

          snake_case			:  Tuple     							= torch.linspace(1       ,		snake_case__       ,		snake_case__       ,		device=snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]       ,		snake_case__						:   int       ,		snake_case__						:   float = None       ,		snake_case__						:   float = None       ,		snake_case__						:   float = None					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  List[str]     							= sigma_min if sigma_min is not None else self.config.sigma_min
          snake_case			:  str     							= sigma_max if sigma_max is not None else self.config.sigma_max
          snake_case			:  Tuple     							= sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if self.timesteps is None:
                 self.set_timesteps(snake_case__       ,		snake_case__					)

          snake_case			:  int     							= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
          snake_case			:  int     							= torch.exp(torch.linspace(math.log(snake_case__					)       ,		math.log(snake_case__					)       ,		snake_case__					)					)
          snake_case			:  List[str]     							= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   Tuple       ,		snake_case__						:   Optional[int]					)						->      Any:


          '''simple docstring'''


          return torch.where(
              timesteps == 0       ,		torch.zeros_like(t.to(timesteps.device					)					)       ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device					)       ,		)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   torch.FloatTensor       ,		snake_case__						:   int       ,		snake_case__						:   torch.FloatTensor       ,		snake_case__						:   Optional[torch.Generator] = None       ,		snake_case__						:   bool = True       ,		)						->      Union[SdeVeOutput, Tuple]:


          '''simple docstring'''


          if self.timesteps is None:
                 raise ValueError(
                     "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

          snake_case			:  Optional[int]     							= timestep * torch.ones(
              sample.shape[0]       ,		device=sample.device					)  # torch.repeat_interleave(timestep, sample.shape[0])
          snake_case			:  str     							= (timestep * (len(self.timesteps					) - 1)).long()

          # mps requires indices to be in the same device, so we use cpu as is the default with cuda
          snake_case			:  Any     							= timesteps.to(self.discrete_sigmas.device					)

          snake_case			:  Dict     							= self.discrete_sigmas[timesteps].to(sample.device					)
          snake_case			:  Union[str, Any]     							= self.get_adjacent_sigma(snake_case__       ,		snake_case__					).to(sample.device					)
          snake_case			:  Optional[Any]     							= torch.zeros_like(snake_case__					)
          snake_case			:  Dict     							= (sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          snake_case			:  int     							= diffusion.flatten()
          while len(diffusion.shape					) < len(sample.shape					):
                 snake_case			:  Any     							= diffusion.unsqueeze(-1					)
          snake_case			:  Tuple     							= drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          snake_case			:  Tuple     							= randn_tensor(
              sample.shape       ,		layout=sample.layout       ,		generator=snake_case__       ,		device=sample.device       ,		dtype=sample.dtype					)
          snake_case			:  List[str]     							= sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          snake_case			:  int     							= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
                 return (prev_sample, prev_sample_mean)

          return SdeVeOutput(prev_sample=snake_case__       ,		prev_sample_mean=snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   torch.FloatTensor       ,		snake_case__						:   torch.FloatTensor       ,		snake_case__						:   Optional[torch.Generator] = None       ,		snake_case__						:   bool = True       ,		)						->      Union[SchedulerOutput, Tuple]:


          '''simple docstring'''


          if self.timesteps is None:
                 raise ValueError(
                     "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          snake_case			:  str     							= randn_tensor(sample.shape       ,		layout=sample.layout       ,		generator=snake_case__					).to(sample.device					)

          # compute step size from the model_output, the noise, and the snr
          snake_case			:  Union[str, Any]     							= torch.norm(model_output.reshape(model_output.shape[0]       ,		-1					)       ,		dim=-1					).mean()
          snake_case			:  List[Any]     							= torch.norm(noise.reshape(noise.shape[0]       ,		-1					)       ,		dim=-1					).mean()
          snake_case			:  List[Any]     							= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          snake_case			:  Optional[Any]     							= step_size * torch.ones(sample.shape[0]					).to(sample.device					)
          # self.repeat_scalar(step_size, sample.shape[0])

          # compute corrected sample: model_output term and noise term
          snake_case			:  List[Any]     							= step_size.flatten()
          while len(step_size.shape					) < len(sample.shape					):
                 snake_case			:  Dict     							= step_size.unsqueeze(-1					)
          snake_case			:  Any     							= sample + step_size * model_output
          snake_case			:  int     							= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
                 return (prev_sample,)

          return SchedulerOutput(prev_sample=snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   torch.FloatTensor       ,		snake_case__						:   torch.FloatTensor       ,		snake_case__						:   torch.FloatTensor       ,		)						->      torch.FloatTensor:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= timesteps.to(original_samples.device					)
          snake_case			:  int     							= self.discrete_sigmas.to(original_samples.device					)[timesteps]
          snake_case			:  Any     							= (
              noise * sigmas[:, None, None, None]
              if noise is not None
              else torch.randn_like(snake_case__					) * sigmas[:, None, None, None]
          )
          snake_case			:  Optional[int]     							= noise + original_samples
          return noisy_samples



   def __len__(self						:   List[Any]					)						->      int:


          '''simple docstring'''


          return self.config.num_train_timesteps







class     UpperCAmelCase						:



   def __init__(self						:   Optional[Any]       ,		snake_case__						:   str = ""       ,		snake_case__						:   bool = False					)						->      None:


          '''simple docstring'''


          snake_case			:  dict[str, RadixNode]     							= {}

          # A node will be a leaf if the tree contains its word
          snake_case			:  int     							= is_leaf

          snake_case			:  Dict     							= prefix



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   str					)						->      tuple[str, str, str]:


          '''simple docstring'''


          snake_case			:  Any     							= 0
          for q, w in zip(self.prefix       ,		snake_case__					):
                 if q != w:
                        break

                 x += 1

          return self.prefix[:x], self.prefix[x:], word[x:]



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   list[str]					)						->      None:


          '''simple docstring'''


          for word in words:
                 self.insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   int       ,		snake_case__						:   str					)						->      None:


          '''simple docstring'''


          if self.prefix == word:
                 snake_case			:  str     							= True

          # Case 2: The node has no edges that have a prefix to the word
          # Solution: We create an edge from the current node to a new one
          # containing the word
          elif word[0] not in self.nodes:
                 snake_case			:  Optional[int]     							= RadixNode(prefix=snake_case__       ,		is_leaf=snake_case__					)

          else:
                 snake_case			:  Dict     							= self.nodes[word[0]]
                 snake_case							,   snake_case							,   snake_case			:  int     							= incoming_node.match(
                     snake_case__					)

                 # Case 3: The node prefix is equal to the matching
                 # Solution: We insert remaining word on the next node
                 if remaining_prefix == "":
                        self.nodes[matching_string[0]].insert(snake_case__					)

                 # Case 4: The word is greater equal to the matching
                 # Solution: Create a node in between both nodes, change
                 # prefixes and add the new node for the remaining word
                 else:
                        snake_case			:  Tuple     							= remaining_prefix

                        snake_case			:  Dict     							= self.nodes[matching_string[0]]
                        snake_case			:  Tuple     							= RadixNode(snake_case__       ,		snake_case__					)
                        snake_case			:  List[str]     							= aux_node

                        if remaining_word == "":
                               snake_case			:  Tuple     							= True
                        else:
                               self.nodes[matching_string[0]].insert(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[int]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # This applies when the word and the prefix are equal
                 elif remaining_word == "":
                        return incoming_node.is_leaf
                 # We have word remaining so we check the next node
                 else:
                        return incoming_node.find(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   str					)						->      bool:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.nodes.get(word[0]       ,		snake_case__					)
          if not incoming_node:
                 return False
          else:
                 snake_case							,   snake_case							,   snake_case			:  Optional[Any]     							= incoming_node.match(
                     snake_case__					)
                 # If there is remaining prefix, the word can't be on the tree
                 if remaining_prefix != "":
                        return False
                 # We have word remaining so we check the next node
                 elif remaining_word != "":
                        return incoming_node.delete(snake_case__					)
                 else:
                        # If it is not a leaf, we don't have to delete
                        if not incoming_node.is_leaf:
                               return False
                        else:
                               # We delete the nodes if no edges go from it
                               if len(incoming_node.nodes					) == 0:
                                      del self.nodes[word[0]]
                                      # We merge the current node with its only child
                                      if len(self.nodes					) == 1 and not self.is_leaf:
                                             snake_case			:  List[Any]     							= list(self.nodes.values()					)[0]
                                             snake_case			:  Optional[int]     							= merging_node.is_leaf
                                             self.prefix += merging_node.prefix
                                             snake_case			:  str     							= merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                               elif len(incoming_node.nodes					) > 1:
                                      snake_case			:  List[Any]     							= False
                               # If there is 1 edge, we merge it with its child
                               else:
                                      snake_case			:  Any     							= list(incoming_node.nodes.values()					)[0]
                                      snake_case			:  Any     							= merging_node.is_leaf
                                      incoming_node.prefix += merging_node.prefix
                                      snake_case			:  Union[str, Any]     							= merging_node.nodes

                               return True



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any       ,		snake_case__						:   int = 0					)						->      None:


          '''simple docstring'''


          if self.prefix != "":
                 print("-" * height       ,		self.prefix       ,		"  (leaf)" if self.is_leaf else ""					)

          for value in self.nodes.values():
                 value.print_tree(height + 1					)



def      UpperCamelCase   (       ):
       snake_case			:  Dict     							= "banana bananas bandana band apple all beast".split()
       snake_case			:  Tuple     							= RadixNode()
       root.insert_many(__lowerCamelCase       )

       assert all(root.find(__lowerCamelCase       ) for word in words       )
       assert not root.find("bandanas"       )
       assert not root.find("apps"       )
       root.delete("all"       )
       assert not root.find("all"       )
       root.delete("banana"       )
       assert not root.find("banana"       )
       assert root.find("bananas"       )

       return True


def      UpperCamelCase   (       ):
       assert test_trie()


def      UpperCamelCase   (       ):
       snake_case			:  Union[str, Any]     							= RadixNode()
       snake_case			:  List[str]     							= "banana bananas bandanas bandana band apple all beast".split()
       root.insert_many(__lowerCamelCase       )

       print("Words:"							,   __lowerCamelCase       )
       print("Tree:"       )
       root.print_tree()


if __name__ == "__main__":
   main()





