





"""simple docstring"""


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

import numpy as np
import torch

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				torch.FloatTensor
			__lowerCAmelCase     :				Optional[torch.FloatTensor]										=  None


def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=0.9_99							,     SCREAMING_SNAKE_CASE="cosine"							,     ):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE     ):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2     ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(SCREAMING_SNAKE_CASE     ):
																		return math.exp(t * -12.0     )

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""     )

						UpperCamelCase				:		Any				   =			[]
						for i in range(SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		int				   =			i / num_diffusion_timesteps
												UpperCamelCase				:		Optional[Any]				   =			(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE     ) / alpha_bar_fn(SCREAMING_SNAKE_CASE     )							,     SCREAMING_SNAKE_CASE     )     )
						return torch.tensor(SCREAMING_SNAKE_CASE							,     dtype=torch.floataa     )






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			@register_to_config
			def __init__(      self						,   _A = 1_0_0_0						,   _A = "fixed_small_log"						,   _A = True						,   _A = 1.0						,   _A = "epsilon"						,   _A = "squaredcos_cap_v2"						,   ):






									'''simple docstring'''







									if beta_schedule != "squaredcos_cap_v2":
															raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

									UpperCamelCase				:		Optional[int]				   =			betas_for_alpha_bar(_A    )

									UpperCamelCase				:		int				   =			1.0 - self.betas
									UpperCamelCase				:		Any				   =			torch.cumprod(self.alphas						,   dim=0    )
									UpperCamelCase				:		Any				   =			torch.tensor(1.0    )

									# standard deviation of the initial noise distribution
									UpperCamelCase				:		str				   =			1.0

									# setable values
									UpperCamelCase				:		str				   =			None
									UpperCamelCase				:		Union[str, Any]				   =			torch.from_numpy(np.arange(0						,   _A    )[::-1].copy()    )

									UpperCamelCase				:		List[str]				   =			variance_type







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									return sample







			def   _a      (      self						,   _A						,   _A = None    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			num_inference_steps
									UpperCamelCase				:		int				   =			(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
									UpperCamelCase				:		Tuple				   =			(np.arange(0						,   _A    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
									UpperCamelCase				:		Union[str, Any]				   =			torch.from_numpy(_A    ).to(_A    )







			def   _a      (      self						,   _A						,   _A=None						,   _A=None						,   _A=None    ):






									'''simple docstring'''







									if prev_timestep is None:
															UpperCamelCase				:		Any				   =			t - 1

									UpperCamelCase				:		Tuple				   =			self.alphas_cumprod[t]
									UpperCamelCase				:		str				   =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
									UpperCamelCase				:		Tuple				   =			1 - alpha_prod_t
									UpperCamelCase				:		int				   =			1 - alpha_prod_t_prev

									if prev_timestep == t - 1:
															UpperCamelCase				:		Dict				   =			self.betas[t]
									else:
															UpperCamelCase				:		Tuple				   =			1 - alpha_prod_t / alpha_prod_t_prev

									# 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
									UpperCamelCase				:		Optional[Any]				   =			beta_prod_t_prev / beta_prod_t * beta

									if variance_type is None:
															UpperCamelCase				:		List[Any]				   =			self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small_log":
															UpperCamelCase				:		Union[str, Any]				   =			torch.log(torch.clamp(_A						,   min=1e-2_0    )    )
															UpperCamelCase				:		List[str]				   =			torch.exp(0.5 * variance    )
									elif variance_type == "learned_range":
															# NOTE difference with DDPM scheduler
															UpperCamelCase				:		Union[str, Any]				   =			variance.log()
															UpperCamelCase				:		Tuple				   =			beta.log()

															UpperCamelCase				:		Any				   =			(predicted_variance + 1) / 2
															UpperCamelCase				:		Union[str, Any]				   =			frac * max_log + (1 - frac) * min_log

									return variance







			def   _a      (      self						,   _A						,   _A						,   _A						,   _A = None						,   _A=None						,   _A = True						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			timestep

									if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
															UpperCamelCase   ,						UpperCamelCase				:		Dict				   =			torch.split(_A						,   sample.shape[1]						,   dim=1    )
									else:
															UpperCamelCase				:		Dict				   =			None

									# 1. compute alphas, betas
									if prev_timestep is None:
															UpperCamelCase				:		Tuple				   =			t - 1

									UpperCamelCase				:		Tuple				   =			self.alphas_cumprod[t]
									UpperCamelCase				:		int				   =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
									UpperCamelCase				:		Union[str, Any]				   =			1 - alpha_prod_t
									UpperCamelCase				:		Union[str, Any]				   =			1 - alpha_prod_t_prev

									if prev_timestep == t - 1:
															UpperCamelCase				:		List[Any]				   =			self.betas[t]
															UpperCamelCase				:		str				   =			self.alphas[t]
									else:
															UpperCamelCase				:		List[str]				   =			1 - alpha_prod_t / alpha_prod_t_prev
															UpperCamelCase				:		List[str]				   =			1 - beta

									# 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":
															UpperCamelCase				:		int				   =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
															UpperCamelCase				:		int				   =			model_output
									else:
															raise ValueError(
															    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
															    """ for the UnCLIPScheduler."""    )

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
															UpperCamelCase				:		Dict				   =			torch.clamp(
															    _A						,   -self.config.clip_sample_range						,   self.config.clip_sample_range    )

									# 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
									UpperCamelCase				:		Dict				   =			(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
									UpperCamelCase				:		int				   =			alpha ** 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
									UpperCamelCase				:		str				   =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									UpperCamelCase				:		Union[str, Any]				   =			0
									if t > 0:
															UpperCamelCase				:		Any				   =			randn_tensor(
															    model_output.shape						,   dtype=model_output.dtype						,   generator=_A						,   device=model_output.device    )

															UpperCamelCase				:		int				   =			self._get_variance(
															    _A						,   predicted_variance=_A						,   prev_timestep=_A						,   )

															if self.variance_type == "fixed_small_log":
																					UpperCamelCase				:		Dict				   =			variance
															elif self.variance_type == "learned_range":
																					UpperCamelCase				:		str				   =			(0.5 * variance).exp()
															else:
																					raise ValueError(
																					    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																					    """ for the UnCLIPScheduler."""    )

															UpperCamelCase				:		str				   =			variance * variance_noise

									UpperCamelCase				:		List[str]				   =			pred_prev_sample + variance

									if not return_dict:
															return (pred_prev_sample,)

									return UnCLIPSchedulerOutput(prev_sample=_A						,   pred_original_sample=_A    )







			def   _a      (      self						,   _A						,   _A						,   _A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			self.alphas_cumprod.to(device=original_samples.device						,   dtype=original_samples.dtype    )
									UpperCamelCase				:		List[str]				   =			timesteps.to(original_samples.device    )

									UpperCamelCase				:		Optional[Any]				   =			alphas_cumprod[timesteps] ** 0.5
									UpperCamelCase				:		Optional[int]				   =			sqrt_alpha_prod.flatten()
									while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
															UpperCamelCase				:		Optional[int]				   =			sqrt_alpha_prod.unsqueeze(-1    )

									UpperCamelCase				:		Optional[Any]				   =			(1 - alphas_cumprod[timesteps]) ** 0.5
									UpperCamelCase				:		Dict				   =			sqrt_one_minus_alpha_prod.flatten()
									while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
															UpperCamelCase				:		Optional[int]				   =			sqrt_one_minus_alpha_prod.unsqueeze(-1    )

									UpperCamelCase				:		Optional[Any]				   =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
									return noisy_samples





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()





