
"""simple docstring"""







from abc import ABC, abstractmethod
from typing import List, Optional







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Union[str, Any]    ):
												# test for the above condition
												self.test()





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       0
												__snake_case     			=       False
												while not completed:
																			if counter == 1:
																										self.reset()
																			__snake_case     			=       self.advance()
																			if not self.does_advance(snake_case_    ):
																										raise Exception(
																										    "Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."    )

																			__snake_case			,   __snake_case			,   __snake_case     			=       self.update(snake_case_    )
																			counter += 1

																			if counter > 10000:
																										raise Exception("update() does not fulfill the constraint."    )

												if self.remaining() != 0:
																			raise Exception("Custom Constraint is not defined correctly."    )





					@abstractmethod
					def 							lowerCAmelCase      (				self   :							str    ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''    )





					@abstractmethod
					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							int    ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''    )





					@abstractmethod
					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							int    ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''    )





					@abstractmethod
					def 							lowerCAmelCase      (				self   :							Dict    ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''    )





					@abstractmethod
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''    )





					@abstractmethod
					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Dict=False    ):
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''    )







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Optional[int]							,						snake_case_   :							List[int]    ):
												super(snake_case_							,						self    ).__init__()

												if not isinstance(snake_case_							,						snake_case_    ) or len(snake_case_    ) == 0:
																			raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.'''    )
												if any((not isinstance(snake_case_							,						snake_case_    ) or token_id < 0) for token_id in token_ids    ):
																			raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'''    )

												__snake_case     			=       token_ids

												__snake_case     			=       len(self.token_ids    )
												__snake_case     			=       -1  # the index of the currently fulfilled step
												__snake_case     			=       False





					def 							lowerCAmelCase      (				self   :							Dict    ):
												if self.completed:
																			return None
												return self.token_ids[self.fulfilled_idx + 1]





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(snake_case_    )}'''    )

												if self.completed:
																			return False

												return token_id == self.token_ids[self.fulfilled_idx + 1]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(snake_case_    )}'''    )

												__snake_case     			=       False
												__snake_case     			=       False
												__snake_case     			=       False

												if self.does_advance(snake_case_    ):
																			self.fulfilled_idx += 1
																			__snake_case     			=       True
																			if self.fulfilled_idx == (self.seqlen - 1):
																										__snake_case     			=       True
																			__snake_case     			=       completed
												else:
																			# failed to make progress.
																			__snake_case     			=       True
																			self.reset()
												return stepped, completed, reset





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       False
												__snake_case     			=       0





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return self.seqlen - (self.fulfilled_idx + 1)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str=False    ):
												__snake_case     			=       PhrasalConstraint(self.token_ids    )

												if stateful:
																			__snake_case     			=       self.seqlen
																			__snake_case     			=       self.fulfilled_idx
																			__snake_case     			=       self.completed

												return new_constraint







class 							__magic_name__     :





					def __init__(				self   :							Tuple							,						snake_case_   :							List[List[int]]							,						snake_case_   :							int=True    ):
												__snake_case     			=       max([len(snake_case_    ) for one in nested_token_ids]    )

												__snake_case     			=       {}
												for token_ids in nested_token_ids:
																			__snake_case     			=       root
																			for tidx, token_id in enumerate(snake_case_    ):
																										if token_id not in level:
																																	__snake_case     			=       {}

																										__snake_case     			=       level[token_id]

												if no_subsets and self.has_subsets(snake_case_							,						snake_case_    ):
																			raise ValueError(
																			    "Each list in `nested_token_ids` can't be a complete subset of another list, but is"
																			    F''' {nested_token_ids}.'''    )

												__snake_case     			=       root





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							str    ):
												__snake_case     			=       self.trie

												for current_token in current_seq:
																			__snake_case     			=       start[current_token]

												__snake_case     			=       list(start.keys()    )

												return next_tokens





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							str    ):
												__snake_case     			=       self.next_tokens(snake_case_    )

												return len(snake_case_    ) == 0





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							int    ):
												__snake_case     			=       list(root.values()    )
												if len(snake_case_    ) == 0:
																			return 1
												else:
																			return sum([self.count_leaves(snake_case_    ) for nn in next_nodes]    )





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[int]							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       self.count_leaves(snake_case_    )
												return len(snake_case_    ) != leaf_count







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Optional[Any]							,						snake_case_   :							List[List[int]]    ):
												super(snake_case_							,						self    ).__init__()

												if not isinstance(snake_case_							,						snake_case_    ) or len(snake_case_    ) == 0:
																			raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''    )
												if any(not isinstance(snake_case_							,						snake_case_    ) for token_ids in nested_token_ids    ):
																			raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'''    )
												if any(
												    any((not isinstance(snake_case_							,						snake_case_    ) or token_id < 0) for token_id in token_ids    )
												    for token_ids in nested_token_ids    ):
																			raise ValueError(
																			    F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'''    )

												__snake_case     			=       DisjunctiveTrie(snake_case_    )
												__snake_case     			=       nested_token_ids

												__snake_case     			=       self.trie.max_height
												__snake_case     			=       []
												__snake_case     			=       False





					def 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       self.trie.next_tokens(self.current_seq    )

												if len(snake_case_    ) == 0:
																			return None
												else:
																			return token_list





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case_    )}'''    )

												__snake_case     			=       self.trie.next_tokens(self.current_seq    )

												return token_id in next_tokens





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case_    )}'''    )

												__snake_case     			=       False
												__snake_case     			=       False
												__snake_case     			=       False

												if self.does_advance(snake_case_    ):
																			self.current_seq.append(snake_case_    )
																			__snake_case     			=       True
												else:
																			__snake_case     			=       True
																			self.reset()

												__snake_case     			=       self.trie.reached_leaf(self.current_seq    )
												__snake_case     			=       completed

												return stepped, completed, reset





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       False
												__snake_case     			=       []





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												if self.completed:
																			# since this can be completed without reaching max height
																			return 0
												else:
																			return self.seqlen - len(self.current_seq    )





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							str=False    ):
												__snake_case     			=       DisjunctiveConstraint(self.token_ids    )

												if stateful:
																			__snake_case     			=       self.seqlen
																			__snake_case     			=       self.current_seq
																			__snake_case     			=       self.completed

												return new_constraint







class 							__magic_name__     :





					def __init__(				self   :							Optional[Any]							,						snake_case_   :							List[Constraint]    ):
												__snake_case     			=       constraints

												# max # of steps required to fulfill a given constraint
												__snake_case     			=       max([c.seqlen for c in constraints]    )
												__snake_case     			=       len(snake_case_    )
												__snake_case     			=       False

												self.init_state()





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       []
												__snake_case     			=       None
												__snake_case     			=       [constraint.copy(stateful=snake_case_    ) for constraint in self.constraints]





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       0
												if self.inprogress_constraint:
																			# extra points for having a constraint mid-fulfilled
																			add += self.max_seqlen - self.inprogress_constraint.remaining()

												return (len(self.complete_constraints    ) * self.max_seqlen) + add





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       []
												if self.inprogress_constraint is None:
																			for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
																										__snake_case     			=       constraint.advance()
																										if isinstance(snake_case_							,						snake_case_    ):
																																	token_list.append(snake_case_    )
																										elif isinstance(snake_case_							,						snake_case_    ):
																																	token_list.extend(snake_case_    )
												else:
																			__snake_case     			=       self.inprogress_constraint.advance()
																			if isinstance(snake_case_							,						snake_case_    ):
																										token_list.append(snake_case_    )
																			elif isinstance(snake_case_							,						snake_case_    ):
																										token_list.extend(snake_case_    )

												if len(snake_case_    ) == 0:
																			return None
												else:
																			return token_list





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Optional[List[int]]    ):
												self.init_state()

												if token_ids is not None:
																			for token in token_ids:
																										# completes or steps **one** constraint
																										__snake_case			,   __snake_case     			=       self.add(snake_case_    )

																										# the entire list of constraints are fulfilled
																										if self.completed:
																																	break





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							int    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.'''    )

												__snake_case			,   __snake_case     			=       False, False

												if self.completed:
																			__snake_case     			=       True
																			__snake_case     			=       False
																			return complete, stepped

												if self.inprogress_constraint is not None:
																			# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
																			# job, simply update the state

																			__snake_case			,   __snake_case			,   __snake_case     			=       self.inprogress_constraint.update(snake_case_    )
																			if reset:
																										# 1. If the next token breaks the progress, then we must restart.
																										#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

																										#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
																										#     constraint, not the full list of constraints.

																										self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case_    )    )
																										__snake_case     			=       None

																			if complete:
																										# 2. If the next token completes the constraint, move it to completed list, set
																										#     inprogress to None. If there are no pending constraints either, then this full list of constraints
																										#     is complete.

																										self.complete_constraints.append(self.inprogress_constraint    )
																										__snake_case     			=       None

																										if len(self.pending_constraints    ) == 0:
																																	# we're done!
																																	__snake_case     			=       True

												else:
																			# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
																			# of constraints?

																			for cidx, pending_constraint in enumerate(self.pending_constraints    ):
																										if pending_constraint.does_advance(snake_case_    ):
																																	__snake_case			,   __snake_case			,   __snake_case     			=       pending_constraint.update(snake_case_    )

																																	if not stepped:
																																								raise Exception(
																																								    "`constraint.update(token_id)` is not yielding incremental progress, "
																																								    "even though `constraint.does_advance(token_id)` is true."    )

																																	if complete:
																																								self.complete_constraints.append(snake_case_    )
																																								__snake_case     			=       None

																																	if not complete and stepped:
																																								__snake_case     			=       pending_constraint

																																	if complete or stepped:
																																								# If we made any progress at all, then it's at least not a "pending constraint".

																																								__snake_case     			=       (
																																								    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																																								)

																																								if len(self.pending_constraints    ) == 0 and self.inprogress_constraint is None:
																																															# If there's no longer any pending after this and no inprogress either, then we must be
																																															# complete.

																																															__snake_case     			=       True

																																								break  # prevent accidentally stepping through multiple constraints with just one token.

												return complete, stepped





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							List[str]=True    ):
												__snake_case     			=       ConstraintListState(self.constraints    )  # we actually never though self.constraints objects
												# throughout this process. So it's at initialization state.

												if stateful:
																			__snake_case     			=       [
																			    constraint.copy(stateful=snake_case_    ) for constraint in self.complete_constraints
																			]
																			if self.inprogress_constraint is not None:
																										__snake_case     			=       self.inprogress_constraint.copy(stateful=snake_case_    )
																			__snake_case     			=       [constraint.copy() for constraint in self.pending_constraints]

												return new_state


"""simple docstring"""







# 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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor
					_SCREAMING_SNAKE_CASE       :					torch.FloatTensor







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   1





					@register_to_config
					def __init__(				self   :							List[Any]							,						snake_case_   :							int = 2000							,						snake_case_   :							float = 0.15							,						snake_case_   :							float = 0.01							,						snake_case_   :							float = 1348.0							,						snake_case_   :							float = 1e-5							,						snake_case_   :							int = 1							,						):
												# standard deviation of the initial noise distribution
												__snake_case     			=       sigma_max

												# setable values
												__snake_case     			=       None

												self.set_sigmas(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[int] = None    ):
												return sample





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							Union[str, torch.device] = None    ):
												__snake_case     			=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

												__snake_case     			=       torch.linspace(1							,						snake_case_							,						snake_case_							,						device=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							int							,						snake_case_   :							float = None							,						snake_case_   :							float = None							,						snake_case_   :							float = None    ):
												__snake_case     			=       sigma_min if sigma_min is not None else self.config.sigma_min
												__snake_case     			=       sigma_max if sigma_max is not None else self.config.sigma_max
												__snake_case     			=       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     			=       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
												__snake_case     			=       torch.exp(torch.linspace(math.log(snake_case_    )							,						math.log(snake_case_    )							,						snake_case_    )    )
												__snake_case     			=       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Dict    ):
												return torch.where(
												    timesteps == 0							,						torch.zeros_like(t.to(timesteps.device    )    )							,						self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,						)





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							int							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												if self.timesteps is None:
																			raise ValueError(
																			    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

												__snake_case     			=       timestep * torch.ones(
												    sample.shape[0]							,						device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
												__snake_case     			=       (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     			=       timesteps.to(self.discrete_sigmas.device    )

												__snake_case     			=       self.discrete_sigmas[timesteps].to(sample.device    )
												__snake_case     			=       self.get_adjacent_sigma(snake_case_							,						snake_case_    ).to(sample.device    )
												__snake_case     			=       torch.zeros_like(snake_case_    )
												__snake_case     			=       (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     			=       diffusion.flatten()
												while len(diffusion.shape    ) < len(sample.shape    ):
																			__snake_case     			=       diffusion.unsqueeze(-1    )
												__snake_case     			=       drift - diffusion**2 * model_output

												#  equation 6: sample noise for the diffusion term of
												__snake_case     			=       randn_tensor(
												    sample.shape							,						layout=sample.layout							,						generator=snake_case_							,						device=sample.device							,						dtype=sample.dtype    )
												__snake_case     			=       sample - drift  # subtract because `dt` is a small negative timestep
												# TODO is the variable diffusion the correct scaling term for the noise?
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							Optional[torch.Generator] = None							,						snake_case_   :							bool = True							,						):
												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     			=       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     			=       torch.norm(model_output.reshape(model_output.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       torch.norm(noise.reshape(noise.shape[0]							,						-1    )							,						dim=-1    ).mean()
												__snake_case     			=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
												__snake_case     			=       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     			=       step_size.flatten()
												while len(step_size.shape    ) < len(sample.shape    ):
																			__snake_case     			=       step_size.unsqueeze(-1    )
												__snake_case     			=       sample + step_size * model_output
												__snake_case     			=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						snake_case_   :							torch.FloatTensor							,						):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												__snake_case     			=       timesteps.to(original_samples.device    )
												__snake_case     			=       self.discrete_sigmas.to(original_samples.device    )[timesteps]
												__snake_case     			=       (
												    noise * sigmas[:, None, None, None]
												    if noise is not None
												    else torch.randn_like(snake_case_    ) * sigmas[:, None, None, None]
												)
												__snake_case     			=       noise + original_samples
												return noisy_samples





					def __len__(				self   :							Tuple    ):
												return self.config.num_train_timesteps
