



'''simple docstring'''



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					UpperCAmelCase__	(		_lowerCAmelCase ):
					a      :     str        =							(UnCLIPScheduler,)



					def        UpperCAmelCase_ (    self			,				**UpperCamelCase     )     ->			Tuple:
						__lowerCAmelCase												=  {
						    "num_train_timesteps": 1000,
						    "variance_type": "fixed_small_log",
						    "clip_sample": True,
						    "clip_sample_range": 1.0,
						    "prediction_type": "epsilon",
						}

						config.update(**_lowerCAmelCase     )
						return config



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:







						for timesteps in [1, 5, 100, 1000]:
							self.check_over_configs(num_train_timesteps=_lowerCAmelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:







						for variance in ["fixed_small_log", "learned_range"]:
							self.check_over_configs(variance_type=_lowerCAmelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Any:







						for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=_lowerCAmelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:







						for clip_sample_range in [1, 5, 10, 20]:
							self.check_over_configs(clip_sample_range=_lowerCAmelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:







						for prediction_type in ["epsilon", "sample"]:
							self.check_over_configs(prediction_type=_lowerCAmelCase     )



					def        UpperCAmelCase_ (    self     )     ->			int:







						for time_step in [0, 500, 999]:
							for prev_timestep in [None, 5, 100, 250, 500, 750]:
								if prev_timestep is not None and prev_timestep >= time_step:
									continue

								self.check_over_forward(time_step=_lowerCAmelCase			,				prev_timestep=_lowerCAmelCase     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config(variance_type="fixed_small_log"     )
						__lowerCAmelCase												=  scheduler_class(**_lowerCAmelCase     )

						assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_000E-10     )     ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.0_54_96_25     )     ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.9_99_49_87     )     ) < 1E-5



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config(variance_type="learned_range"     )
						__lowerCAmelCase												=  scheduler_class(**_lowerCAmelCase     )

						__lowerCAmelCase												=  0.5

						assert scheduler._get_variance(1			,				predicted_variance=_lowerCAmelCase     ) - -10.1_71_27_90 < 1E-5
						assert scheduler._get_variance(487			,				predicted_variance=_lowerCAmelCase     ) - -5.7_99_80_52 < 1E-5
						assert scheduler._get_variance(999			,				predicted_variance=_lowerCAmelCase     ) - -0.0_01_00_11 < 1E-5



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**_lowerCAmelCase     )

						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter
						__lowerCAmelCase												=  torch.manual_seed(0     )

						for i, t in enumerate(_lowerCAmelCase     ):
							# 1. predict noise residual
							__lowerCAmelCase												=  model(_lowerCAmelCase			,				_lowerCAmelCase     )

							# 2. predict previous mean of sample x_t-1
							__lowerCAmelCase												=  scheduler.step(_lowerCAmelCase			,				_lowerCAmelCase			,				_lowerCAmelCase			,				generator=_lowerCAmelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(_lowerCAmelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(_lowerCAmelCase     )     )

						assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1E-2
						assert abs(result_mean.item() - 0.3_28_47_43     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			str:
						__lowerCAmelCase												=  self.scheduler_classes[0]
						__lowerCAmelCase												=  self.get_scheduler_config()
						__lowerCAmelCase												=  scheduler_class(**_lowerCAmelCase     )

						scheduler.set_timesteps(25     )

						__lowerCAmelCase												=  scheduler.timesteps

						__lowerCAmelCase												=  self.dummy_model()
						__lowerCAmelCase												=  self.dummy_sample_deter
						__lowerCAmelCase												=  torch.manual_seed(0     )

						for i, t in enumerate(_lowerCAmelCase     ):
							# 1. predict noise residual
							__lowerCAmelCase												=  model(_lowerCAmelCase			,				_lowerCAmelCase     )

							if i + 1 == timesteps.shape[0]:
								__lowerCAmelCase												=  None
							else:
								__lowerCAmelCase												=  timesteps[i + 1]

							# 2. predict previous mean of sample x_t-1
							__lowerCAmelCase												=  scheduler.step(
							    _lowerCAmelCase			,				_lowerCAmelCase			,				_lowerCAmelCase			,				prev_timestep=_lowerCAmelCase			,				generator=_lowerCAmelCase     ).prev_sample

							__lowerCAmelCase												=  pred_prev_sample

						__lowerCAmelCase												=  torch.sum(torch.abs(_lowerCAmelCase     )     )
						__lowerCAmelCase												=  torch.mean(torch.abs(_lowerCAmelCase     )     )

						assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1E-2
						assert abs(result_mean.item() - 0.3_36_20_38     ) < 1E-3



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						pass



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						pass




'''simple docstring'''



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






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					def __init__(    self     )     ->			List[str]:
						# test for the above condition
						self.test()



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  0
						__lowerCAmelCase												=  False
						while not completed:
							if counter == 1:
								self.reset()
							__lowerCAmelCase												=  self.advance()
							if not self.does_advance(UpperCamelCase     ):
								raise Exception(
								    "Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."     )

							__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  self.update(UpperCamelCase     )
							counter += 1

							if counter > 1_0000:
								raise Exception("update() does not fulfill the constraint."     )

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



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



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



					@abstractmethod
					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Any:
						raise NotImplementedError(
						    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )



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



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



					@abstractmethod
					def        UpperCAmelCase_ (    self			,				UpperCamelCase=False     )     ->			str:
						raise NotImplementedError(
						    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''     )






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					def __init__(    self			,				UpperCamelCase     )     ->			Dict:
						super(UpperCamelCase			,				self     ).__init__()

						if not isinstance(UpperCamelCase			,				UpperCamelCase     ) or len(UpperCamelCase     ) == 0:
							raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.'''     )
						if any((not isinstance(UpperCamelCase			,				UpperCamelCase     ) 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}.'''     )

						__lowerCAmelCase												=  token_ids

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



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						if self.completed:
							return None
						return self.token_ids[self.fulfilled_idx + 1]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			str:
						if not isinstance(UpperCamelCase			,				UpperCamelCase     ):
							raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase     )}'''     )

						if self.completed:
							return False

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



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			int:
						if not isinstance(UpperCamelCase			,				UpperCamelCase     ):
							raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase     )}'''     )

						__lowerCAmelCase												=  False
						__lowerCAmelCase												=  False
						__lowerCAmelCase												=  False

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



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  False
						__lowerCAmelCase												=  0



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						return self.seqlen - (self.fulfilled_idx + 1)



					def        UpperCAmelCase_ (    self			,				UpperCamelCase=False     )     ->			Optional[Any]:
						__lowerCAmelCase												=  PhrasalConstraint(self.token_ids     )

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

						return new_constraint






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase=True     )     ->			Optional[int]:
						__lowerCAmelCase												=  max([len(UpperCamelCase     ) for one in nested_token_ids]     )

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

								__lowerCAmelCase												=  level[token_id]

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

						__lowerCAmelCase												=  root



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			int:
						__lowerCAmelCase												=  self.trie

						for current_token in current_seq:
							__lowerCAmelCase												=  start[current_token]

						__lowerCAmelCase												=  list(start.keys()     )

						return next_tokens



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			str:
						__lowerCAmelCase												=  self.next_tokens(UpperCamelCase     )

						return len(UpperCamelCase     ) == 0



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Optional[int]:
						__lowerCAmelCase												=  list(root.values()     )
						if len(UpperCamelCase     ) == 0:
							return 1
						else:
							return sum([self.count_leaves(UpperCamelCase     ) for nn in next_nodes]     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.count_leaves(UpperCamelCase     )
						return len(UpperCamelCase     ) != leaf_count






class 					UpperCAmelCase__	(		UpperCamelCase__ ):



					def __init__(    self			,				UpperCamelCase     )     ->			List[Any]:
						super(UpperCamelCase			,				self     ).__init__()

						if not isinstance(UpperCamelCase			,				UpperCamelCase     ) or len(UpperCamelCase     ) == 0:
							raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''     )
						if any(not isinstance(UpperCamelCase			,				UpperCamelCase     ) 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(UpperCamelCase			,				UpperCamelCase     ) 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}.'''     )

						__lowerCAmelCase												=  DisjunctiveTrie(UpperCamelCase     )
						__lowerCAmelCase												=  nested_token_ids

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



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  self.trie.next_tokens(self.current_seq     )

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



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			List[str]:
						if not isinstance(UpperCamelCase			,				UpperCamelCase     ):
							raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase     )}'''     )

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

						return token_id in next_tokens



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			int:
						if not isinstance(UpperCamelCase			,				UpperCamelCase     ):
							raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase     )}'''     )

						__lowerCAmelCase												=  False
						__lowerCAmelCase												=  False
						__lowerCAmelCase												=  False

						if self.does_advance(UpperCamelCase     ):
							self.current_seq.append(UpperCamelCase     )
							__lowerCAmelCase												=  True
						else:
							__lowerCAmelCase												=  True
							self.reset()

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

						return stepped, completed, reset



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  False
						__lowerCAmelCase												=  []



					def        UpperCAmelCase_ (    self     )     ->			int:
						if self.completed:
							# since this can be completed without reaching max height
							return 0
						else:
							return self.seqlen - len(self.current_seq     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase=False     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  DisjunctiveConstraint(self.token_ids     )

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

						return new_constraint






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  constraints

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

						self.init_state()



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  []
						__lowerCAmelCase												=  None
						__lowerCAmelCase												=  [constraint.copy(stateful=UpperCamelCase     ) for constraint in self.constraints]



					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  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        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  []
						if self.inprogress_constraint is None:
							for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
								__lowerCAmelCase												=  constraint.advance()
								if isinstance(UpperCamelCase			,				UpperCamelCase     ):
									token_list.append(UpperCamelCase     )
								elif isinstance(UpperCamelCase			,				UpperCamelCase     ):
									token_list.extend(UpperCamelCase     )
						else:
							__lowerCAmelCase												=  self.inprogress_constraint.advance()
							if isinstance(UpperCamelCase			,				UpperCamelCase     ):
								token_list.append(UpperCamelCase     )
							elif isinstance(UpperCamelCase			,				UpperCamelCase     ):
								token_list.extend(UpperCamelCase     )

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



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			int:
						self.init_state()

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

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



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Dict:
						if not isinstance(UpperCamelCase			,				UpperCamelCase     ):
							raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.'''     )

						__lowerCAmelCase							,       __lowerCAmelCase												=  False, False

						if self.completed:
							__lowerCAmelCase												=  True
							__lowerCAmelCase												=  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

							__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  self.inprogress_constraint.update(UpperCamelCase     )
							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=UpperCamelCase     )     )
								__lowerCAmelCase												=  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     )
								__lowerCAmelCase												=  None

								if len(self.pending_constraints     ) == 0:
									# we're done!
									__lowerCAmelCase												=  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(UpperCamelCase     ):
									__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  pending_constraint.update(UpperCamelCase     )

									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(UpperCamelCase     )
										__lowerCAmelCase												=  None

									if not complete and stepped:
										__lowerCAmelCase												=  pending_constraint

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

										__lowerCAmelCase												=  (
										    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.

											__lowerCAmelCase												=  True

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

						return complete, stepped



					def        UpperCAmelCase_ (    self			,				UpperCamelCase=True     )     ->			str:
						__lowerCAmelCase												=  ConstraintListState(self.constraints     )  # we actually never though self.constraints objects
						# throughout this process. So it's at initialization state.

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

						return new_state