



"""simple docstring"""







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__		:    Optional[Any]				   =     transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def     UpperCamelCase_   (   lowerCAmelCase__				:    Union[str, Any]						)						->       int:

		"""simple docstring"""




		if isinstance(_A   ,       torch.Tensor						):
				return image
		elif isinstance(_A   ,       PIL.Image.Image						):
				lowerCAmelCase_       :    str								=			[image]

		lowerCAmelCase_       :    Dict								=			[trans(img.convert('RGB'						)						) for img in image]
		lowerCAmelCase_       :    Optional[int]								=			torch.stack(_A						)
		return image







class 				UpperCamelCase__       (     lowerCamelCase__  ):




						"""simple docstring"""




						def __init__(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				str	):
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								lowerCAmelCase_       :    str								=			DDIMScheduler.from_config(scheduler.config	)

								self.register_modules(unet=__snake_case   ,      scheduler=__snake_case	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]	):
								if strength < 0 or strength > 1:
										raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}"	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
								# get the original timestep using init_timestep
								lowerCAmelCase_       :    Union[str, Any]								=			min(int(num_inference_steps * strength	)   ,      __snake_case	)

								lowerCAmelCase_       :    str								=			max(num_inference_steps - init_timestep   ,      0	)
								lowerCAmelCase_       :    int								=			self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict=None	):
								if not isinstance(__snake_case   ,      (torch.Tensor, PIL.Image.Image, list)	):
										raise ValueError(
										    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case	)}"	)

								lowerCAmelCase_       :    Union[str, Any]								=			image.to(device=__snake_case   ,      dtype=__snake_case	)

								if isinstance(__snake_case   ,      __snake_case	) and len(__snake_case	) != batch_size:
										raise ValueError(
										    F"You have passed a list of generators of length {len(__snake_case	)}, but requested an effective batch"
										    F" size of {batch_size}. Make sure the batch size matches the length of the generators."	)

								lowerCAmelCase_       :    List[Any]								=			init_latents.shape
								lowerCAmelCase_       :    str								=			randn_tensor(__snake_case   ,      generator=__snake_case   ,      device=__snake_case   ,      dtype=__snake_case	)

								# get latents
								print('add noise to latents at timestep'   ,      __snake_case	)
								lowerCAmelCase_       :    List[Any]								=			self.scheduler.add_noise(__snake_case   ,      __snake_case   ,      __snake_case	)
								lowerCAmelCase_       :    Dict								=			init_latents

								return latents





						@torch.no_grad()
						def __call__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[torch.FloatTensor, PIL.Image.Image] = None   ,      SCREAMING_SNAKE_CASE_    :				float = 0.8   ,      SCREAMING_SNAKE_CASE_    :				int = 1   ,      SCREAMING_SNAKE_CASE_    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,      SCREAMING_SNAKE_CASE_    :				float = 0.0   ,      SCREAMING_SNAKE_CASE_    :				int = 5_0   ,      SCREAMING_SNAKE_CASE_    :				Optional[bool] = None   ,      SCREAMING_SNAKE_CASE_    :				Optional[str] = "pil"   ,      SCREAMING_SNAKE_CASE_    :				bool = True   ,      ):
								self.check_inputs(__snake_case	)

								# 2. Preprocess image
								lowerCAmelCase_       :    Tuple								=			preprocess(__snake_case	)

								# 3. set timesteps
								self.scheduler.set_timesteps(__snake_case   ,      device=self.device	)
								lowerCAmelCase_       :    Optional[Any]								=			self.get_timesteps(__snake_case   ,      __snake_case   ,      self.device	)
								lowerCAmelCase_       :    List[str]								=			timesteps[:1].repeat(__snake_case	)

								# 4. Prepare latent variables
								lowerCAmelCase_       :    List[str]								=			self.prepare_latents(__snake_case   ,      __snake_case   ,      __snake_case   ,      self.unet.dtype   ,      self.device   ,      __snake_case	)
								lowerCAmelCase_       :    Optional[Any]								=			latents

								# 5. Denoising loop
								for t in self.progress_bar(__snake_case	):
										# 1. predict noise model_output
										lowerCAmelCase_       :    List[Any]								=			self.unet(__snake_case   ,      __snake_case	).sample

										# 2. predict previous mean of image x_t-1 and add variance depending on eta
										# eta corresponds to η in paper and should be between [0, 1]
										# do x_t -> x_t-1
										lowerCAmelCase_       :    Optional[int]								=			self.scheduler.step(
										    __snake_case   ,      __snake_case   ,      __snake_case   ,      eta=__snake_case   ,      use_clipped_model_output=__snake_case   ,      generator=__snake_case   ,      ).prev_sample

								lowerCAmelCase_       :    Optional[Any]								=			(image / 2 + 0.5).clamp(0   ,      1	)
								lowerCAmelCase_       :    Union[str, Any]								=			image.cpu().permute(0   ,      2   ,      3   ,      1	).numpy()
								if output_type == "pil":
										lowerCAmelCase_       :    List[str]								=			self.numpy_to_pil(__snake_case	)

								if not return_dict:
										return (image, latent_timestep.item())

								return ImagePipelineOutput(images=__snake_case	)










"""simple docstring"""







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







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      def __init__(				self    :				int	):
        # test for the above condition
        self.test()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Any								=			0
        lowerCAmelCase_       :    Optional[Any]								=			False
        while not completed:
          if counter == 1:
            self.reset()
          lowerCAmelCase_       :    Any								=			self.advance()
          if not self.does_advance(SCREAMING_SNAKE_CASE_	):
            raise Exception(
                'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'	)

          lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    List[str]								=			self.update(SCREAMING_SNAKE_CASE_	)
          counter += 1

          if counter > 1_0_0_0_0:
            raise Exception('update() does not fulfill the constraint.'	)

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



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



      @abstractmethod
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				int	):
        raise NotImplementedError(
            F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)



      @abstractmethod
      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        raise NotImplementedError(
            F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)



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



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





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







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      def __init__(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				List[int]	):
        super(SCREAMING_SNAKE_CASE_   ,      self	).__init__()

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

        lowerCAmelCase_       :    Union[str, Any]								=			token_ids

        lowerCAmelCase_       :    Union[str, Any]								=			len(self.token_ids	)
        lowerCAmelCase_       :    Union[str, Any]								=			-1  # the index of the currently fulfilled step
        lowerCAmelCase_       :    Dict								=			False



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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				int	):
        if not isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
          raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(SCREAMING_SNAKE_CASE_	)}"	)

        if self.completed:
          return False

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				int	):
        if not isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
          raise ValueError(F"`token_id` has to be an `int`, but is {token_id} of type {type(SCREAMING_SNAKE_CASE_	)}"	)

        lowerCAmelCase_       :    List[Any]								=			False
        lowerCAmelCase_       :    Optional[int]								=			False
        lowerCAmelCase_       :    Union[str, Any]								=			False

        if self.does_advance(SCREAMING_SNAKE_CASE_	):
          self.fulfilled_idx += 1
          lowerCAmelCase_       :    Optional[int]								=			True
          if self.fulfilled_idx == (self.seqlen - 1):
            lowerCAmelCase_       :    List[str]								=			True
          lowerCAmelCase_       :    List[str]								=			completed
        else:
          # failed to make progress.
          lowerCAmelCase_       :    Optional[Any]								=			True
          self.reset()
        return stepped, completed, reset



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Optional[int]								=			False
        lowerCAmelCase_       :    int								=			0



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        return self.seqlen - (self.fulfilled_idx + 1)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple=False	):
        lowerCAmelCase_       :    Any								=			PhrasalConstraint(self.token_ids	)

        if stateful:
          lowerCAmelCase_       :    int								=			self.seqlen
          lowerCAmelCase_       :    Dict								=			self.fulfilled_idx
          lowerCAmelCase_       :    Optional[Any]								=			self.completed

        return new_constraint







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[List[int]]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=True	):
        lowerCAmelCase_       :    Tuple								=			max([len(SCREAMING_SNAKE_CASE_	) for one in nested_token_ids]	)

        lowerCAmelCase_       :    Optional[Any]								=			{}
        for token_ids in nested_token_ids:
          lowerCAmelCase_       :    Union[str, Any]								=			root
          for tidx, token_id in enumerate(SCREAMING_SNAKE_CASE_	):
            if token_id not in level:
              lowerCAmelCase_       :    Dict								=			{}

            lowerCAmelCase_       :    Tuple								=			level[token_id]

        if no_subsets and self.has_subsets(SCREAMING_SNAKE_CASE_   ,      SCREAMING_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}."	)

        lowerCAmelCase_       :    Union[str, Any]								=			root



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        lowerCAmelCase_       :    str								=			self.trie

        for current_token in current_seq:
          lowerCAmelCase_       :    Optional[int]								=			start[current_token]

        lowerCAmelCase_       :    Dict								=			list(start.keys()	)

        return next_tokens



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				Any	):
        lowerCAmelCase_       :    Any								=			self.next_tokens(SCREAMING_SNAKE_CASE_	)

        return len(SCREAMING_SNAKE_CASE_	) == 0



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        lowerCAmelCase_       :    Tuple								=			list(root.values()	)
        if len(SCREAMING_SNAKE_CASE_	) == 0:
          return 1
        else:
          return sum([self.count_leaves(SCREAMING_SNAKE_CASE_	) for nn in next_nodes]	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]	):
        lowerCAmelCase_       :    Any								=			self.count_leaves(SCREAMING_SNAKE_CASE_	)
        return len(SCREAMING_SNAKE_CASE_	) != leaf_count







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




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

        if not isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) or len(SCREAMING_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(SCREAMING_SNAKE_CASE_   ,      SCREAMING_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(SCREAMING_SNAKE_CASE_   ,      SCREAMING_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}."	)

        lowerCAmelCase_       :    Dict								=			DisjunctiveTrie(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			nested_token_ids

        lowerCAmelCase_       :    Tuple								=			self.trie.max_height
        lowerCAmelCase_       :    Union[str, Any]								=			[]
        lowerCAmelCase_       :    Optional[Any]								=			False



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    int								=			self.trie.next_tokens(self.current_seq	)

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



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

        lowerCAmelCase_       :    Optional[int]								=			self.trie.next_tokens(self.current_seq	)

        return token_id in next_tokens



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

        lowerCAmelCase_       :    int								=			False
        lowerCAmelCase_       :    Dict								=			False
        lowerCAmelCase_       :    Optional[Any]								=			False

        if self.does_advance(SCREAMING_SNAKE_CASE_	):
          self.current_seq.append(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    List[Any]								=			True
        else:
          lowerCAmelCase_       :    List[str]								=			True
          self.reset()

        lowerCAmelCase_       :    Dict								=			self.trie.reached_leaf(self.current_seq	)
        lowerCAmelCase_       :    List[str]								=			completed

        return stepped, completed, reset



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    int								=			False
        lowerCAmelCase_       :    Optional[Any]								=			[]



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





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Dict=False	):
        lowerCAmelCase_       :    Dict								=			DisjunctiveConstraint(self.token_ids	)

        if stateful:
          lowerCAmelCase_       :    Dict								=			self.seqlen
          lowerCAmelCase_       :    Optional[Any]								=			self.current_seq
          lowerCAmelCase_       :    List[Any]								=			self.completed

        return new_constraint







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Constraint]	):
        lowerCAmelCase_       :    Optional[int]								=			constraints

        # max # of steps required to fulfill a given constraint
        lowerCAmelCase_       :    Optional[int]								=			max([c.seqlen for c in constraints]	)
        lowerCAmelCase_       :    List[str]								=			len(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[str]								=			False

        self.init_state()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Tuple								=			[]
        lowerCAmelCase_       :    int								=			None
        lowerCAmelCase_       :    Optional[int]								=			[constraint.copy(stateful=SCREAMING_SNAKE_CASE_	) for constraint in self.constraints]



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        lowerCAmelCase_       :    Optional[Any]								=			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 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    List[str]								=			[]
        if self.inprogress_constraint is None:
          for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
            lowerCAmelCase_       :    List[Any]								=			constraint.advance()
            if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
              token_list.append(SCREAMING_SNAKE_CASE_	)
            elif isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
              token_list.extend(SCREAMING_SNAKE_CASE_	)
        else:
          lowerCAmelCase_       :    Dict								=			self.inprogress_constraint.advance()
          if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            token_list.append(SCREAMING_SNAKE_CASE_	)
          elif isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            token_list.extend(SCREAMING_SNAKE_CASE_	)

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[List[int]]	):
        self.init_state()

        if token_ids is not None:
          for token in token_ids:
            # completes or steps **one** constraint
            lowerCAmelCase_						,lowerCAmelCase_       :    Union[str, Any]								=			self.add(SCREAMING_SNAKE_CASE_	)

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				int	):
        if not isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
          raise ValueError(F"`token_id` should be an `int`, but is `{token_id}`."	)

        lowerCAmelCase_						,lowerCAmelCase_       :    Tuple								=			False, False

        if self.completed:
          lowerCAmelCase_       :    Any								=			True
          lowerCAmelCase_       :    Dict								=			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_       :    Any								=			self.inprogress_constraint.update(SCREAMING_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=SCREAMING_SNAKE_CASE_	)	)
            lowerCAmelCase_       :    Optional[int]								=			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_       :    str								=			None

            if len(self.pending_constraints	) == 0:
              # we're done!
              lowerCAmelCase_       :    Union[str, Any]								=			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(SCREAMING_SNAKE_CASE_	):
              lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    str								=			pending_constraint.update(SCREAMING_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(SCREAMING_SNAKE_CASE_	)
                lowerCAmelCase_       :    Any								=			None

              if not complete and stepped:
                lowerCAmelCase_       :    Optional[Any]								=			pending_constraint

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

                lowerCAmelCase_       :    Optional[int]								=			(
                    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_       :    Optional[Any]								=			True

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

        return complete, stepped





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str=True	):
        lowerCAmelCase_       :    List[Any]								=			ConstraintListState(self.constraints	)  # we actually never though self.constraints objects
        # throughout this process. So it's at initialization state.

        if stateful:
          lowerCAmelCase_       :    Any								=			[
              constraint.copy(stateful=SCREAMING_SNAKE_CASE_	) for constraint in self.complete_constraints
          ]
          if self.inprogress_constraint is not None:
            lowerCAmelCase_       :    List[str]								=			self.inprogress_constraint.copy(stateful=SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    List[Any]								=			[constraint.copy() for constraint in self.pending_constraints]

        return new_state





