


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 	_a							(      UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      Dict  				=			KandinskyInpaintPipeline
							_lowercase     :      List[str]  				=			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
							_lowercase     :      Tuple  				=			[
							    '''prompt''',
							    '''negative_prompt''',
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							    '''mask_image''',
							]
							_lowercase     :      Optional[int]  				=			[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''negative_prompt''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							_lowercase     :      str  				=			False

							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       List[str]:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Optional[int]:





									"""simple docstring"""







									return 32

							@property
							def 				lowerCamelCase_			(							self:     Dict							)      ->       Union[str, Any]:





									"""simple docstring"""







									return self.time_input_dim

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       List[Any]:





									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									return 100

							@property
							def 				lowerCamelCase_			(							self:     int							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''							)
									return tokenizer

							@property
							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Dict:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				MCLIPConfig(
									    numDims=self.cross_attention_dim   ,						transformerDimensions=self.text_embedder_hidden_size   ,						hidden_size=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						vocab_size=1_005   ,						)

									lowercase__						=				MultilingualCLIP(UpperCamelCase_							)
									lowercase__						=				text_encoder.eval()

									return text_encoder

							@property
							def 				lowerCamelCase_			(							self:     Any							)      ->       List[Any]:





									"""simple docstring"""







									torch.manual_seed(0							)

									lowercase__						=				{
									    '''in_channels''': 9,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 8,
									    '''addition_embed_type''': '''text_image''',
									    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
									    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
									    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
									    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
									    '''layers_per_block''': 1,
									    '''encoder_hid_dim''': self.text_embedder_hidden_size,
									    '''encoder_hid_dim_type''': '''text_image_proj''',
									    '''cross_attention_dim''': self.cross_attention_dim,
									    '''attention_head_dim''': 4,
									    '''resnet_time_scale_shift''': '''scale_shift''',
									    '''class_embed_type''': None,
									}

									lowercase__						=				UNetaDConditionModel(**UpperCamelCase_							)
									return model

							@property
							def 				lowerCamelCase_			(							self:     str							)      ->       List[str]:





									"""simple docstring"""







									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}

							@property
							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									torch.manual_seed(0							)
									lowercase__						=				VQModel(**self.dummy_movq_kwargs							)
									return model

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.dummy_text_encoder
									lowercase__						=				self.dummy_tokenizer
									lowercase__						=				self.dummy_unet
									lowercase__						=				self.dummy_movq

									lowercase__						=				DDIMScheduler(
									    num_train_timesteps=1_000   ,						beta_schedule='''linear'''   ,						beta_start=0.00085   ,						beta_end=0.012   ,						clip_sample=UpperCamelCase_   ,						set_alpha_to_one=UpperCamelCase_   ,						steps_offset=1   ,						prediction_type='''epsilon'''   ,						thresholding=UpperCamelCase_   ,						)

									lowercase__						=				{
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Union[str, Any]=0							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
									lowercase__						=				floats_tensor((1, self.cross_attention_dim)   ,						rng=random.Random(seed + 1							)							).to(UpperCamelCase_							)
									# create init_image
									lowercase__						=				floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
									lowercase__						=				image.cpu().permute(0   ,						2   ,						3   ,						1							)[0]
									lowercase__						=				Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((256, 256)							)
									# create mask
									lowercase__						=				np.ones((64, 64)   ,						dtype=np.floataa							)
									lowercase__						=				0

									if str(UpperCamelCase_							).startswith('''mps'''							):
											lowercase__						=				torch.manual_seed(UpperCamelCase_							)
									else:
											lowercase__						=				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
									lowercase__						=				{
									    '''prompt''': '''horse''',
									    '''image''': init_image,
									    '''mask_image''': mask,
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''generator''': generator,
									    '''height''': 64,
									    '''width''': 64,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 4.0,
									    '''output_type''': '''np''',
									}
									return inputs

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				'''cpu'''

									lowercase__						=				self.get_dummy_components()

									lowercase__						=				self.pipeline_class(**UpperCamelCase_							)
									lowercase__						=				pipe.to(UpperCamelCase_							)

									pipe.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				pipe(**self.get_dummy_inputs(UpperCamelCase_							)							)
									lowercase__						=				output.images

									lowercase__						=				pipe(
									    **self.get_dummy_inputs(UpperCamelCase_							)   ,						return_dict=UpperCamelCase_   ,						)[0]

									lowercase__						=				image[0, -3:, -3:, -1]
									lowercase__						=				image_from_tuple[0, -3:, -3:, -1]

									print(f'image.shape {image.shape}'							)

									assert image.shape == (1, 64, 64, 3)

									lowercase__						=				np.array(
									    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]							)

									assert (
									    np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
									), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
									), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'


							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Union[str, Any]:





									"""simple docstring"""







									super().test_inference_batch_single_identical(expected_max_diff=3E-3							)


@slow
@require_torch_gpu
class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Dict							)      ->       int:





									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''							)

									lowercase__						=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''							)
									lowercase__						=				np.ones((768, 768)   ,						dtype=np.floataa							)
									lowercase__						=				0

									lowercase__						=				'''a hat'''

									lowercase__						=				KandinskyPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-prior'''   ,						torch_dtype=torch.floataa							)
									pipe_prior.to(UpperCamelCase_							)

									lowercase__						=				KandinskyInpaintPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-1-inpaint'''   ,						torch_dtype=torch.floataa							)
									lowercase__						=				pipeline.to(UpperCamelCase_							)
									pipeline.set_progress_bar_config(disable=UpperCamelCase_							)

									lowercase__						=				torch.Generator(device='''cpu'''							).manual_seed(0							)
									lowercase__					,		lowercase__						=				pipe_prior(
									    UpperCamelCase_   ,						generator=UpperCamelCase_   ,						num_inference_steps=5   ,						negative_prompt=''''''   ,						).to_tuple()

									lowercase__						=				pipeline(
									    UpperCamelCase_   ,						image=UpperCamelCase_   ,						mask_image=UpperCamelCase_   ,						image_embeds=UpperCamelCase_   ,						negative_image_embeds=UpperCamelCase_   ,						generator=UpperCamelCase_   ,						num_inference_steps=100   ,						height=768   ,						width=768   ,						output_type='''np'''   ,						)

									lowercase__						=				output.images[0]

									assert image.shape == (768, 768, 3)

									assert_mean_pixel_difference(UpperCamelCase_   ,						UpperCamelCase_							)






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


class 	_a							(      UpperCamelCase__      ):

							def __init__(							self:     Optional[int]							)      ->       Union[str, Any]:





									"""simple docstring"""







									self.test()

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





									"""simple docstring"""







									lowercase__						=				0
									lowercase__						=				False
									while not completed:
											if counter == 1:
													self.reset()
											lowercase__						=				self.advance()
											if not self.does_advance(UpperCamelCase_							):
													raise Exception(
													    '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''							)

											lowercase__					,		lowercase__					,		lowercase__						=				self.update(UpperCamelCase_							)
											counter += 1

											if counter > 10_000:
													raise Exception('''update() does not fulfill the constraint.'''							)

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

							@abstractmethod
							def 				lowerCamelCase_			(							self:     int							)      ->       Any:





									"""simple docstring"""







									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)

							@abstractmethod
							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     int							)      ->       Optional[int]:





									"""simple docstring"""







									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)

							@abstractmethod
							def 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     int							)      ->       str:





									"""simple docstring"""







									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)

							@abstractmethod
							def 				lowerCamelCase_			(							self:     Tuple							)      ->       List[Any]:





									"""simple docstring"""







									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)

							@abstractmethod
							def 				lowerCamelCase_			(							self:     Any							)      ->       Tuple:





									"""simple docstring"""







									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)


							@abstractmethod
							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     int=False							)      ->       Any:





									"""simple docstring"""







									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)


class 	_a							(      UpperCamelCase__      ):

							def __init__(							self:     str   ,						UpperCamelCase_:     List[int]							)      ->       Tuple:





									"""simple docstring"""







									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}.'							)

									lowercase__						=				token_ids

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

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Tuple:





									"""simple docstring"""







									if self.completed:
											return None
									return self.token_ids[self.fulfilled_idx + 1]

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     int							)      ->       Optional[Any]:





									"""simple docstring"""







									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 				lowerCamelCase_			(							self:     Optional[int]   ,						UpperCamelCase_:     int							)      ->       Dict:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
											raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase_							)}'							)

									lowercase__						=				False
									lowercase__						=				False
									lowercase__						=				False

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

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__						=				False
									lowercase__						=				0

							def 				lowerCamelCase_			(							self:     Any							)      ->       Dict:





									"""simple docstring"""







									return self.seqlen - (self.fulfilled_idx + 1)


							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Union[str, Any]=False							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				PhrasalConstraint(self.token_ids							)

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

									return new_constraint


class 	_a							:

							def __init__(							self:     Union[str, Any]   ,						UpperCamelCase_:     List[List[int]]   ,						UpperCamelCase_:     int=True							)      ->       int:





									"""simple docstring"""







									lowercase__						=				max([len(UpperCamelCase_							) for one in nested_token_ids]							)

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

													lowercase__						=				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}.'							)

									lowercase__						=				root

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     str							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				self.trie

									for current_token in current_seq:
											lowercase__						=				start[current_token]

									lowercase__						=				list(start.keys()							)

									return next_tokens

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Tuple							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.next_tokens(UpperCamelCase_							)

									return len(UpperCamelCase_							) == 0

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     Optional[Any]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				list(root.values()							)
									if len(UpperCamelCase_							) == 0:
											return 1
									else:
											return sum([self.count_leaves(UpperCamelCase_							) for nn in next_nodes]							)


							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     int							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				self.count_leaves(UpperCamelCase_							)
									return len(UpperCamelCase_							) != leaf_count


class 	_a							(      UpperCamelCase__      ):

							def __init__(							self:     Optional[int]   ,						UpperCamelCase_:     List[List[int]]							)      ->       List[Any]:





									"""simple docstring"""







									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}.'							)

									lowercase__						=				DisjunctiveTrie(UpperCamelCase_							)
									lowercase__						=				nested_token_ids

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

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





									"""simple docstring"""







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

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

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     int							)      ->       Tuple:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
											raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase_							)}'							)

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

									return token_id in next_tokens

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     int							)      ->       Dict:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
											raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase_							)}'							)

									lowercase__						=				False
									lowercase__						=				False
									lowercase__						=				False

									if self.does_advance(UpperCamelCase_							):
											self.current_seq.append(UpperCamelCase_							)
											lowercase__						=				True
									else:
											lowercase__						=				True
											self.reset()

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

									return stepped, completed, reset

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				False
									lowercase__						=				[]

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Optional[Any]:





									"""simple docstring"""







									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:     Any   ,						UpperCamelCase_:     Optional[Any]=False							)      ->       str:





									"""simple docstring"""







									lowercase__						=				DisjunctiveConstraint(self.token_ids							)

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

									return new_constraint


class 	_a							:

							def __init__(							self:     int   ,						UpperCamelCase_:     List[Constraint]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				constraints

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

									self.init_state()

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				[]
									lowercase__						=				None
									lowercase__						=				[constraint.copy(stateful=UpperCamelCase_							) for constraint in self.constraints]

							def 				lowerCamelCase_			(							self:     str							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				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:     Union[str, Any]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				[]
									if self.inprogress_constraint is None:
											for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
													lowercase__						=				constraint.advance()
													if isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
															token_list.append(UpperCamelCase_							)
													elif isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
															token_list.extend(UpperCamelCase_							)
									else:
											lowercase__						=				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 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     Optional[List[int]]							)      ->       Optional[int]:





									"""simple docstring"""







									self.init_state()

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

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

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     int							)      ->       int:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_   ,						UpperCamelCase_							):
											raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.'							)

									lowercase__					,		lowercase__						=				False, False

									if self.completed:
											lowercase__						=				True
											lowercase__						=				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

											lowercase__					,		lowercase__					,		lowercase__						=				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_							)							)
													lowercase__						=				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							)
													lowercase__						=				None

													if len(self.pending_constraints							) == 0:
															# we're done!
															lowercase__						=				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_							):
															lowercase__					,		lowercase__					,		lowercase__						=				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_							)
																	lowercase__						=				None

															if not complete and stepped:
																	lowercase__						=				pending_constraint

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

																	lowercase__						=				(
																	    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.

																			lowercase__						=				True

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

									return complete, stepped


							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Optional[Any]=True							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				ConstraintListState(self.constraints							)  # we actually never though self.constraints objects
									# throughout this process. So it's at initialization state.

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

									return new_state


