






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=1):
			if n_shave_prefix_segments >= 0:
						return ".".join(path.split('.')[n_shave_prefix_segments:])
			else:
						return ".".join(path.split('.')[:n_shave_prefix_segments])






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=0):
			SCREAMING_SNAKE_CASE						= []
			for old_item in old_list:
						SCREAMING_SNAKE_CASE						= old_item.replace('in_layers.0'							,       'norm1')
						SCREAMING_SNAKE_CASE						= new_item.replace('in_layers.2'							,       'conv1')

						SCREAMING_SNAKE_CASE						= new_item.replace('out_layers.0'							,       'norm2')
						SCREAMING_SNAKE_CASE						= new_item.replace('out_layers.3'							,       'conv2')

						SCREAMING_SNAKE_CASE						= new_item.replace('emb_layers.1'							,       'time_emb_proj')
						SCREAMING_SNAKE_CASE						= new_item.replace('skip_connection'							,       'conv_shortcut')

						SCREAMING_SNAKE_CASE						= shave_segments(_UpperCAmelCase							,       n_shave_prefix_segments=_UpperCAmelCase)

						mapping.append({'old': old_item, 'new': new_item})

			return mapping






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=0):
			SCREAMING_SNAKE_CASE						= []
			for old_item in old_list:
						SCREAMING_SNAKE_CASE						= old_item

						SCREAMING_SNAKE_CASE						= new_item.replace('norm.weight'							,       'group_norm.weight')
						SCREAMING_SNAKE_CASE						= new_item.replace('norm.bias'							,       'group_norm.bias')

						SCREAMING_SNAKE_CASE						= new_item.replace('proj_out.weight'							,       'proj_attn.weight')
						SCREAMING_SNAKE_CASE						= new_item.replace('proj_out.bias'							,       'proj_attn.bias')

						SCREAMING_SNAKE_CASE						= shave_segments(_UpperCAmelCase							,       n_shave_prefix_segments=_UpperCAmelCase)

						mapping.append({'old': old_item, 'new': new_item})

			return mapping






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None							,       _UpperCAmelCase=None):
			assert isinstance(_UpperCAmelCase							,       _UpperCAmelCase), "Paths should be a list of dicts containing 'old' and 'new' keys."

			# Splits the attention layers into three variables.
			if attention_paths_to_split is not None:
						for path, path_map in attention_paths_to_split.items():
									SCREAMING_SNAKE_CASE						= old_checkpoint[path]
									SCREAMING_SNAKE_CASE						= old_tensor.shape[0] // 3

									SCREAMING_SNAKE_CASE						= (-1, channels) if len(old_tensor.shape) == 3 else (-1)

									SCREAMING_SNAKE_CASE						= old_tensor.shape[0] // config['num_head_channels'] // 3

									SCREAMING_SNAKE_CASE						= old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= old_tensor.split(channels // num_heads							,       dim=1)

									SCREAMING_SNAKE_CASE						= query.reshape(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= key.reshape(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= value.reshape(_UpperCAmelCase)

			for path in paths:
						SCREAMING_SNAKE_CASE						= path['new']

						# These have already been assigned
						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						# Global renaming happens here
						SCREAMING_SNAKE_CASE						= new_path.replace('middle_block.0'							,       'mid_block.resnets.0')
						SCREAMING_SNAKE_CASE						= new_path.replace('middle_block.1'							,       'mid_block.attentions.0')
						SCREAMING_SNAKE_CASE						= new_path.replace('middle_block.2'							,       'mid_block.resnets.1')

						if additional_replacements is not None:
									for replacement in additional_replacements:
												SCREAMING_SNAKE_CASE						= new_path.replace(replacement['old']							,       replacement['new'])

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									SCREAMING_SNAKE_CASE						= old_checkpoint[path['old']][:, :, 0]
						else:
									SCREAMING_SNAKE_CASE						= old_checkpoint[path['old']]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= {}

			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.0.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.0.bias']
			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.2.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['time_embed.2.bias']

			SCREAMING_SNAKE_CASE						= checkpoint['input_blocks.0.0.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['input_blocks.0.0.bias']

			SCREAMING_SNAKE_CASE						= checkpoint['out.0.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['out.0.bias']
			SCREAMING_SNAKE_CASE						= checkpoint['out.2.weight']
			SCREAMING_SNAKE_CASE						= checkpoint['out.2.bias']

			# Retrieves the keys for the input blocks only
			SCREAMING_SNAKE_CASE						= len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'input_blocks' in layer})
			SCREAMING_SNAKE_CASE						= {
			    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
			    for layer_id in range(_UpperCAmelCase)
			}

			# Retrieves the keys for the middle blocks only
			SCREAMING_SNAKE_CASE						= len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'middle_block' in layer})
			SCREAMING_SNAKE_CASE						= {
			    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
			    for layer_id in range(_UpperCAmelCase)
			}

			# Retrieves the keys for the output blocks only
			SCREAMING_SNAKE_CASE						= len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'output_blocks' in layer})
			SCREAMING_SNAKE_CASE						= {
			    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
			    for layer_id in range(_UpperCAmelCase)
			}

			for i in range(1							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= (i - 1) // (config['num_res_blocks'] + 1)
						SCREAMING_SNAKE_CASE						= (i - 1) % (config['num_res_blocks'] + 1)

						SCREAMING_SNAKE_CASE						= [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
						SCREAMING_SNAKE_CASE						= [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

						if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
									SCREAMING_SNAKE_CASE						= checkpoint[
									    F'''input_blocks.{i}.0.op.weight'''
									]
									SCREAMING_SNAKE_CASE						= checkpoint[
									    F'''input_blocks.{i}.0.op.bias'''
									]
									continue

						SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= {'old': F'''input_blocks.{i}.0''', 'new': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						SCREAMING_SNAKE_CASE						= {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
						assign_to_checkpoint(
						    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path, resnet_op]							,       config=_UpperCAmelCase)

						if len(_UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= renew_attention_paths(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= {
									    'old': F'''input_blocks.{i}.1''',
									    'new': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									SCREAMING_SNAKE_CASE						= {
									    F'''input_blocks.{i}.1.qkv.bias''': {
									        'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
									        'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
									        'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
									    },
									    F'''input_blocks.{i}.1.qkv.weight''': {
									        'key': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
									        'query': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
									        'value': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
									    },
									}
									assign_to_checkpoint(
									    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path]							,       attention_paths_to_split=_UpperCAmelCase							,       config=_UpperCAmelCase							,       )

			SCREAMING_SNAKE_CASE						= middle_blocks[0]
			SCREAMING_SNAKE_CASE						= middle_blocks[1]
			SCREAMING_SNAKE_CASE						= middle_blocks[2]

			SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
			assign_to_checkpoint(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       config=_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
			assign_to_checkpoint(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       config=_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= renew_attention_paths(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= {
			    'middle_block.1.qkv.bias': {
			        'key': 'mid_block.attentions.0.key.bias',
			        'query': 'mid_block.attentions.0.query.bias',
			        'value': 'mid_block.attentions.0.value.bias',
			    },
			    'middle_block.1.qkv.weight': {
			        'key': 'mid_block.attentions.0.key.weight',
			        'query': 'mid_block.attentions.0.query.weight',
			        'value': 'mid_block.attentions.0.value.weight',
			    },
			}
			assign_to_checkpoint(
			    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       attention_paths_to_split=_UpperCAmelCase							,       config=_UpperCAmelCase)

			for i in range(_UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= i // (config['num_res_blocks'] + 1)
						SCREAMING_SNAKE_CASE						= i % (config['num_res_blocks'] + 1)
						SCREAMING_SNAKE_CASE						= [shave_segments(_UpperCAmelCase							,       2) for name in output_blocks[i]]
						SCREAMING_SNAKE_CASE						= {}

						for layer in output_block_layers:
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= layer.split('.')[0], shave_segments(_UpperCAmelCase							,       1)
									if layer_id in output_block_list:
												output_block_list[layer_id].append(_UpperCAmelCase)
									else:
												SCREAMING_SNAKE_CASE						= [layer_name]

						if len(_UpperCAmelCase) > 1:
									SCREAMING_SNAKE_CASE						= [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
									SCREAMING_SNAKE_CASE						= [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

									SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase)

									SCREAMING_SNAKE_CASE						= {'old': F'''output_blocks.{i}.0''', 'new': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
									assign_to_checkpoint(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path]							,       config=_UpperCAmelCase)

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												SCREAMING_SNAKE_CASE						= list(output_block_list.values()).index(['conv.weight', 'conv.bias'])
												SCREAMING_SNAKE_CASE						= checkpoint[
												    F'''output_blocks.{i}.{index}.conv.weight'''
												]
												SCREAMING_SNAKE_CASE						= checkpoint[
												    F'''output_blocks.{i}.{index}.conv.bias'''
												]

												# Clear attentions as they have been attributed above.
												if len(_UpperCAmelCase) == 2:
															SCREAMING_SNAKE_CASE						= []

									if len(_UpperCAmelCase):
												SCREAMING_SNAKE_CASE						= renew_attention_paths(_UpperCAmelCase)
												SCREAMING_SNAKE_CASE						= {
												    'old': F'''output_blocks.{i}.1''',
												    'new': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												SCREAMING_SNAKE_CASE						= {
												    F'''output_blocks.{i}.1.qkv.bias''': {
												        'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    F'''output_blocks.{i}.1.qkv.weight''': {
												        'key': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        'query': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        'value': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       additional_replacements=[meta_path]							,       attention_paths_to_split=to_split if any('qkv' in key for key in attentions) else None							,       config=_UpperCAmelCase							,       )
						else:
									SCREAMING_SNAKE_CASE						= renew_resnet_paths(_UpperCAmelCase							,       n_shave_prefix_segments=1)
									for path in resnet_0_paths:
												SCREAMING_SNAKE_CASE						= '.'.join(['output_blocks', str(_UpperCAmelCase), path['old']])
												SCREAMING_SNAKE_CASE						= '.'.join(['up_blocks', str(_UpperCAmelCase), 'resnets', str(_UpperCAmelCase), path['new']])

												SCREAMING_SNAKE_CASE						= checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
				a_		:						Tuple             =    argparse.ArgumentParser()

				parser.add_argument(
				    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
				)

				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The config json file corresponding to the architecture.',
				)

				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

				a_		:						Union[str, Any]             =    parser.parse_args()

				a_		:						Optional[int]             =    torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								a_		:						Dict             =    json.loads(f.read())

				a_		:						Any             =    convert_ldm_checkpoint(checkpoint, config)

				if "ldm" in config:
								del config["ldm"]

				a_		:						str             =    UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								a_		:						int             =    DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
								a_		:						Any             =    VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

								a_		:						str             =    LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)












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

class 		_snake_case  (  A__					):






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= False
						while not completed:
									if counter == 1:
												self.reset()
									SCREAMING_SNAKE_CASE						= self.advance()
									if not self.does_advance(a):
												raise Exception(
												    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.')

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.update(a)
									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    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')






			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')






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






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






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







			@abstractmethod
			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       int:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')



class 		_snake_case  (  A__					):






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

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

						SCREAMING_SNAKE_CASE						= token_ids

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






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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Any:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(a)}''')

						if self.completed:
									return False

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Optional[int]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(a)}''')

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= 0






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







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       int:
						SCREAMING_SNAKE_CASE						= PhrasalConstraint(self.token_ids)

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

						return new_constraint



class 		_snake_case  :






			def __init__(       self		,					a		,					a=True)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= max([len(a) for one in nested_token_ids])

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

												SCREAMING_SNAKE_CASE						= level[token_id]

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

						SCREAMING_SNAKE_CASE						= root






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.trie

						for current_token in current_seq:
									SCREAMING_SNAKE_CASE						= start[current_token]

						SCREAMING_SNAKE_CASE						= list(start.keys())

						return next_tokens






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= self.next_tokens(a)

						return len(a) == 0






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







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.count_leaves(a)
						return len(a) != leaf_count



class 		_snake_case  (  A__					):






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

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

						SCREAMING_SNAKE_CASE						= DisjunctiveTrie(a)
						SCREAMING_SNAKE_CASE						= nested_token_ids

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.trie.next_tokens(self.current_seq)

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






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

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

						return token_id in next_tokens






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(a)}''')

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False

						if self.does_advance(a):
									self.current_seq.append(a)
									SCREAMING_SNAKE_CASE						= True
						else:
									SCREAMING_SNAKE_CASE						= True
									self.reset()

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

						return stepped, completed, reset






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= []






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						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		,					a=False)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= DisjunctiveConstraint(self.token_ids)

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

						return new_constraint



class 		_snake_case  :






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

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

						self.init_state()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= [constraint.copy(stateful=a) for constraint in self.constraints]






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_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    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= []
						if self.inprogress_constraint is None:
									for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
												SCREAMING_SNAKE_CASE						= constraint.advance()
												if isinstance(a		,					a):
															token_list.append(a)
												elif isinstance(a		,					a):
															token_list.extend(a)
						else:
									SCREAMING_SNAKE_CASE						= self.inprogress_constraint.advance()
									if isinstance(a		,					a):
												token_list.append(a)
									elif isinstance(a		,					a):
												token_list.extend(a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						self.init_state()

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

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Union[str, Any]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''')

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= False, False

						if self.completed:
									SCREAMING_SNAKE_CASE						= True
									SCREAMING_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

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.inprogress_constraint.update(a)
									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=a))
												SCREAMING_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)
												SCREAMING_SNAKE_CASE						= None

												if len(self.pending_constraints) == 0:
															# we're done!
															SCREAMING_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(a):
															SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pending_constraint.update(a)

															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(a)
																		SCREAMING_SNAKE_CASE						= None

															if not complete and stepped:
																		SCREAMING_SNAKE_CASE						= pending_constraint

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

																		SCREAMING_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.

																					SCREAMING_SNAKE_CASE						= True

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

						return complete, stepped







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

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

						return new_state




