


import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase	=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase	=							[]
for i in range(6):
			# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
			rename_keys.append(
			    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
			)
			rename_keys.append(
			    (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
			)
			rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
			rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
			rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
			rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
			rename_keys.append(
			    (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
			)
			rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
			rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
			rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
			# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
			rename_keys.append(
			    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
			)
			rename_keys.append(
			    (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
			)
			rename_keys.append(
			    (
			        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
			        f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
			    )
			)
			rename_keys.append(
			    (
			        f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
			        f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
			    )
			)
			rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
			rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
			rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
			rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
			rename_keys.append(
			    (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
			)
			rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
			rename_keys.append(
			    (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
			)
			rename_keys.append(
			    (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
			)
			rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
			rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
        ('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				state_dict.pop(SCREAMING_SNAKE_CASE					)
		lowercase__						=				val
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				OrderedDict()
		for key, value in state_dict.items():
				if "backbone.0.body" in key:
						lowercase__						=				key.replace('''backbone.0.body'''						,    '''backbone.conv_encoder.model'''					)
						lowercase__						=				value
				else:
						lowercase__						=				value

		return new_state_dict
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				''''''

		# first: transformer encoder
		for i in range(6					):
				# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
				lowercase__						=				state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'					)
				lowercase__						=				state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'					)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__						=				in_proj_weight[:2_56, :]
				lowercase__						=				in_proj_bias[:2_56]
				lowercase__						=				in_proj_weight[2_56:5_12, :]
				lowercase__						=				in_proj_bias[2_56:5_12]
				lowercase__						=				in_proj_weight[-2_56:, :]
				lowercase__						=				in_proj_bias[-2_56:]
		# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
		for i in range(6					):
				# read in weights + bias of input projection layer of self-attention
				lowercase__						=				state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'					)
				lowercase__						=				state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'					)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__						=				in_proj_weight[:2_56, :]
				lowercase__						=				in_proj_bias[:2_56]
				lowercase__						=				in_proj_weight[2_56:5_12, :]
				lowercase__						=				in_proj_bias[2_56:5_12]
				lowercase__						=				in_proj_weight[-2_56:, :]
				lowercase__						=				in_proj_bias[-2_56:]
				# read in weights + bias of input projection layer of cross-attention
				lowercase__						=				state_dict.pop(
				    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'					)
				lowercase__						=				state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'					)
				# next, add query, keys and values (in that order) of cross-attention to the state dict
				lowercase__						=				in_proj_weight_cross_attn[:2_56, :]
				lowercase__						=				in_proj_bias_cross_attn[:2_56]
				lowercase__						=				in_proj_weight_cross_attn[2_56:5_12, :]
				lowercase__						=				in_proj_bias_cross_attn[2_56:5_12]
				lowercase__						=				in_proj_weight_cross_attn[-2_56:, :]
				lowercase__						=				in_proj_bias_cross_attn[-2_56:]
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__					,		lowercase__						=				image.size
		lowercase__						=				max(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		lowercase__						=				8_00 if '''detection''' in checkpoint_url else 10_00
		lowercase__						=				target_max_size / current_max_size
		lowercase__						=				image.resize((int(round(scale * width					)					), int(round(scale * height					)					))					)

		return resized_image
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				F.to_tensor(SCREAMING_SNAKE_CASE					)
		lowercase__						=				F.normalize(SCREAMING_SNAKE_CASE						,    mean=[0.485, 0.456, 0.406]						,    std=[0.229, 0.224, 0.225]					)
		return image
@torch.no_grad()
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		logger.info('''Converting model...'''					)

		# load original state dict
		lowercase__						=				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''					)
		# rename keys
		for src, dest in rename_keys:
				rename_key(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		lowercase__						=				rename_backbone_keys(SCREAMING_SNAKE_CASE					)
		# query, key and value matrices need special treatment
		read_in_q_k_v(SCREAMING_SNAKE_CASE					)
		# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
		lowercase__						=				'''model.'''
		for key in state_dict.copy().keys():
				if not key.startswith('''class_labels_classifier'''					) and not key.startswith('''bbox_predictor'''					):
						lowercase__						=				state_dict.pop(SCREAMING_SNAKE_CASE					)
						lowercase__						=				val
    # create HuggingFace model and load state dict
		lowercase__						=				TableTransformerConfig(
		    backbone='''resnet18'''						,    mask_loss_coefficient=1						,    dice_loss_coefficient=1						,    ce_loss_coefficient=1						,    bbox_loss_coefficient=5						,    giou_loss_coefficient=2						,    eos_coefficient=0.4						,    class_cost=1						,    bbox_cost=5						,    giou_cost=2						,    )

		if "detection" in checkpoint_url:
				lowercase__						=				15
				lowercase__						=				2
				lowercase__						=				{0: '''table''', 1: '''table rotated'''}
				lowercase__						=				idalabel
				lowercase__						=				{v: k for k, v in idalabel.items()}
		else:
				lowercase__						=				1_25
				lowercase__						=				6
				lowercase__						=				{
				    0: '''table''',
				    1: '''table column''',
				    2: '''table row''',
				    3: '''table column header''',
				    4: '''table projected row header''',
				    5: '''table spanning cell''',
				}
				lowercase__						=				idalabel
				lowercase__						=				{v: k for k, v in idalabel.items()}

		lowercase__						=				DetrImageProcessor(
		    format='''coco_detection'''						,    max_size=8_00 if '''detection''' in checkpoint_url else 10_00					)
		lowercase__						=				TableTransformerForObjectDetection(SCREAMING_SNAKE_CASE					)
		model.load_state_dict(SCREAMING_SNAKE_CASE					)
		model.eval()

		# verify our conversion
		lowercase__						=				'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
		lowercase__						=				hf_hub_download(repo_id='''nielsr/example-pdf'''						,    repo_type='''dataset'''						,    filename=SCREAMING_SNAKE_CASE					)
		lowercase__						=				Image.open(SCREAMING_SNAKE_CASE					).convert('''RGB'''					)
		lowercase__						=				normalize(resize(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)					).unsqueeze(0					)

		lowercase__						=				model(SCREAMING_SNAKE_CASE					)

		if "detection" in checkpoint_url:
				lowercase__						=				(1, 15, 3)
				lowercase__						=				torch.tensor(
				    [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]					)
				lowercase__						=				torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]					)

		else:
				lowercase__						=				(1, 1_25, 7)
				lowercase__						=				torch.tensor(
				    [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]					)
				lowercase__						=				torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]					)

		assert outputs.logits.shape == expected_shape
		assert torch.allclose(outputs.logits[0, :3, :3]						,    SCREAMING_SNAKE_CASE						,    atol=1E-4					)
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,    SCREAMING_SNAKE_CASE						,    atol=1E-4					)
		print('''Looks ok!'''					)

		if pytorch_dump_folder_path is not None:
				# Save model and image processor
				logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'					)
				Path(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
				model.save_pretrained(SCREAMING_SNAKE_CASE					)
				image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)

		if push_to_hub:
				# Push model to HF hub
				logger.info('''Pushing model to the hub...'''					)
				lowercase__						=				(
				    '''microsoft/table-transformer-detection'''
				    if '''detection''' in checkpoint_url
				    else '''microsoft/table-transformer-structure-recognition'''
				)
				model.push_to_hub(SCREAMING_SNAKE_CASE					)
				image_processor.push_to_hub(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
			lowerCAmelCase	=							argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_url',
			    default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
			    type=str,
			    choices=[
			        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
			        'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
			    ],
			    help='URL of the Table Transformer checkpoint you\'d like to convert.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
			)
			lowerCAmelCase	=							parser.parse_args()
			convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






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


