





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def 							a_	(    __magic_name__=32		,     __magic_name__=10		,     __magic_name__=100		,     __magic_name__=1_026		,     __magic_name__=True		,     __magic_name__="data/tokenized_stories_train_wikitext103.jbl"		,     __magic_name__="igf_context_pairs.jbl"		,     )				->			Dict:




				"""simple docstring"""






				set_seed(3   )
				# generate train_data and objective_set
				snake_case   :					Optional[Any]    						=				generate_datasets(
				    _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     number=_SCREAMING_SNAKE_CASE		,     min_len=1_026		,     trim=_SCREAMING_SNAKE_CASE   )
				# keeps model same across runs
				set_seed(4   )
				# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
				# can we train on GPU?
				snake_case   :					Optional[Any]    						=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )

				# load pretrained model
				snake_case   :					Tuple    						=				load_gpta('''gpt2'''   ).to(_SCREAMING_SNAKE_CASE   )
				print('''computing perplexity on objective set'''   )
				snake_case   :					int    						=				compute_perplexity(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ).item()
				print('''perplexity on objective set:'''		,     _SCREAMING_SNAKE_CASE   )

				# collect igf pairs and save to file demo.jbl
				collect_objective_set(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

				# clean up, delete model and data we don't need anymore
				del model, train_data, objective_set
				torch.cuda.empty_cache()
def 							a_	(    __magic_name__		,     __magic_name__=15		,     __magic_name__=128		,     __magic_name__=100		,     __magic_name__="igf_model.pt"		,     )				->			str:




				"""simple docstring"""






				set_seed(42   )

				# Load pre-trained model
				snake_case   :					Any    						=				GPTaLMHeadModel.from_pretrained('''gpt2'''   )

				# Initialize secondary learner to use embedding weights of model
				snake_case   :					int    						=				SecondaryLearner(_SCREAMING_SNAKE_CASE   )

				# Train secondary learner
				snake_case   :					Union[str, Any]    						=				train_secondary_learner(
				    _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     max_epochs=_SCREAMING_SNAKE_CASE		,     batch_size=_SCREAMING_SNAKE_CASE		,     eval_freq=100		,     igf_model_path=_SCREAMING_SNAKE_CASE		,     )

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner
def 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__		,     __magic_name__=32		,     __magic_name__=1_000		,     __magic_name__=16		,     __magic_name__=1.0		,     __magic_name__=recopy_gpta		,     __magic_name__=None		,     __magic_name__=10		,     __magic_name__="gpt2_finetuned.pt"		,     )				->			Optional[int]:




				"""simple docstring"""






				snake_case   :					int    						=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )
				snake_case   :					List[str]    						=				RandomSampler(_SCREAMING_SNAKE_CASE   )
				snake_case   :					int    						=				DataLoader(_SCREAMING_SNAKE_CASE		,     sampler=_SCREAMING_SNAKE_CASE   )

				snake_case   :					List[str]    						=				max_steps // (len(_SCREAMING_SNAKE_CASE   )) + 1
				snake_case   :					List[Any]    						=				0
				snake_case   :					Union[str, Any]    						=				torch.zeros((1, context_len)		,     dtype=torch.long		,     device=_SCREAMING_SNAKE_CASE   )
				snake_case   :					List[Any]    						=				recopy_model(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

				model.train()
				if secondary_learner is not None:
								secondary_learner.to(_SCREAMING_SNAKE_CASE   )
								secondary_learner.eval()
				snake_case   :					Optional[Any]    						=				[]
				snake_case   :					Any    						=				0

				snake_case   :					Optional[Any]    						=				[]
				snake_case   :					List[str]    						=				[]

				# Compute the performance of the transformer model at the beginning
				snake_case   :					Optional[int]    						=				compute_perplexity(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
				test_perps.append(_SCREAMING_SNAKE_CASE   )
				print('''Test perplexity, step'''		,     _SCREAMING_SNAKE_CASE		,     ''':'''		,     _SCREAMING_SNAKE_CASE   )
				for epoch in range(int(_SCREAMING_SNAKE_CASE   )   ):
								for step, example in enumerate(_SCREAMING_SNAKE_CASE   ):
												torch.cuda.empty_cache()
												snake_case   :					Optional[int]    						=				random.randint(0		,     example.size(2   ) - context_len - 1   )
												snake_case   :					int    						=				example[0, 0, start : start + context_len]
												lm_optimizer.zero_grad()
												snake_case   :					Any    						=				model(_SCREAMING_SNAKE_CASE		,     labels=_SCREAMING_SNAKE_CASE   )
												snake_case   :					int    						=				True

												if secondary_learner is not None:
																snake_case   :					Optional[int]    						=				secondary_learner.forward(
																    torch.tensor(_SCREAMING_SNAKE_CASE		,     dtype=torch.long		,     device=_SCREAMING_SNAKE_CASE   ).unsqueeze(0   )   )[0].item()
																observed_qs.append(float(_SCREAMING_SNAKE_CASE   )   )

																# Here we implement the simple non-constant threshold for the predicted IG(X) value
																# We will decay the selectivity of our secondary learner filter from
																# 1 standard deviation above average to 1 below average after 10 batches.

																if global_step == 10:
																				snake_case   :					Dict    						=				-1
																if predicted_q < threshold:
																				snake_case   :					Optional[Any]    						=				False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()   )   )
																snake_case   :					Optional[int]    						=				outputs[0]
																lm_loss.backward()
																examples += 1

												del outputs

												# Once the batch is filled with enough contexts, backprop on the batch.
												if examples == batch_size:
																torch.cuda.empty_cache()
																snake_case   :					Any    						=				0
																# Do LM backprop
																torch.nn.utils.clip_grad_norm_(model.parameters()		,     3.0   )
																lm_optimizer.step()
																lm_scheduler.step()  # Update learning rate schedule
																global_step += 1
																# Compute the performance of the transformer model at this batch
																if global_step % eval_interval == 0:
																				snake_case   :					Optional[Any]    						=				compute_perplexity(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
																				test_perps.append(_SCREAMING_SNAKE_CASE   )

																				print('''Test perplexity, step'''		,     _SCREAMING_SNAKE_CASE		,     ''':'''		,     _SCREAMING_SNAKE_CASE   )
            # Break out of the loop after 60 batches
												if max_steps > 0 and global_step > 60:
																break
								if max_steps > 0 and global_step > 60:
												break

    # save finetuned transformer model
				torch.save(model.state_dict()		,     _SCREAMING_SNAKE_CASE   )
				torch.cuda.empty_cache()
				# Do some cleaning up so we can reinitialize for the next run of this function
				del lm_optimizer
				del lm_scheduler
				return model
def 							a_	(    )				->			Any:




				"""simple docstring"""






				snake_case   :					Optional[int]    						=				argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''   )

				# Required parameters
				parser.add_argument(
				    '''--data_dir'''		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     required=_SCREAMING_SNAKE_CASE		,     help='''The input data dir. Should contain data files for WikiText.'''		,     )
				parser.add_argument(
				    '''--model_name_or_path'''		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     required=_SCREAMING_SNAKE_CASE		,     help='''Path to pretrained model or model identifier from huggingface.co/models'''		,     )
				parser.add_argument(
				    '''--data_file'''		,     type=_SCREAMING_SNAKE_CASE		,     default=_SCREAMING_SNAKE_CASE		,     help=(
				        '''A jbl file containing tokenized data which can be split as objective dataset, '''
				        '''train_dataset and test_dataset.'''
				    )		,     )

				parser.add_argument(
				    '''--igf_data_file'''		,     type=_SCREAMING_SNAKE_CASE		,     default=_SCREAMING_SNAKE_CASE		,     help='''A jbl file containing the context and information gain pairs to train secondary learner.'''		,     )

				parser.add_argument(
				    '''--output_dir'''		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     required=_SCREAMING_SNAKE_CASE		,     help='''The output directory where the final fine-tuned model is stored.'''		,     )

				parser.add_argument(
				    '''--tokenizer_name'''		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     help='''Pretrained tokenizer name or path if not the same as model_name'''		,     )
				parser.add_argument('''--seed'''		,     type=_SCREAMING_SNAKE_CASE		,     default=_SCREAMING_SNAKE_CASE		,     help='''A seed for reproducible training.'''   )

				parser.add_argument(
				    '''--context_len'''		,     default=32		,     type=_SCREAMING_SNAKE_CASE		,     help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )		,     )

				parser.add_argument(
				    '''--size_objective_set'''		,     default=100		,     type=_SCREAMING_SNAKE_CASE		,     help='''number of articles that are long enough to be used as our objective set'''		,     )
				parser.add_argument(
				    '''--eval_freq'''		,     default=100		,     type=_SCREAMING_SNAKE_CASE		,     help='''secondary model evaluation is triggered at eval_freq'''   )

				parser.add_argument('''--max_steps'''		,     default=1_000		,     type=_SCREAMING_SNAKE_CASE		,     help='''To calculate training epochs'''   )

				parser.add_argument(
				    '''--secondary_learner_batch_size'''		,     default=128		,     type=_SCREAMING_SNAKE_CASE		,     help='''batch size of training data for secondary learner'''		,     )

				parser.add_argument(
				    '''--batch_size'''		,     default=16		,     type=_SCREAMING_SNAKE_CASE		,     help='''batch size of training data of language model(gpt2) '''   )

				parser.add_argument(
				    '''--eval_interval'''		,     default=10		,     type=_SCREAMING_SNAKE_CASE		,     help=(
				        '''decay the selectivity of our secondary learner filter from'''
				        '''1 standard deviation above average to 1 below average after 10 batches'''
				    )		,     )

				parser.add_argument(
				    '''--number'''		,     default=100		,     type=_SCREAMING_SNAKE_CASE		,     help='''The number of examples split to be used as objective_set/test_data'''   )

				parser.add_argument(
				    '''--min_len'''		,     default=1_026		,     type=_SCREAMING_SNAKE_CASE		,     help='''The minimum length of the article to be used as objective set'''   )

				parser.add_argument(
				    '''--secondary_learner_max_epochs'''		,     default=15		,     type=_SCREAMING_SNAKE_CASE		,     help='''number of epochs to train secondary learner'''   )

				parser.add_argument('''--trim'''		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     help='''truncate the example if it exceeds context length'''   )

				parser.add_argument(
				    '''--threshold'''		,     default=1.0		,     type=_SCREAMING_SNAKE_CASE		,     help=(
				        '''The threshold value used by secondary learner to filter the train_data and allow only'''
				        ''' informative data as input to the model'''
				    )		,     )

				parser.add_argument('''--finetuned_model_name'''		,     default='''gpt2_finetuned.pt'''		,     type=_SCREAMING_SNAKE_CASE		,     help='''finetuned_model_name'''   )

				parser.add_argument(
				    '''--recopy_model'''		,     default=_SCREAMING_SNAKE_CASE		,     type=_SCREAMING_SNAKE_CASE		,     help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''		,     )

				# function calls
				# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
				generate_n_pairs(
				    context_len=32		,     max_steps=10		,     size_objective_set=100		,     min_len=1_026		,     trim=_SCREAMING_SNAKE_CASE		,     data_file='''data/tokenized_stories_train_wikitext103.jbl'''		,     igf_data_file='''igf_context_pairs.jbl'''		,     )

				# Load train data for secondary learner
				snake_case   :					Any    						=				joblib.load('''data/IGF_values.jbl'''   )

				# Train secondary learner
				snake_case   :					Optional[Any]    						=				training_secondary_learner(
				    _SCREAMING_SNAKE_CASE		,     secondary_learner_max_epochs=15		,     secondary_learner_batch_size=128		,     eval_freq=100		,     igf_model_path='''igf_model.pt'''		,     )

				# load pretrained gpt2 model
				snake_case   :					Union[str, Any]    						=				GPTaLMHeadModel.from_pretrained('''gpt2'''   )
				set_seed(42   )

				# Generate train and test data to train and evaluate gpt2 model
				snake_case   :					Optional[int]    						=				generate_datasets(
				    context_len=32		,     file='''data/tokenized_stories_train_wikitext103.jbl'''		,     number=100		,     min_len=1_026		,     trim=_SCREAMING_SNAKE_CASE   )

				# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
				finetune(
				    _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     context_len=32		,     max_steps=1_000		,     batch_size=16		,     threshold=1.0		,     recopy_model=_SCREAMING_SNAKE_CASE		,     secondary_learner=_SCREAMING_SNAKE_CASE		,     eval_interval=10		,     finetuned_model_name='''gpt2_finetuned.pt'''		,     )


if __name__ == "__main__":
			main()

from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
	import torch

if is_tf_available():
	import tensorflow as tf

class        UpperCamelCase						(				__a				):
						a__		:str      =			['''image_processor''']
						a__		:str      =			'''SamImageProcessor'''





						def __init__(self			,     __UpperCamelCase						)				->				int:
												super().__init__(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		self.image_processor
												UpperCamelCase_		:							int  =		-10
												UpperCamelCase_		:							Dict  =		self.image_processor.size["""longest_edge"""]





						def __call__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				BatchEncoding:
												UpperCamelCase_		:							int  =		self.image_processor(
												    __UpperCamelCase			,     return_tensors=__UpperCamelCase			,     **__UpperCamelCase			,     )

												# pop arguments that are not used in the foward but used nevertheless
												UpperCamelCase_		:							Optional[int]  =		encoding_image_processor["""original_sizes"""]

												if hasattr(__UpperCamelCase			,     """numpy"""						):  # Checks if Torch or TF tensor
																		UpperCamelCase_		:							Dict  =		original_sizes.numpy()

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							str  =		self._check_and_preprocess_points(
												    input_points=__UpperCamelCase			,     input_labels=__UpperCamelCase			,     input_boxes=__UpperCamelCase			,     )

												UpperCamelCase_		:							List[str]  =		self._normalize_and_convert(
												    __UpperCamelCase			,     __UpperCamelCase			,     input_points=__UpperCamelCase			,     input_labels=__UpperCamelCase			,     input_boxes=__UpperCamelCase			,     return_tensors=__UpperCamelCase			,     )

												return encoding_image_processor





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase="pt"			,     )				->				Tuple:
												if input_points is not None:
																		if len(__UpperCamelCase						) != len(__UpperCamelCase						):
																								UpperCamelCase_		:							str  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     original_sizes[0]						) for point in input_points
																								]
																		else:
																								UpperCamelCase_		:							List[str]  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     __UpperCamelCase						)
																								    for point, original_size in zip(__UpperCamelCase			,     __UpperCamelCase						)
																								]
																		# check that all arrays have the same shape
																		if not all(point.shape == input_points[0].shape for point in input_points						):
																								if input_labels is not None:
																														UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		self._pad_points_and_labels(__UpperCamelCase			,     __UpperCamelCase						)

																		UpperCamelCase_		:							Dict  =		np.array(__UpperCamelCase						)

												if input_labels is not None:
																		UpperCamelCase_		:							Optional[Any]  =		np.array(__UpperCamelCase						)

												if input_boxes is not None:
																		if len(__UpperCamelCase						) != len(__UpperCamelCase						):
																								UpperCamelCase_		:							Tuple  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     original_sizes[0]			,     is_bounding_box=__UpperCamelCase						)
																								    for box in input_boxes
																								]
																		else:
																								UpperCamelCase_		:							Any  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     __UpperCamelCase			,     is_bounding_box=__UpperCamelCase						)
																								    for box, original_size in zip(__UpperCamelCase			,     __UpperCamelCase						)
																								]
																		UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)

												if input_boxes is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Union[str, Any]  =		torch.from_numpy(__UpperCamelCase						)
																								# boxes batch size of 1 by default
																								UpperCamelCase_		:							Any  =		input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							List[str]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# boxes batch size of 1 by default
																								UpperCamelCase_		:							Optional[int]  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_boxes.shape						) != 3 else input_boxes
																		encoding_image_processor.update({"""input_boxes""": input_boxes}						)
												if input_points is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Optional[Any]  =		torch.from_numpy(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							List[Any]  =		input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							Optional[Any]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Dict  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_points.shape						) != 4 else input_points
																		encoding_image_processor.update({"""input_points""": input_points}						)
												if input_labels is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Optional[int]  =		torch.from_numpy(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Tuple  =		input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							Optional[Any]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Dict  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_labels.shape						) != 3 else input_labels
																		encoding_image_processor.update({"""input_labels""": input_labels}						)

												return encoding_image_processor





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Tuple:
												UpperCamelCase_		:							Union[str, Any]  =		max([point.shape[0] for point in input_points]						)
												UpperCamelCase_		:							Any  =		[]
												for i, point in enumerate(__UpperCamelCase						):
																		if point.shape[0] != expected_nb_points:
																								UpperCamelCase_		:							Optional[int]  =		np.concatenate(
																								    [point, np.zeros((expected_nb_points - point.shape[0], 2)						) + self.point_pad_value]			,     axis=0						)
																								UpperCamelCase_		:							Dict  =		np.append(input_labels[i]			,     [self.point_pad_value]						)
																		processed_input_points.append(__UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		processed_input_points
												return input_points, input_labels





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=False						)				->				np.ndarray:
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		original_size
												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		self.image_processor._get_preprocess_shape(__UpperCamelCase			,     longest_edge=__UpperCamelCase						)
												UpperCamelCase_		:							str  =		deepcopy(__UpperCamelCase						).astype(__UpperCamelCase						)

												if is_bounding_box:
																		UpperCamelCase_		:							int  =		coords.reshape(-1			,     2			,     2						)

												UpperCamelCase_		:							Tuple  =		coords[..., 0] * (new_w / old_w)
												UpperCamelCase_		:							Optional[int]  =		coords[..., 1] * (new_h / old_h)

												if is_bounding_box:
																		UpperCamelCase_		:							Optional[Any]  =		coords.reshape(-1			,     4						)

												return coords





						def        A_							(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     )				->				List[str]:
												if input_points is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):  # Checks for TF or Torch tensor
																								UpperCamelCase_		:							int  =		input_points.numpy().tolist()

																		if not isinstance(__UpperCamelCase			,     __UpperCamelCase						) or not isinstance(input_points[0]			,     __UpperCamelCase						):
																								raise ValueError("""Input points must be a list of list of floating points."""						)
																		UpperCamelCase_		:							List[Any]  =		[np.array(__UpperCamelCase						) for input_point in input_points]
												else:
																		UpperCamelCase_		:							str  =		None

												if input_labels is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):
																								UpperCamelCase_		:							Any  =		input_labels.numpy().tolist()

																		if not isinstance(__UpperCamelCase			,     __UpperCamelCase						) or not isinstance(input_labels[0]			,     __UpperCamelCase						):
																								raise ValueError("""Input labels must be a list of list integers."""						)
																		UpperCamelCase_		:							List[str]  =		[np.array(__UpperCamelCase						) for label in input_labels]
												else:
																		UpperCamelCase_		:							List[str]  =		None

												if input_boxes is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):
																								UpperCamelCase_		:							Dict  =		input_boxes.numpy().tolist()

																		if (
																		    not isinstance(__UpperCamelCase			,     __UpperCamelCase						)
																		    or not isinstance(input_boxes[0]			,     __UpperCamelCase						)
																		    or not isinstance(input_boxes[0][0]			,     __UpperCamelCase						)
																		):
																								raise ValueError("""Input boxes must be a list of list of list of floating points."""						)
																		UpperCamelCase_		:							Tuple  =		[np.array(__UpperCamelCase						).astype(np.floataa						) for box in input_boxes]
												else:
																		UpperCamelCase_		:							List[str]  =		None

												return input_points, input_labels, input_boxes





						@property
						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							str  =		self.image_processor.model_input_names
												return list(dict.fromkeys(__UpperCamelCase						)						)





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				Optional[Any]:
												return self.image_processor.post_process_masks(*__UpperCamelCase			,     **__UpperCamelCase						)






