





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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Tuple=32   ,    _lowercase     : int=10   ,    _lowercase     : List[Any]=100   ,    _lowercase     : Tuple=1_026   ,    _lowercase     : List[str]=True   ,    _lowercase     : int="data/tokenized_stories_train_wikitext103.jbl"   ,    _lowercase     : List[Any]="igf_context_pairs.jbl"   ,    )   ->							List[Any]:




		'''simple docstring'''
		set_seed(3			)
		# generate train_data and objective_set
		lowercase__   ,				lowercase__					:      Optional[int]      =		generate_datasets(
		    _lowercase   ,    _lowercase   ,    number=_lowercase   ,    min_len=1_026   ,    trim=_lowercase			)
		# 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?
		lowercase__					:      Optional[int]      =		torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)

		# load pretrained model
		lowercase__					:      Union[str, Any]      =		load_gpta('gpt2'			).to(_lowercase			)
		print('computing perplexity on objective set'			)
		lowercase__					:      int      =		compute_perplexity(_lowercase   ,    _lowercase   ,    _lowercase			).item()
		print('perplexity on objective set:'   ,    _lowercase			)

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

		# clean up, delete model and data we don't need anymore
		del model, train_data, objective_set
		torch.cuda.empty_cache()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Any   ,    _lowercase     : Optional[Any]=15   ,    _lowercase     : Optional[int]=128   ,    _lowercase     : Optional[Any]=100   ,    _lowercase     : int="igf_model.pt"   ,    )   ->							Union[str, Any]:




		'''simple docstring'''
		set_seed(42			)

		# Load pre-trained model
		lowercase__					:      Optional[Any]      =		GPTaLMHeadModel.from_pretrained('gpt2'			)

		# Initialize secondary learner to use embedding weights of model
		lowercase__					:      Any      =		SecondaryLearner(_lowercase			)

		# Train secondary learner
		lowercase__					:      List[Any]      =		train_secondary_learner(
		    _lowercase   ,    _lowercase   ,    max_epochs=_lowercase   ,    batch_size=_lowercase   ,    eval_freq=100   ,    igf_model_path=_lowercase   ,    )

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[str]   ,    _lowercase     : Union[str, Any]   ,    _lowercase     : Dict   ,    _lowercase     : Tuple=32   ,    _lowercase     : int=1_000   ,    _lowercase     : str=16   ,    _lowercase     : Union[str, Any]=1.0   ,    _lowercase     : Dict=recopy_gpta   ,    _lowercase     : Any=None   ,    _lowercase     : Any=10   ,    _lowercase     : List[str]="gpt2_finetuned.pt"   ,    )   ->							List[str]:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)
		lowercase__					:      int      =		RandomSampler(_lowercase			)
		lowercase__					:      Optional[Any]      =		DataLoader(_lowercase   ,    sampler=_lowercase			)

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

		model.train()
		if secondary_learner is not None:
				secondary_learner.to(_lowercase			)
				secondary_learner.eval()
		lowercase__					:      List[str]      =		[]
		lowercase__					:      Union[str, Any]      =		0

		lowercase__					:      Tuple      =		[]
		lowercase__					:      Optional[Any]      =		[]

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

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

								# 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:
										lowercase__					:      List[Any]      =		-1
								if predicted_q < threshold:
										lowercase__					:      Optional[Any]      =		False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()			)			)
								lowercase__					:      str      =		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()
								lowercase__					:      Tuple      =		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:
										lowercase__					:      Tuple      =		compute_perplexity(_lowercase   ,    _lowercase   ,    _lowercase			)
										test_perps.append(_lowercase			)

										print('Test perplexity, step'   ,    _lowercase   ,    ':'   ,    _lowercase			)
            # 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()   ,    _lowercase			)
		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       SCREAMING_SNAKE_CASE__  (       )   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      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=_lowercase   ,    type=_lowercase   ,    required=_lowercase   ,    help='The input data dir. Should contain data files for WikiText.'   ,    )
		parser.add_argument(
		    '--model_name_or_path'   ,    default=_lowercase   ,    type=_lowercase   ,    required=_lowercase   ,    help='Path to pretrained model or model identifier from huggingface.co/models'   ,    )
		parser.add_argument(
		    '--data_file'   ,    type=_lowercase   ,    default=_lowercase   ,    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=_lowercase   ,    default=_lowercase   ,    help='A jbl file containing the context and information gain pairs to train secondary learner.'   ,    )

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

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

		parser.add_argument(
		    '--context_len'   ,    default=32   ,    type=_lowercase   ,    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=_lowercase   ,    help='number of articles that are long enough to be used as our objective set'   ,    )
		parser.add_argument(
		    '--eval_freq'   ,    default=100   ,    type=_lowercase   ,    help='secondary model evaluation is triggered at eval_freq'			)

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

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

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

		parser.add_argument(
		    '--eval_interval'   ,    default=10   ,    type=_lowercase   ,    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=_lowercase   ,    help='The number of examples split to be used as objective_set/test_data'			)

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

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

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

		parser.add_argument(
		    '--threshold'   ,    default=1.0   ,    type=_lowercase   ,    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=_lowercase   ,    help='finetuned_model_name'			)

		parser.add_argument(
		    '--recopy_model'   ,    default=_lowercase   ,    type=_lowercase   ,    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=_lowercase   ,    data_file='data/tokenized_stories_train_wikitext103.jbl'   ,    igf_data_file='igf_context_pairs.jbl'   ,    )

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

		# Train secondary learner
		lowercase__					:      int      =		training_secondary_learner(
		    _lowercase   ,    secondary_learner_max_epochs=15   ,    secondary_learner_batch_size=128   ,    eval_freq=100   ,    igf_model_path='igf_model.pt'   ,    )

		# load pretrained gpt2 model
		lowercase__					:      str      =		GPTaLMHeadModel.from_pretrained('gpt2'			)
		set_seed(42			)

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

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


if __name__ == "__main__":
			main()









import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import YolosImageProcessor


class       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	def __init__(						self:						Optional[int],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Tuple=7,					lowerCamelCase_:						Dict=3,					lowerCamelCase_:						Optional[Any]=30,					lowerCamelCase_:						str=400,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						Optional[Any]=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=[0.5, 0.5, 0.5],					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						List[str]=1 / 255,					lowerCamelCase_:						Dict=True,					):
			# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
			lowercase__					:      str      =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
			lowercase__					:      Optional[int]      =		parent
			lowercase__					:      List[Any]      =		batch_size
			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      Optional[Any]      =		min_resolution
			lowercase__					:      Union[str, Any]      =		max_resolution
			lowercase__					:      Tuple      =		do_resize
			lowercase__					:      str      =		size
			lowercase__					:      Dict      =		do_normalize
			lowercase__					:      Optional[int]      =		image_mean
			lowercase__					:      Dict      =		image_std
			lowercase__					:      int      =		do_rescale
			lowercase__					:      Any      =		rescale_factor
			lowercase__					:      Union[str, Any]      =		do_pad

	def    snake_case__(						self:						str			):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_rescale": self.do_rescale,
			    "rescale_factor": self.rescale_factor,
			    "do_pad": self.do_pad,
			}





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						int=False			):
			if not batched:
					lowercase__					:      Any      =		image_inputs[0]
					if isinstance(lowerCamelCase_,					Image.Image			):
							lowercase__   ,				lowercase__					:      List[str]      =		image.size
					else:
							lowercase__   ,				lowercase__					:      str      =		image.shape[1], image.shape[2]
					if w < h:
							lowercase__					:      Optional[Any]      =		int(self.size['shortest_edge'] * h / w			)
							lowercase__					:      Optional[int]      =		self.size['shortest_edge']
					elif w > h:
							lowercase__					:      List[str]      =		self.size['shortest_edge']
							lowercase__					:      Any      =		int(self.size['shortest_edge'] * w / h			)
					else:
							lowercase__					:      List[str]      =		self.size['shortest_edge']
							lowercase__					:      List[Any]      =		self.size['shortest_edge']

			else:
					lowercase__					:      int      =		[]
					for image in image_inputs:
							lowercase__   ,				lowercase__					:      Dict      =		self.get_expected_values([image]			)
							expected_values.append((expected_height, expected_width)			)
					lowercase__					:      int      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[0]			)[0]
					lowercase__					:      Tuple      =		max(lowerCamelCase_,					key=lambda lowerCamelCase_			: item[1]			)[1]

			return expected_height, expected_width


@require_torch
@require_vision
class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				YolosImageProcessor if is_vision_available() else None

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[int]      =		YolosImageProcessingTester(self			)

	@property
	def    snake_case__(						self:						Union[str, Any]			):
			return self.image_processor_tester.prepare_image_processor_dict()

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Optional[Any]      =		self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_mean'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'image_std'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_normalize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'do_resize'			)			)
			self.assertTrue(hasattr(lowerCamelCase_,					'size'			)			)

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Tuple      =		self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 18, 'longest_edge': 1333}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		self.image_processing_class.from_dict(
			    self.image_processor_dict,					size=42,					max_size=84,					pad_and_return_pixel_mask=lowerCamelCase_			)
			self.assertEqual(image_processor.size,					{'shortest_edge': 42, 'longest_edge': 84}			)
			self.assertEqual(image_processor.do_pad,					lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						Optional[Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			lowercase__					:      Union[str, Any]      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					Image.Image			)

			# Test not batched input
			lowercase__					:      Tuple      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      List[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__   ,				lowercase__					:      Optional[Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			lowercase__					:      Dict      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values
			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						str			):
			# Initialize image_processing
			lowercase__					:      str      =		self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			lowercase__					:      int      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					numpify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					np.ndarray			)

			# Test not batched input
			lowercase__					:      List[Any]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__					:      Any      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Optional[int]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						Union[str, Any]			):
			# Initialize image_processing
			lowercase__					:      Optional[int]      =		self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			lowercase__					:      Dict      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

			# Test not batched input
			lowercase__					:      List[str]      =		image_processing(image_inputs[0],					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(1, self.image_processor_tester.num_channels, expected_height, expected_width),					)

			# Test batched
			lowercase__					:      List[Any]      =		image_processing(lowerCamelCase_,					return_tensors='pt'			).pixel_values

			lowercase__   ,				lowercase__					:      Union[str, Any]      =		self.image_processor_tester.get_expected_values(lowerCamelCase_,					batched=lowerCamelCase_			)

			self.assertEqual(
			    encoded_images.shape,					(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        expected_height,
			        expected_width,
			    ),					)

	def    snake_case__(						self:						int			):
			# Initialize image_processings
			lowercase__					:      Tuple      =		self.image_processing_class(**self.image_processor_dict			)
			lowercase__					:      List[str]      =		self.image_processing_class(do_resize=lowerCamelCase_,					do_normalize=lowerCamelCase_,					do_rescale=lowerCamelCase_			)
			# create random PyTorch tensors
			lowercase__					:      Tuple      =		prepare_image_inputs(self.image_processor_tester,					equal_resolution=lowerCamelCase_,					torchify=lowerCamelCase_			)
			for image in image_inputs:
					self.assertIsInstance(lowerCamelCase_,					torch.Tensor			)

			# Test whether the method "pad" and calling the image processor return the same tensors
			lowercase__					:      Optional[int]      =		image_processing_a.pad(lowerCamelCase_,					return_tensors='pt'			)
			lowercase__					:      int      =		image_processing_a(lowerCamelCase_,					return_tensors='pt'			)

			self.assertTrue(
			    torch.allclose(encoded_images_with_method['pixel_values'],					encoded_images['pixel_values'],					atol=1E-4			)			)

	@slow
	def    snake_case__(						self:						List[str]			):
			# prepare image and target
			lowercase__					:      Any      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',					'r'			) as f:
					lowercase__					:      List[str]      =		json.loads(f.read()			)

			lowercase__					:      List[str]      =		{'image_id': 39769, 'annotations': target}

			# encode them
			lowercase__					:      Any      =		YolosImageProcessor.from_pretrained('hustvl/yolos-small'			)
			lowercase__					:      Optional[Any]      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      Optional[Any]      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      int      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      int      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      Tuple      =		torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Tuple      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      Union[str, Any]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      Tuple      =		torch.tensor([75, 75, 63, 65, 17, 17]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      List[Any]      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)





	@slow
	def    snake_case__(						self:						Dict			):
			# prepare image, target and masks_path
			lowercase__					:      Optional[int]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt',					'r'			) as f:
					lowercase__					:      Optional[Any]      =		json.loads(f.read()			)

			lowercase__					:      List[Any]      =		{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

			lowercase__					:      Optional[int]      =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'			)

			# encode them
			lowercase__					:      List[Any]      =		YolosImageProcessor(format='coco_panoptic'			)
			lowercase__					:      Dict      =		image_processing(images=lowerCamelCase_,					annotations=lowerCamelCase_,					masks_path=lowerCamelCase_,					return_tensors='pt'			)

			# verify pixel values
			lowercase__					:      str      =		torch.Size([1, 3, 800, 1066]			)
			self.assertEqual(encoding['pixel_values'].shape,					lowerCamelCase_			)

			lowercase__					:      List[str]      =		torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]			)
			self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],					lowerCamelCase_,					atol=1E-4			)			)

			# verify area
			lowercase__					:      Optional[int]      =		torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['area'],					lowerCamelCase_			)			)
			# verify boxes
			lowercase__					:      Dict      =		torch.Size([6, 4]			)
			self.assertEqual(encoding['labels'][0]['boxes'].shape,					lowerCamelCase_			)
			lowercase__					:      List[str]      =		torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],					lowerCamelCase_,					atol=1E-3			)			)
			# verify image_id
			lowercase__					:      Any      =		torch.tensor([39769]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],					lowerCamelCase_			)			)
			# verify is_crowd
			lowercase__					:      List[str]      =		torch.tensor([0, 0, 0, 0, 0, 0]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],					lowerCamelCase_			)			)
			# verify class_labels
			lowercase__					:      List[str]      =		torch.tensor([17, 17, 63, 75, 75, 93]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],					lowerCamelCase_			)			)
			# verify masks
			lowercase__					:      Optional[Any]      =		822873
			self.assertEqual(encoding['labels'][0]['masks'].sum().item(),					lowerCamelCase_			)
			# verify orig_size
			lowercase__					:      Optional[int]      =		torch.tensor([480, 640]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],					lowerCamelCase_			)			)
			# verify size
			lowercase__					:      Dict      =		torch.tensor([800, 1066]			)
			self.assertTrue(torch.allclose(encoding['labels'][0]['size'],					lowerCamelCase_			)			)


