






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       (				snake_case_      :							Any=32  ,						snake_case_      :							Optional[int]=10  ,						snake_case_      :							Optional[int]=100  ,						snake_case_      :							Optional[int]=1026  ,						snake_case_      :							Optional[int]=True  ,						snake_case_      :							Tuple="data/tokenized_stories_train_wikitext103.jbl"  ,						snake_case_      :							List[str]="igf_context_pairs.jbl"  ,						):
 set_seed(3     )
 # generate train_data and objective_set
 snake_case__,		snake_case__				:   Optional[int]     =						generate_datasets(
     _A  ,						_A  ,						number=_A  ,						min_len=1026  ,						trim=_A     )
 # 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[int]     =						torch.device("cuda:0" if torch.cuda.is_available() else "cpu"     )

 # load pretrained model
 snake_case__				:   str     =						load_gpta("gpt2"     ).to(_A     )
 print("computing perplexity on objective set"     )
 snake_case__				:   List[str]     =						compute_perplexity(_A  ,						_A  ,						_A     ).item()
 print("perplexity on objective set:"  ,						_A     )

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

 # 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       (				snake_case_      :							List[Any]  ,						snake_case_      :							Tuple=15  ,						snake_case_      :							Tuple=128  ,						snake_case_      :							Tuple=100  ,						snake_case_      :							Optional[Any]="igf_model.pt"  ,						):
 set_seed(42     )

 # Load pre-trained model
 snake_case__				:   List[str]     =						GPTaLMHeadModel.from_pretrained("gpt2"     )

 # Initialize secondary learner to use embedding weights of model
 snake_case__				:   str     =						SecondaryLearner(_A     )

 # Train secondary learner
 snake_case__				:   int     =						train_secondary_learner(
     _A  ,						_A  ,						max_epochs=_A  ,						batch_size=_A  ,						eval_freq=100  ,						igf_model_path=_A  ,						)

 del model, secondary_learner_train_data
 torch.cuda.empty_cache()

 return secondary_learner







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]  ,						snake_case_      :							Tuple  ,						snake_case_      :							Tuple  ,						snake_case_      :							str=32  ,						snake_case_      :							Optional[int]=1000  ,						snake_case_      :							Tuple=16  ,						snake_case_      :							Tuple=1.0  ,						snake_case_      :							Dict=recopy_gpta  ,						snake_case_      :							str=None  ,						snake_case_      :							Tuple=10  ,						snake_case_      :							str="gpt2_finetuned.pt"  ,						):
 snake_case__				:   Tuple     =						torch.device("cuda:0" if torch.cuda.is_available() else "cpu"     )
 snake_case__				:   Optional[int]     =						RandomSampler(_A     )
 snake_case__				:   Optional[Any]     =						DataLoader(_A  ,						sampler=_A     )

 snake_case__				:   Union[str, Any]     =						max_steps // (len(_A     )) + 1
 snake_case__				:   str     =						0
 snake_case__				:   Optional[Any]     =						torch.zeros((1, context_len)  ,						dtype=torch.long  ,						device=_A     )
 snake_case__,		snake_case__,		snake_case__				:   Tuple     =						recopy_model(_A  ,						_A  ,						_A     )

 model.train()
 if secondary_learner is not None:
  secondary_learner.to(_A     )
  secondary_learner.eval()
 snake_case__				:   Any     =						[]
 snake_case__				:   List[str]     =						0

 snake_case__				:   Tuple     =						[]
 snake_case__				:   List[str]     =						[]

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

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

    # 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__				:   Optional[int]     =						-1
    if predicted_q < threshold:
     snake_case__				:   List[Any]     =						False

            # If we passed the filter, add the context to the batch!
   if do_backprop:
    contexts.append(np.array(context.cpu()     )     )
    snake_case__				:   List[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()
    snake_case__				:   Union[str, 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__				:   int     =						compute_perplexity(_A  ,						_A  ,						_A     )
     test_perps.append(_A     )

     print("Test perplexity, step"  ,						_A  ,						":"  ,						_A     )
            # 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()  ,						_A     )
 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       (				):
 snake_case__				:   Union[str, Any]     =						argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"     )

 # Required parameters
 parser.add_argument(
     "--data_dir"  ,						default=_A  ,						type=_A  ,						required=_A  ,						help="The input data dir. Should contain data files for WikiText."  ,						)
 parser.add_argument(
     "--model_name_or_path"  ,						default=_A  ,						type=_A  ,						required=_A  ,						help="Path to pretrained model or model identifier from huggingface.co/models"  ,						)
 parser.add_argument(
     "--data_file"  ,						type=_A  ,						default=_A  ,						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=_A  ,						default=_A  ,						help="A jbl file containing the context and information gain pairs to train secondary learner."  ,						)

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

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

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

 parser.add_argument("--max_steps"  ,						default=1000  ,						type=_A  ,						help="To calculate training epochs"     )

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

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

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

 parser.add_argument(
     "--min_len"  ,						default=1026  ,						type=_A  ,						help="The minimum length of the article to be used as objective set"     )

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

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

 parser.add_argument(
     "--threshold"  ,						default=1.0  ,						type=_A  ,						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=_A  ,						help="finetuned_model_name"     )

 parser.add_argument(
     "--recopy_model"  ,						default=_A  ,						type=_A  ,						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=1026  ,						trim=_A  ,						data_file="data/tokenized_stories_train_wikitext103.jbl"  ,						igf_data_file="igf_context_pairs.jbl"  ,						)

 # Load train data for secondary learner
 snake_case__				:   Optional[int]     =						joblib.load("data/IGF_values.jbl"     )

 # Train secondary learner
 snake_case__				:   Union[str, Any]     =						training_secondary_learner(
     _A  ,						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__,		snake_case__				:   int     =						generate_datasets(
     context_len=32  ,						file="data/tokenized_stories_train_wikitext103.jbl"  ,						number=100  ,						min_len=1026  ,						trim=_A     )

 # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
 finetune(
     _A  ,						_A  ,						_A  ,						context_len=32  ,						max_steps=1000  ,						batch_size=16  ,						threshold=1.0  ,						recopy_model=_A  ,						secondary_learner=_A  ,						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 ConditionalDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						List[Any]							,	__A  :						Dict							,	__A  :						int=7							,	__A  :						Optional[Any]=3							,	__A  :						List[str]=3_0							,	__A  :						List[Any]=4_0_0							,	__A  :						Union[str, Any]=True							,	__A  :						List[Any]=None							,	__A  :						Optional[Any]=True							,	__A  :						Tuple=[0.5, 0.5, 0.5]							,	__A  :						Union[str, Any]=[0.5, 0.5, 0.5]							,	__A  :						List[str]=True							,	__A  :						Any=1 / 2_5_5							,	__A  :						Optional[int]=True							,	):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__				:   List[str]     =						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__				:   Dict     =						parent
						snake_case__				:   Optional[int]     =						batch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   str     =						min_resolution
						snake_case__				:   Tuple     =						max_resolution
						snake_case__				:   List[Any]     =						do_resize
						snake_case__				:   Dict     =						size
						snake_case__				:   List[str]     =						do_normalize
						snake_case__				:   Optional[int]     =						image_mean
						snake_case__				:   Optional[int]     =						image_std
						snake_case__				:   Any     =						do_rescale
						snake_case__				:   Optional[int]     =						rescale_factor
						snake_case__				:   int     =						do_pad



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

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

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	ConditionalDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						Any ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(__A							,	"image_mean" ) )
						self.assertTrue(hasattr(__A							,	"image_std" ) )
						self.assertTrue(hasattr(__A							,	"do_normalize" ) )
						self.assertTrue(hasattr(__A							,	"do_resize" ) )
						self.assertTrue(hasattr(__A							,	"size" ) )



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[Any]     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
						self.assertEqual(image_processor.do_pad							,	__A )

						snake_case__				:   Any     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   Dict     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	Image.Image )

						# Test not batched input
						snake_case__				:   Union[str, Any]     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Union[str, Any]     =						self.image_processor_tester.get_expected_values(__A )

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

						# Test batched
						snake_case__,		snake_case__				:   Tuple     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						snake_case__				:   int     =						image_processing(__A							,	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  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   List[str]     =						self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						snake_case__				:   List[str]     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	np.ndarray )

						# Test not batched input
						snake_case__				:   int     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Dict     =						self.image_processor_tester.get_expected_values(__A )

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

						# Test batched
						snake_case__				:   Optional[Any]     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   str     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

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



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

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

						snake_case__,		snake_case__				:   Optional[int]     =						self.image_processor_tester.get_expected_values(__A )

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

						# Test batched
						snake_case__				:   Dict     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

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



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

						snake_case__				:   Optional[Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
						snake_case__				:   int     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   str     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Tuple     =						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]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   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"]							,	__A ) )
						# verify boxes
						snake_case__				:   Tuple     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						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]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   List[Any]     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[int]     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   Dict     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[str]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






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

						snake_case__				:   Optional[int]     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__				:   Optional[Any]     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   Optional[int]     =						ConditionalDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   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]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						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"]							,	__A ) )
						# verify boxes
						snake_case__				:   Dict     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   int     =						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]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[Any]     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   str     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )



