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 lowerCamelCase  (   SCREAMING_SNAKE_CASE=32  ,			SCREAMING_SNAKE_CASE=10  ,			SCREAMING_SNAKE_CASE=100  ,			SCREAMING_SNAKE_CASE=1_026  ,			SCREAMING_SNAKE_CASE=True  ,			SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"  ,			SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"  ,			):
    '''simple docstring'''







    set_seed(3	)
    # generate train_data and objective_set
    __UpperCamelCase  ,       __UpperCamelCase     :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?
    __UpperCamelCase     :List[Any]			  =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)

    # load pretrained model
    __UpperCamelCase     :str			  =		load_gpta('''gpt2'''	).to(SCREAMING_SNAKE_CASE	)
    print('''computing perplexity on objective set'''	)
    __UpperCamelCase     :List[str]			  =		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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=15  ,			SCREAMING_SNAKE_CASE=128  ,			SCREAMING_SNAKE_CASE=100  ,			SCREAMING_SNAKE_CASE="igf_model.pt"  ,			):
    '''simple docstring'''







    set_seed(42	)

    # Load pre-trained model
    __UpperCamelCase     :str			  =		GPTaLMHeadModel.from_pretrained('''gpt2'''	)

    # Initialize secondary learner to use embedding weights of model
    __UpperCamelCase     :List[str]			  =		SecondaryLearner(SCREAMING_SNAKE_CASE	)

    # Train secondary learner
    __UpperCamelCase     :Tuple			  =		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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=32  ,			SCREAMING_SNAKE_CASE=1_000  ,			SCREAMING_SNAKE_CASE=16  ,			SCREAMING_SNAKE_CASE=1.0  ,			SCREAMING_SNAKE_CASE=recopy_gpta  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=10  ,			SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)
    __UpperCamelCase     :Tuple			  =		RandomSampler(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Union[str, Any]			  =		DataLoader(SCREAMING_SNAKE_CASE  ,			sampler=SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :List[Any]			  =		max_steps // (len(SCREAMING_SNAKE_CASE	)) + 1
    __UpperCamelCase     :Optional[int]			  =		0
    __UpperCamelCase     :int			  =		torch.zeros((1, context_len)  ,			dtype=torch.long  ,			device=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		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()
    __UpperCamelCase     :List[str]			  =		[]
    __UpperCamelCase     :str			  =		0

    __UpperCamelCase     :int			  =		[]
    __UpperCamelCase     :int			  =		[]

    # Compute the performance of the transformer model at the beginning
    __UpperCamelCase     :List[str]			  =		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()
            __UpperCamelCase     :Optional[Any]			  =		random.randint(0  ,			example.size(2	) - context_len - 1	)
            __UpperCamelCase     :Tuple			  =		example[0, 0, start : start + context_len]
            lm_optimizer.zero_grad()
            __UpperCamelCase     :List[str]			  =		model(SCREAMING_SNAKE_CASE  ,			labels=SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Any			  =		True

            if secondary_learner is not None:
                __UpperCamelCase     :List[Any]			  =		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:
                    __UpperCamelCase     :List[Any]			  =		-1
                if predicted_q < threshold:
                    __UpperCamelCase     :List[str]			  =		False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()	)	)
                __UpperCamelCase     :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()
                __UpperCamelCase     :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:
                    __UpperCamelCase     :Tuple			  =		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 lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		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
    __UpperCamelCase     :Optional[Any]			  =		joblib.load('''data/IGF_values.jbl'''	)

    # Train secondary learner
    __UpperCamelCase     :str			  =		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
    __UpperCamelCase     :Union[str, Any]			  =		GPTaLMHeadModel.from_pretrained('''gpt2'''	)
    set_seed(42	)

    # Generate train and test data to train and evaluate gpt2 model
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		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()












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 						_A(					unittest.TestCase   ):


				"""simple docstring"""


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



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

									else:
														__A	:						int       =				[]
														for image in image_inputs:
																			__A    ,				__A	:						Optional[Any]       =				self.get_expected_values([image]     )
																			expected_values.append((expected_height, expected_width)     )
														__A	:						List[str]       =				max(_A     ,					key=lambda _A     : item[0]     )[0]
														__A	:						str       =				max(_A     ,					key=lambda _A     : item[1]     )[1]

									return expected_height, expected_width





@require_torch
@require_vision
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  List[str]					    =	YolosImageProcessor if is_vision_available() else None



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				YolosImageProcessingTester(self     )



				@property
				def 					UpperCAmelCase_		(			self     ):
									return self.image_processor_tester.prepare_image_processor_dict()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				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 					UpperCAmelCase_		(			self     ):
									__A	:						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     ,					_A     )

									__A	:						Dict       =				self.image_processing_class.from_dict(
									    self.image_processor_dict     ,					size=42     ,					max_size=84     ,					pad_and_return_pixel_mask=_A     )
									self.assertEqual(image_processor.size     ,					{'shortest_edge': 42, 'longest_edge': 84}     )
									self.assertEqual(image_processor.do_pad     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									pass



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

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

									__A    ,				__A	:						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
									__A    ,				__A	:						Optional[Any]       =				self.image_processor_tester.get_expected_values(_A     ,					batched=_A     )

									__A	:						str       =				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 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Optional[Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									__A	:						List[Any]       =				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
									__A	:						str       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						List[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
									__A	:						Tuple       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[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,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processing
									__A	:						Union[str, Any]       =				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									__A	:						Dict       =				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
									__A	:						Union[str, Any]       =				image_processing(image_inputs[0]     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						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
									__A	:						Optional[int]       =				image_processing(_A     ,					return_tensors='pt'     ).pixel_values

									__A    ,				__A	:						Optional[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,
									    )     ,					)



				def 					UpperCAmelCase_		(			self     ):
									# Initialize image_processings
									__A	:						Tuple       =				self.image_processing_class(**self.image_processor_dict     )
									__A	:						Any       =				self.image_processing_class(do_resize=_A     ,					do_normalize=_A     ,					do_rescale=_A     )
									# create random PyTorch tensors
									__A	:						Union[str, Any]       =				prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=_A     ,					torchify=_A     )
									for image in image_inputs:
														self.assertIsInstance(_A     ,					torch.Tensor     )

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

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



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

									__A	:						Optional[Any]       =				{'image_id': 39769, 'annotations': target}

									# encode them
									__A	:						str       =				YolosImageProcessor.from_pretrained('hustvl/yolos-small'     )
									__A	:						List[Any]       =				image_processing(images=_A     ,					annotations=_A     ,					return_tensors='pt'     )

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

									__A	:						Union[str, Any]       =				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
									__A	:						List[Any]       =				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
									__A	:						Any       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						Optional[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
									__A	:						Optional[int]       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						str       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						Any       =				torch.tensor([75, 75, 63, 65, 17, 17]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify orig_size
									__A	:						int       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						str       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									# prepare image, target and masks_path
									__A	:						Dict       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,					'r'     ) as f:
														__A	:						Tuple       =				json.loads(f.read()     )

									__A	:						Any       =				{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

									__A	:						List[Any]       =				pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

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

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

									__A	:						Union[str, Any]       =				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
									__A	:						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']     ,					_A     )     )
									# verify boxes
									__A	:						Optional[int]       =				torch.Size([6, 4]     )
									self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					_A     )
									__A	:						Optional[Any]       =				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
									__A	:						Union[str, Any]       =				torch.tensor([39769]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					_A     )     )
									# verify is_crowd
									__A	:						Tuple       =				torch.tensor([0, 0, 0, 0, 0, 0]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					_A     )     )
									# verify class_labels
									__A	:						List[str]       =				torch.tensor([17, 17, 63, 75, 75, 93]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					_A     )     )
									# verify masks
									__A	:						Tuple       =				822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,					_A     )
									# verify orig_size
									__A	:						str       =				torch.tensor([480, 640]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					_A     )     )
									# verify size
									__A	:						int       =				torch.tensor([800, 1066]     )
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					_A     )     )




