






'''simple docstring'''







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_ (UpperCamelCase=32       ,    UpperCamelCase=10       ,    UpperCamelCase=100       ,    UpperCamelCase=1026       ,    UpperCamelCase=True       ,    UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"       ,    UpperCamelCase="igf_context_pairs.jbl"       ,    )   -> Optional[Any]:
							set_seed(3   )
							# generate train_data and objective_set
							lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= generate_datasets(
							    UpperCamelCase       ,    UpperCamelCase       ,    number=UpperCamelCase       ,    min_len=1026       ,    trim=UpperCamelCase   )
							# 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?
							lowerCamelCase__      :			Dict 						= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )

							# load pretrained model
							lowerCamelCase__      :			str 						= load_gpta("""gpt2"""   ).to(UpperCamelCase   )
							print("""computing perplexity on objective set"""   )
							lowerCamelCase__      :			Optional[Any] 						= compute_perplexity(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ).item()
							print("""perplexity on objective set:"""       ,    UpperCamelCase   )

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

							# 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_ (UpperCamelCase       ,    UpperCamelCase=15       ,    UpperCamelCase=128       ,    UpperCamelCase=100       ,    UpperCamelCase="igf_model.pt"       ,    )   -> int:
							set_seed(42   )

							# Load pre-trained model
							lowerCamelCase__      :			int 						= GPTaLMHeadModel.from_pretrained("""gpt2"""   )

							# Initialize secondary learner to use embedding weights of model
							lowerCamelCase__      :			Union[str, Any] 						= SecondaryLearner(UpperCamelCase   )

							# Train secondary learner
							lowerCamelCase__      :			Tuple 						= train_secondary_learner(
							    UpperCamelCase       ,    UpperCamelCase       ,    max_epochs=UpperCamelCase       ,    batch_size=UpperCamelCase       ,    eval_freq=100       ,    igf_model_path=UpperCamelCase       ,    )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=32       ,    UpperCamelCase=1000       ,    UpperCamelCase=16       ,    UpperCamelCase=1.0       ,    UpperCamelCase=recopy_gpta       ,    UpperCamelCase=None       ,    UpperCamelCase=10       ,    UpperCamelCase="gpt2_finetuned.pt"       ,    )   -> List[str]:
							lowerCamelCase__      :			Optional[Any] 						= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
							lowerCamelCase__      :			List[str] 						= RandomSampler(UpperCamelCase   )
							lowerCamelCase__      :			str 						= DataLoader(UpperCamelCase       ,    sampler=UpperCamelCase   )

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

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(UpperCamelCase   )
														secondary_learner.eval()
							lowerCamelCase__      :			Union[str, Any] 						= []
							lowerCamelCase__      :			Optional[Any] 						= 0

							lowerCamelCase__      :			Union[str, Any] 						= []
							lowerCamelCase__      :			Any 						= []

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

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

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

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()   )   )
																												lowerCamelCase__      :			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()
																												lowerCamelCase__      :			Optional[int] 						= 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:
																																			lowerCamelCase__      :			Tuple 						= compute_perplexity(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
																																			test_perps.append(UpperCamelCase   )

																																			print("""Test perplexity, step"""       ,    UpperCamelCase       ,    """:"""       ,    UpperCamelCase   )
            # 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()       ,    UpperCamelCase   )
							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_ ()   -> Tuple:
							lowerCamelCase__      :			List[Any] 						= argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""   )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

							# Train secondary learner
							lowerCamelCase__      :			Optional[int] 						= training_secondary_learner(
							    UpperCamelCase       ,    secondary_learner_max_epochs=15       ,    secondary_learner_batch_size=128       ,    eval_freq=100       ,    igf_model_path="""igf_model.pt"""       ,    )

							# load pretrained gpt2 model
							lowerCamelCase__      :			int 						= GPTaLMHeadModel.from_pretrained("""gpt2"""   )
							set_seed(42   )

							# Generate train and test data to train and evaluate gpt2 model
							lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= generate_datasets(
							    context_len=32       ,    file="""data/tokenized_stories_train_wikitext103.jbl"""       ,    number=100       ,    min_len=1026       ,    trim=UpperCamelCase   )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    context_len=32       ,    max_steps=1000       ,    batch_size=16       ,    threshold=1.0       ,    recopy_model=UpperCamelCase       ,    secondary_learner=UpperCamelCase       ,    eval_interval=10       ,    finetuned_model_name="""gpt2_finetuned.pt"""       ,    )


if __name__ == "__main__":
			main()









'''simple docstring'''







import inspect
import math
import tempfile
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import ViTMAEForPreTraining, ViTMAEModel
			from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class _lowercase			:




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]=13    ,							UpperCamelCase__:       Optional[int]=30    ,							UpperCamelCase__:       Optional[int]=2    ,							UpperCamelCase__:       List[str]=3    ,							UpperCamelCase__:       List[str]=True    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       List[Any]=32    ,							UpperCamelCase__:       Any=5    ,							UpperCamelCase__:       Optional[Any]=4    ,							UpperCamelCase__:       Dict=37    ,							UpperCamelCase__:       List[Any]="gelu"    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       List[Any]=10    ,							UpperCamelCase__:       Tuple=0.02    ,							UpperCamelCase__:       Optional[int]=3    ,							UpperCamelCase__:       Dict=0.6    ,							UpperCamelCase__:       int=None    ,							):
														lowerCamelCase__      :			Dict 						= parent
														lowerCamelCase__      :			Optional[Any] 						= batch_size
														lowerCamelCase__      :			Optional[int] 						= image_size
														lowerCamelCase__      :			Optional[Any] 						= patch_size
														lowerCamelCase__      :			Any 						= num_channels
														lowerCamelCase__      :			Any 						= is_training
														lowerCamelCase__      :			Union[str, Any] 						= use_labels
														lowerCamelCase__      :			List[str] 						= hidden_size
														lowerCamelCase__      :			List[str] 						= num_hidden_layers
														lowerCamelCase__      :			List[Any] 						= num_attention_heads
														lowerCamelCase__      :			str 						= intermediate_size
														lowerCamelCase__      :			str 						= hidden_act
														lowerCamelCase__      :			Any 						= hidden_dropout_prob
														lowerCamelCase__      :			Optional[int] 						= attention_probs_dropout_prob
														lowerCamelCase__      :			List[Any] 						= type_sequence_label_size
														lowerCamelCase__      :			int 						= initializer_range
														lowerCamelCase__      :			List[str] 						= mask_ratio
														lowerCamelCase__      :			List[Any] 						= scope

														# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
														# (we add 1 for the [CLS] token)
														lowerCamelCase__      :			str 						= (image_size // patch_size) ** 2
														lowerCamelCase__      :			Optional[int] 						= int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCamelCase__      :			Optional[int] 						= None
														if self.use_labels:
																					lowerCamelCase__      :			List[str] 						= ids_tensor([self.batch_size]    ,							self.type_sequence_label_size  )

														lowerCamelCase__      :			Any 						= self.get_config()

														return config, pixel_values, labels




							def 	lowerCamelCase_       (							self:       str  ):
														return ViTMAEConfig(
														    image_size=self.image_size    ,							patch_size=self.patch_size    ,							num_channels=self.num_channels    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							is_decoder=UpperCamelCase__    ,							initializer_range=self.initializer_range    ,							mask_ratio=self.mask_ratio    ,							)




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Tuple 						= ViTMAEModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)  )




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Dict  ):
														lowerCamelCase__      :			int 						= ViTMAEForPreTraining(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Optional[int] 						= model(UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= (self.image_size // self.patch_size) ** 2
														lowerCamelCase__      :			str 						= self.patch_size**2 * self.num_channels
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, num_patches, expected_num_channels)  )

														# test greyscale images
														lowerCamelCase__      :			Dict 						= 1
														lowerCamelCase__      :			Optional[int] 						= ViTMAEForPreTraining(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Dict 						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
														lowerCamelCase__      :			Optional[Any] 						= model(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= self.patch_size**2
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, num_patches, expected_num_channels)  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Optional[int] 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Any 						= config_and_inputs
														lowerCamelCase__      :			Optional[int] 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							a          = {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Tuple 						= ViTMAEModelTester(self  )
														lowerCamelCase__      :			Union[str, Any] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       Dict  ):
														pass




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			str 						= model_class(UpperCamelCase__  )
																					self.assertIsInstance(model.get_input_embeddings()    ,							(nn.Module)  )
																					lowerCamelCase__      :			Optional[Any] 						= model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__    ,							nn.Linear  )  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Any 						= model_class(UpperCamelCase__  )
																					lowerCamelCase__      :			str 						= inspect.signature(model.forward  )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__      :			Any 						= [*signature.parameters.keys()]

																					lowerCamelCase__      :			List[str] 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Optional[int]  ):
														# make masks reproducible
														np.random.seed(2  )

														lowerCamelCase__      :			Tuple 						= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
														lowerCamelCase__      :			List[str] 						= np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
														lowerCamelCase__      :			Tuple 						= torch.from_numpy(UpperCamelCase__  )

														# Add `noise` argument.
														# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
														lowerCamelCase__      :			Tuple 						= pt_noise

														super().check_pt_tf_models(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Union[str, Any] 						= model_class(UpperCamelCase__  )
																					model.to(UpperCamelCase__  )
																					model.eval()
																					# make random mask reproducible
																					torch.manual_seed(2  )
																					with torch.no_grad():
																												lowerCamelCase__      :			Optional[int] 						= model(**self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )  )

																					lowerCamelCase__      :			Optional[int] 						= outputs[0].cpu().numpy()
																					lowerCamelCase__      :			List[str] 						= 0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(UpperCamelCase__  )
																												lowerCamelCase__      :			List[str] 						= model_class.from_pretrained(UpperCamelCase__  )
																												model.to(UpperCamelCase__  )
																												# make random mask reproducible
																												torch.manual_seed(2  )
																												with torch.no_grad():
																																			lowerCamelCase__      :			Tuple 						= model(**self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )  )

																												# Make sure we don't have nans
																												lowerCamelCase__      :			Dict 						= after_outputs[0].cpu().numpy()
																												lowerCamelCase__      :			Tuple 						= 0
																												lowerCamelCase__      :			Union[str, Any] 						= np.amax(np.abs(out_a - out_a  )  )
																												self.assertLessEqual(UpperCamelCase__    ,							1e-5  )




							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""  )
							def 	lowerCamelCase_       (							self:       Any  ):
														pass




							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""  )
							def 	lowerCamelCase_       (							self:       List[str]  ):
														pass




							@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""  )
							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														pass




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Tuple 						= ViTMAEModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> List[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



@require_torch
@require_vision
class _lowercase			(       unittest.TestCase	):




							@cached_property
							def 	lowerCamelCase_       (							self:       List[str]  ):
														return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""  ) if is_vision_available() else None




							@slow
							def 	lowerCamelCase_       (							self:       Tuple  ):
														# make random mask reproducible across the PT and TF model
														np.random.seed(2  )

														lowerCamelCase__      :			str 						= ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""  ).to(UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= self.default_image_processor
														lowerCamelCase__      :			List[str] 						= prepare_img()
														lowerCamelCase__      :			int 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# prepare a noise vector that will be also used for testing the TF model
														# (this way we can ensure that the PT and TF models operate on the same inputs)
														lowerCamelCase__      :			List[str] 						= ViTMAEConfig()
														lowerCamelCase__      :			int 						= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
														lowerCamelCase__      :			Any 						= np.random.uniform(size=(1, num_patches)  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			List[Any] 						= model(**UpperCamelCase__    ,							noise=torch.from_numpy(UpperCamelCase__  ).to(device=UpperCamelCase__  )  )

														# verify the logits
														lowerCamelCase__      :			List[str] 						= torch.Size((1, 196, 768)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			str 						= torch.tensor(
														    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]  )

														self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,							expected_slice.to(UpperCamelCase__  )    ,							atol=1e-4  )  )

