


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   (      A__						: Union[str, Any]=32						,						A__						: Optional[Any]=10						,						A__						: Any=100						,						A__						: Optional[Any]=1026						,						A__						: Union[str, Any]=True						,						A__						: List[Any]="data/tokenized_stories_train_wikitext103.jbl"						,						A__						: Optional[int]="igf_context_pairs.jbl"						,						)     ->    List[str]:
   set_seed(3					)
   # generate train_data and objective_set
   lowerCamelCase_,	lowerCamelCase_						:   Tuple		= generate_datasets(
       __lowercase						,						__lowercase						,						number=__lowercase						,						min_len=1026						,						trim=__lowercase					)
   # keeps model same across runs
   set_seed(4					)
   # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
   # can we train on GPU?
   lowerCamelCase_						:   Optional[Any]		= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""					)

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

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

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


def    __lowerCamelCase   (      A__						: str						,						A__						: Dict=15						,						A__						: List[Any]=128						,						A__						: str=100						,						A__						: int="igf_model.pt"						,						)     ->    List[Any]:
   set_seed(42					)

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

   # Initialize secondary learner to use embedding weights of model
   lowerCamelCase_						:   Optional[int]		= SecondaryLearner(__lowercase					)

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

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner


def    __lowerCamelCase   (      A__						: Dict						,						A__						: Any						,						A__						: Any						,						A__						: Optional[int]=32						,						A__						: Dict=1000						,						A__						: str=16						,						A__						: int=1.0						,						A__						: Union[str, Any]=recopy_gpta						,						A__						: Tuple=None						,						A__						: List[Any]=10						,						A__						: List[str]="gpt2_finetuned.pt"						,						)     ->    Dict:
   lowerCamelCase_						:   Union[str, Any]		= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""					)
   lowerCamelCase_						:   Optional[int]		= RandomSampler(__lowercase					)
   lowerCamelCase_						:   int		= DataLoader(__lowercase						,						sampler=__lowercase					)

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

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(__lowercase					)
      secondary_learner.eval()
   lowerCamelCase_						:   int		= []
   lowerCamelCase_						:   str		= 0

   lowerCamelCase_						:   str		= []
   lowerCamelCase_						:   int		= []

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

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

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

            if global_step == 10:
               lowerCamelCase_						:   Any		= -1
            if predicted_q < threshold:
               lowerCamelCase_						:   Tuple		= False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()					)					)
            lowerCamelCase_						:   Optional[Any]		= 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_						:   List[str]		= 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_						:   List[str]		= compute_perplexity(__lowercase						,						__lowercase						,						__lowercase					)
               test_perps.append(__lowercase					)

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

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


def    __lowerCamelCase   (      )     ->    List[str]:
   lowerCamelCase_						:   Optional[Any]		= argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""					)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

   parser.add_argument("""--finetuned_model_name"""						,						default="""gpt2_finetuned.pt"""						,						type=__lowercase						,						help="""finetuned_model_name"""					)

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

   # function calls
   # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
   generate_n_pairs(
       context_len=32						,						max_steps=10						,						size_objective_set=100						,						min_len=1026						,						trim=__lowercase						,						data_file="""data/tokenized_stories_train_wikitext103.jbl"""						,						igf_data_file="""igf_context_pairs.jbl"""						,						)

   # Load train data for secondary learner
   lowerCamelCase_						:   Tuple		= joblib.load("""data/IGF_values.jbl"""					)

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

   # load pretrained gpt2 model
   lowerCamelCase_						:   Any		= GPTaLMHeadModel.from_pretrained("""gpt2"""					)
   set_seed(42					)

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

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


if __name__ == "__main__":
      main()













import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image


class     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=2		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=10		,		_UpperCAmelCase=3		,		_UpperCAmelCase=32 * 8		,		_UpperCAmelCase=32 * 8		,		_UpperCAmelCase=4		,		_UpperCAmelCase=64		,		)      ->			List[Any]:
     UpperCamelCase_						=  parent
     UpperCamelCase_						=  batch_size
     UpperCamelCase_						=  is_training
     UpperCamelCase_						=  use_auxiliary_loss
     UpperCamelCase_						=  num_queries
     UpperCamelCase_						=  num_channels
     UpperCamelCase_						=  min_size
     UpperCamelCase_						=  max_size
     UpperCamelCase_						=  num_labels
     UpperCamelCase_						=  hidden_dim
     UpperCamelCase_						=  hidden_dim




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
         _UpperCAmelCase			)

     UpperCamelCase_						=  torch.ones([self.batch_size, self.min_size, self.max_size]		,		device=_UpperCAmelCase			)

     UpperCamelCase_						=  (
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,		device=_UpperCAmelCase			) > 0.5
     ).float()
     UpperCamelCase_						=  (torch.rand((self.batch_size, self.num_labels)		,		device=_UpperCAmelCase			) > 0.5).long()

     UpperCamelCase_						=  self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  MaskaFormerConfig(
         hidden_size=self.hidden_dim		,		)
     UpperCamelCase_						=  self.num_queries
     UpperCamelCase_						=  self.num_labels

     UpperCamelCase_						=  [1, 1, 1, 1]
     UpperCamelCase_						=  self.num_channels

     UpperCamelCase_						=  64
     UpperCamelCase_						=  128
     UpperCamelCase_						=  self.hidden_dim
     UpperCamelCase_						=  self.hidden_dim
     UpperCamelCase_						=  self.hidden_dim
     return config




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.prepare_config_and_inputs()
     UpperCamelCase_						=  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
     return config, inputs_dict




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     UpperCamelCase_						=  output.encoder_hidden_states
     UpperCamelCase_						=  output.pixel_decoder_hidden_states
     UpperCamelCase_						=  output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(_UpperCAmelCase			)		,		len(config.backbone_config.depths			)			)
     self.parent.assertTrue(len(_UpperCAmelCase			)		,		len(config.backbone_config.depths			)			)
     self.parent.assertTrue(len(_UpperCAmelCase			)		,		config.decoder_layers			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False			)      ->			Any:
     with torch.no_grad():
        UpperCamelCase_						=  MaskaFormerModel(config=_UpperCAmelCase			)
        model.to(_UpperCAmelCase			)
        model.eval()

        UpperCamelCase_						=  model(pixel_values=_UpperCAmelCase		,		pixel_mask=_UpperCAmelCase			)
        UpperCamelCase_						=  model(_UpperCAmelCase		,		output_hidden_states=_UpperCAmelCase			)

     self.parent.assertEqual(
         output.transformer_decoder_last_hidden_state.shape		,		(self.batch_size, self.num_queries, self.hidden_dim)		,		)
     # let's ensure the other two hidden state exists
     self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None			)
     self.parent.assertTrue(output.encoder_last_hidden_state is not None			)

     if output_hidden_states:
        self.check_output_hidden_state(_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation(config=_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.eval()

     def comm_check_on_output(_UpperCAmelCase			):
        # let's still check that all the required stuff is there
        self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None			)
        self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None			)
        self.parent.assertTrue(result.encoder_last_hidden_state is not None			)
        # okay, now we need to check the logits shape
        # due to the encoder compression, masks have a //4 spatial size
        self.parent.assertEqual(
            result.masks_queries_logits.shape		,		(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)		,		)
        # + 1 for null class
        self.parent.assertEqual(
            result.class_queries_logits.shape		,		(self.batch_size, self.num_queries, self.num_labels + 1)			)

     with torch.no_grad():
        UpperCamelCase_						=  model(pixel_values=_UpperCAmelCase		,		pixel_mask=_UpperCAmelCase			)
        UpperCamelCase_						=  model(_UpperCAmelCase			)

        comm_check_on_output(_UpperCAmelCase			)

        UpperCamelCase_						=  model(
            pixel_values=_UpperCAmelCase		,		pixel_mask=_UpperCAmelCase		,		mask_labels=_UpperCAmelCase		,		class_labels=_UpperCAmelCase			)

     comm_check_on_output(_UpperCAmelCase			)

     self.parent.assertTrue(result.loss is not None			)
     self.parent.assertEqual(result.loss.shape		,		torch.Size([1]			)			)




@require_torch
class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  A_						       = {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

  A_						       = False
  A_						       = False
  A_						       = False
  A_						       = False




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  MaskaFormerModelTester(self			)
     UpperCamelCase_						=  ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     self.config_tester.run_common_tests()




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase		,		**_UpperCAmelCase		,		output_hidden_states=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_UpperCAmelCase			)




  @unittest.skip(reason='Mask2Former does not use inputs_embeds'			)
  def  _UpperCAmelCase  (  self			)      ->			Any:
     pass




  @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'			)
  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     pass




  @unittest.skip(reason='Mask2Former is not a generative model'			)
  def  _UpperCAmelCase  (  self			)      ->			Any:
     pass




  @unittest.skip(reason='Mask2Former does not use token embeddings'			)
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     pass




  @require_torch_multi_gpu
  @unittest.skip(
      reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'			)
  def  _UpperCAmelCase  (  self			)      ->			int:
     pass




  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
  def  _UpperCAmelCase  (  self			)      ->			str:
     pass




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase_						=  ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase			)




  @slow
  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
        UpperCamelCase_						=  MaskaFormerModel.from_pretrained(_UpperCAmelCase			)
        self.assertIsNotNone(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  (self.model_tester.min_size,) * 2
     UpperCamelCase_						=  {
         'pixel_values': torch.randn((2, 3, *size)		,		device=_UpperCAmelCase			),
         'mask_labels': torch.randn((2, 10, *size)		,		device=_UpperCAmelCase			),
         'class_labels': torch.zeros(2		,		10		,		device=_UpperCAmelCase			).long(),
     }
     UpperCamelCase_						=  self.model_tester.get_config()

     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation(_UpperCAmelCase			).to(_UpperCAmelCase			)
     UpperCamelCase_						=  model(**_UpperCAmelCase			)
     self.assertTrue(outputs.loss is not None			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase		,		**_UpperCAmelCase		,		output_hidden_states=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_						=  model_class(_UpperCAmelCase			).to(_UpperCAmelCase			)
        UpperCamelCase_						=  model(**_UpperCAmelCase		,		output_attentions=_UpperCAmelCase			)
        self.assertTrue(outputs.attentions is not None			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     if not self.model_tester.is_training:
        return

     UpperCamelCase_						=  self.all_model_classes[1]
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()

     UpperCamelCase_						=  model_class(_UpperCAmelCase			)
     model.to(_UpperCAmelCase			)
     model.train()

     UpperCamelCase_						=  model(_UpperCAmelCase		,		mask_labels=_UpperCAmelCase		,		class_labels=_UpperCAmelCase			).loss
     loss.backward()




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_						=  self.all_model_classes[1]
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs()
     UpperCamelCase_						=  True
     UpperCamelCase_						=  True

     UpperCamelCase_						=  model_class(_UpperCAmelCase			).to(_UpperCAmelCase			)
     model.train()

     UpperCamelCase_						=  model(_UpperCAmelCase		,		mask_labels=_UpperCAmelCase		,		class_labels=_UpperCAmelCase			)

     UpperCamelCase_						=  outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     UpperCamelCase_						=  outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()

     UpperCamelCase_						=  outputs.transformer_decoder_hidden_states[0]
     transformer_decoder_hidden_states.retain_grad()

     UpperCamelCase_						=  outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=_UpperCAmelCase			)

     self.assertIsNotNone(encoder_hidden_states.grad			)
     self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
     self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
     self.assertIsNotNone(attentions.grad			)




snake_case__   :						List[Any]													=   1E-4






def      _snake_case     ():
   UpperCamelCase_						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


@require_vision
@slow
class     _a							(	unittest.TestCase     ):






  """simple docstring"""





  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     return "facebook/mask2former-swin-small-coco-instance"




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints			) if is_vision_available() else None




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(_UpperCAmelCase			)
     UpperCamelCase_						=  self.default_image_processor
     UpperCamelCase_						=  prepare_img()
     UpperCamelCase_						=  image_processor(_UpperCAmelCase		,		return_tensors='pt'			).to(_UpperCAmelCase			)
     UpperCamelCase_						=  inputs['pixel_values'].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
     # check size
     self.assertEqual(_UpperCAmelCase		,		(1, 3, 384, 384)			)

     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)

     UpperCamelCase_						=  torch.tensor(
         [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]			).to(_UpperCAmelCase			)
     self.assertTrue(
         torch.allclose(
             outputs.encoder_last_hidden_state[0, 0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)

     UpperCamelCase_						=  torch.tensor(
         [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]			).to(_UpperCAmelCase			)
     self.assertTrue(
         torch.allclose(
             outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)

     UpperCamelCase_						=  torch.tensor(
         [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]			).to(_UpperCAmelCase			)
     self.assertTrue(
         torch.allclose(
             outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(_UpperCAmelCase			).eval()
     UpperCamelCase_						=  self.default_image_processor
     UpperCamelCase_						=  prepare_img()
     UpperCamelCase_						=  image_processor(_UpperCAmelCase		,		return_tensors='pt'			).to(_UpperCAmelCase			)
     UpperCamelCase_						=  inputs['pixel_values'].shape
     # check size is divisible by 32
     self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
     # check size
     self.assertEqual(_UpperCAmelCase		,		(1, 3, 384, 384)			)

     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)
     # masks_queries_logits
     UpperCamelCase_						=  outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape		,		(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			)
     UpperCamelCase_						=  [
         [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
         [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
         [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
     ]
     UpperCamelCase_						=  torch.tensor(_UpperCAmelCase			).to(_UpperCAmelCase			)
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)
     # class_queries_logits
     UpperCamelCase_						=  outputs.class_queries_logits
     self.assertEqual(class_queries_logits.shape		,		(1, model.config.num_queries, model.config.num_labels + 1)			)
     UpperCamelCase_						=  torch.tensor(
         [
             [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
             [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
             [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
         ]			).to(_UpperCAmelCase			)
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,		_UpperCAmelCase		,		atol=_UpperCAmelCase			)			)




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(_UpperCAmelCase			).eval()
     UpperCamelCase_						=  self.default_image_processor

     UpperCamelCase_						=  image_processor(
         [np.zeros((3, 800, 1333)			), np.zeros((3, 800, 1333)			)]		,		segmentation_maps=[np.zeros((384, 384)			).astype(np.floataa			), np.zeros((384, 384)			).astype(np.floataa			)]		,		return_tensors='pt'		,		)

     UpperCamelCase_						=  inputs['pixel_values'].to(_UpperCAmelCase			)
     UpperCamelCase_						=  [el.to(_UpperCAmelCase			) for el in inputs['mask_labels']]
     UpperCamelCase_						=  [el.to(_UpperCAmelCase			) for el in inputs['class_labels']]

     with torch.no_grad():
        UpperCamelCase_						=  model(**_UpperCAmelCase			)

     self.assertTrue(outputs.loss is not None			)





