


"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
UpperCAmelCase				=						logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")




@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					field(
       default='''cifar10''' ,metadata={'''help''': '''Name of a dataset from the datasets package'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''The column name of the images in the files.'''}				)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''A folder containing the training data.'''}				)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''A folder containing the validation data.'''}				)
   __UpperCAmelCase							       =					field(
       default=0.1_5 ,metadata={'''help''': '''Percent to split off of train for validation.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of training examples to this '''
               '''value if set.'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
               '''value if set.'''
           )
       } ,)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Any   =		{}
      if self.train_dir is not None:
         _lowerCamelCase	:					int   =		self.train_dir
      if self.validation_dir is not None:
         _lowerCamelCase	:					Tuple   =		self.validation_dir
      _lowerCamelCase	:					Optional[int]   =		data_files if data_files else None







@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''Override some existing default config settings when a model is trained from scratch. Example: '''
               '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}				)
   __UpperCAmelCase							       =					field(
       default='''main''' ,metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''Name or path of preprocessor config.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=0.7_5 ,metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}				)







@dataclass
class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					field(
       default=1e-3 ,metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}				)







def 				_snake_case      (   __snake_case							:      Optional[Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		torch.stack([example["""pixel_values"""] for example in examples]   )
   return {"pixel_values": pixel_values}

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
   if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry("""run_mae"""						, __snake_case						, __snake_case   )

   # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""						, datefmt="""%m/%d/%Y %H:%M:%S"""						, handlers=[logging.StreamHandler(sys.stdout   )]						, )

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   _lowerCamelCase	:					Union[str, Any]   =		training_args.get_process_log_level()
   logger.setLevel(__snake_case   )
   transformers.utils.logging.set_verbosity(__snake_case   )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
       + F'distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'   )
   logger.info(F'Training/evaluation parameters {training_args}'   )

   # Detecting last checkpoint.
   _lowerCamelCase	:					List[Any]   =		None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      _lowerCamelCase	:					Optional[int]   =		get_last_checkpoint(training_args.output_dir   )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
         raise ValueError(
             F'Output directory ({training_args.output_dir}) already exists and is not empty. '
             """Use --overwrite_output_dir to overcome."""   )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
             """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""   )

    # Initialize our dataset.
   _lowerCamelCase	:					Optional[Any]   =		load_dataset(
       data_args.dataset_name						, data_args.dataset_config_name						, data_files=data_args.data_files						, cache_dir=model_args.cache_dir						, use_auth_token=True if model_args.use_auth_token else None						, )

   # If we don't have a validation split, split off a percentage of train as validation.
   _lowerCamelCase	:					Tuple   =		None if """validation""" in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split						, __snake_case   ) and data_args.train_val_split > 0.0:
      _lowerCamelCase	:					List[str]   =		ds["""train"""].train_test_split(data_args.train_val_split   )
      _lowerCamelCase	:					Union[str, Any]   =		split["""train"""]
      _lowerCamelCase	:					Optional[int]   =		split["""test"""]

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   _lowerCamelCase	:					str   =		{
       """cache_dir""": model_args.cache_dir,
       """revision""": model_args.model_revision,
       """use_auth_token""": True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      _lowerCamelCase	:					Dict   =		ViTMAEConfig.from_pretrained(model_args.config_name						, **__snake_case   )
   elif model_args.model_name_or_path:
      _lowerCamelCase	:					Union[str, Any]   =		ViTMAEConfig.from_pretrained(model_args.model_name_or_path						, **__snake_case   )
   else:
      _lowerCamelCase	:					Optional[Any]   =		ViTMAEConfig()
      logger.warning("""You are instantiating a new config instance from scratch."""   )
      if model_args.config_overrides is not None:
         logger.info(F'Overriding config: {model_args.config_overrides}'   )
         config.update_from_string(model_args.config_overrides   )
         logger.info(F'New config: {config}'   )

    # adapt config
   config.update(
       {
           """mask_ratio""": model_args.mask_ratio,
           """norm_pix_loss""": model_args.norm_pix_loss,
       }   )

   # create image processor
   if model_args.image_processor_name:
      _lowerCamelCase	:					str   =		ViTImageProcessor.from_pretrained(model_args.image_processor_name						, **__snake_case   )
   elif model_args.model_name_or_path:
      _lowerCamelCase	:					Dict   =		ViTImageProcessor.from_pretrained(model_args.model_name_or_path						, **__snake_case   )
   else:
      _lowerCamelCase	:					Union[str, Any]   =		ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      _lowerCamelCase	:					List[Any]   =		ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path						, from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )						, config=__snake_case						, cache_dir=model_args.cache_dir						, revision=model_args.model_revision						, use_auth_token=True if model_args.use_auth_token else None						, )
   else:
      logger.info("""Training new model from scratch"""   )
      _lowerCamelCase	:					Union[str, Any]   =		ViTMAEForPreTraining(__snake_case   )

   if training_args.do_train:
      _lowerCamelCase	:					List[Any]   =		ds["""train"""].column_names
   else:
      _lowerCamelCase	:					Union[str, Any]   =		ds["""validation"""].column_names

   if data_args.image_column_name is not None:
      _lowerCamelCase	:					str   =		data_args.image_column_name
   elif "image" in column_names:
      _lowerCamelCase	:					Optional[Any]   =		"""image"""
   elif "img" in column_names:
      _lowerCamelCase	:					List[Any]   =		"""img"""
   else:
      _lowerCamelCase	:					str   =		column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      _lowerCamelCase	:					Dict   =		image_processor.size["""shortest_edge"""]
   else:
      _lowerCamelCase	:					List[Any]   =		(image_processor.size["""height"""], image_processor.size["""width"""])
   _lowerCamelCase	:					Tuple   =		Compose(
       [
           Lambda(lambda __snake_case   : img.convert("""RGB"""   ) if img.mode != "RGB" else img   ),
           RandomResizedCrop(__snake_case						, scale=(0.2, 1.0)						, interpolation=InterpolationMode.BICUBIC   ),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean						, std=image_processor.image_std   ),
       ]   )

   def preprocess_images(__snake_case							:      Optional[Any]   ):
      _lowerCamelCase	:					Dict   =		[transforms(__snake_case   ) for image in examples[image_column_name]]
      return examples

   if training_args.do_train:
      if "train" not in ds:
         raise ValueError("""--do_train requires a train dataset"""   )
      if data_args.max_train_samples is not None:
         _lowerCamelCase	:					int   =		ds["""train"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
      # Set the training transforms
      ds["train"].set_transform(__snake_case   )

   if training_args.do_eval:
      if "validation" not in ds:
         raise ValueError("""--do_eval requires a validation dataset"""   )
      if data_args.max_eval_samples is not None:
         _lowerCamelCase	:					Union[str, Any]   =		(
             ds["""validation"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
         )
      # Set the validation transforms
      ds["validation"].set_transform(__snake_case   )

   # Compute absolute learning rate
   _lowerCamelCase	:					Optional[Any]   =		(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      _lowerCamelCase	:					Tuple   =		training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   _lowerCamelCase	:					Optional[Any]   =		Trainer(
       model=__snake_case						, args=__snake_case						, train_dataset=ds["""train"""] if training_args.do_train else None						, eval_dataset=ds["""validation"""] if training_args.do_eval else None						, tokenizer=__snake_case						, data_collator=__snake_case						, )

   # Training
   if training_args.do_train:
      _lowerCamelCase	:					Any   =		None
      if training_args.resume_from_checkpoint is not None:
         _lowerCamelCase	:					List[Any]   =		training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _lowerCamelCase	:					Union[str, Any]   =		last_checkpoint
      _lowerCamelCase	:					Optional[Any]   =		trainer.train(resume_from_checkpoint=__snake_case   )
      trainer.save_model()
      trainer.log_metrics("""train"""						, train_result.metrics   )
      trainer.save_metrics("""train"""						, train_result.metrics   )
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      _lowerCamelCase	:					int   =		trainer.evaluate()
      trainer.log_metrics("""eval"""						, __snake_case   )
      trainer.save_metrics("""eval"""						, __snake_case   )

   # Write model card and (optionally) push to hub
   _lowerCamelCase	:					Optional[Any]   =		{
       """tasks""": """masked-auto-encoding""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""masked-auto-encoding"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__snake_case   )
   else:
      trainer.create_model_card(**__snake_case   )

def 				_snake_case      (   __snake_case							:      Dict   ):




   """simple docstring"""







   main()


if __name__ == "__main__":
 main()





"""simple docstring"""
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 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=10      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=32 * 8      ,   SCREAMING_SNAKE_CASE=32 * 8      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=64      ,   )  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		parent
      _lowerCamelCase	:					List[Any]   =		batch_size
      _lowerCamelCase	:					Tuple   =		is_training
      _lowerCamelCase	:					Tuple   =		use_auxiliary_loss
      _lowerCamelCase	:					Any   =		num_queries
      _lowerCamelCase	:					List[str]   =		num_channels
      _lowerCamelCase	:					List[str]   =		min_size
      _lowerCamelCase	:					Tuple   =		max_size
      _lowerCamelCase	:					str   =		num_labels
      _lowerCamelCase	:					Any   =		hidden_dim
      _lowerCamelCase	:					Dict   =		hidden_dim





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

      _lowerCamelCase	:					List[Any]   =		torch.ones([self.batch_size, self.min_size, self.max_size]      ,   device=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		(
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,   device=SCREAMING_SNAKE_CASE) > 0.5
      ).float()
      _lowerCamelCase	:					Dict   =		(torch.rand((self.batch_size, self.num_labels)      ,   device=SCREAMING_SNAKE_CASE) > 0.5).long()

      _lowerCamelCase	:					Optional[int]   =		self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					List[str]   =		MaskaFormerConfig(
          hidden_size=self.hidden_dim      ,   )
      _lowerCamelCase	:					Any   =		self.num_queries
      _lowerCamelCase	:					int   =		self.num_labels

      _lowerCamelCase	:					int   =		[1, 1, 1, 1]
      _lowerCamelCase	:					Any   =		self.num_channels

      _lowerCamelCase	:					Optional[Any]   =		64
      _lowerCamelCase	:					str   =		128
      _lowerCamelCase	:					Optional[Any]   =		self.hidden_dim
      _lowerCamelCase	:					Any   =		self.hidden_dim
      _lowerCamelCase	:					List[Any]   =		self.hidden_dim
      return config





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		self.prepare_config_and_inputs()
      _lowerCamelCase	:					str   =		{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					str   =		output.encoder_hidden_states
      _lowerCamelCase	:					int   =		output.pixel_decoder_hidden_states
      _lowerCamelCase	:					Optional[int]   =		output.transformer_decoder_hidden_states

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=False)  ->  List[str]:
      with torch.no_grad():
         _lowerCamelCase	:					Optional[int]   =		MaskaFormerModel(config=SCREAMING_SNAKE_CASE)
         model.to(SCREAMING_SNAKE_CASE)
         model.eval()

         _lowerCamelCase	:					Optional[int]   =		model(pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[str]   =		model(SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)

      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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					str   =		MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.eval()

      def comm_check_on_output(SCREAMING_SNAKE_CASE):
         # 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():
         _lowerCamelCase	:					List[Any]   =		model(pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE)

         comm_check_on_output(SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Optional[Any]   =		model(
             pixel_values=SCREAMING_SNAKE_CASE      ,   pixel_mask=SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE)

      comm_check_on_output(SCREAMING_SNAKE_CASE)

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







@require_torch
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   __UpperCAmelCase							       =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		MaskaFormerModelTester(self)
      _lowerCamelCase	:					Union[str, Any]   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   has_text_modality=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   output_hidden_states=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE)





   @unittest.skip(reason="""Mask2Former does not use inputs_embeds""")
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      pass





   @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""")
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      pass





   @unittest.skip(reason="""Mask2Former is not a generative model""")
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      pass





   @unittest.skip(reason="""Mask2Former does not use token embeddings""")
   def 		UpperCamelCase_  (							self)  ->  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)  ->  Dict:
      pass





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





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

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

         _lowerCamelCase	:					int   =		["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   SCREAMING_SNAKE_CASE)





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





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Dict   =		(self.model_tester.min_size,) * 2
      _lowerCamelCase	:					str   =		{
          """pixel_values""": torch.randn((2, 3, *size)      ,   device=SCREAMING_SNAKE_CASE),
          """mask_labels""": torch.randn((2, 10, *size)      ,   device=SCREAMING_SNAKE_CASE),
          """class_labels""": torch.zeros(2      ,   10      ,   device=SCREAMING_SNAKE_CASE).long(),
      }
      _lowerCamelCase	:					List[str]   =		self.model_tester.get_config()

      _lowerCamelCase	:					Tuple   =		MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		model(**SCREAMING_SNAKE_CASE)
      self.assertTrue(outputs.loss is not None)





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





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

      for model_class in self.all_model_classes:
         _lowerCamelCase	:					str   =		model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Union[str, Any]   =		model(**SCREAMING_SNAKE_CASE      ,   output_attentions=SCREAMING_SNAKE_CASE)
         self.assertTrue(outputs.attentions is not None)





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

      _lowerCamelCase	:					Any   =		self.all_model_classes[1]
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs()

      _lowerCamelCase	:					List[Any]   =		model_class(SCREAMING_SNAKE_CASE)
      model.to(SCREAMING_SNAKE_CASE)
      model.train()

      _lowerCamelCase	:					int   =		model(SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE).loss
      loss.backward()





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

      _lowerCamelCase	:					str   =		model_class(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      model.train()

      _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE      ,   mask_labels=SCREAMING_SNAKE_CASE      ,   class_labels=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      _lowerCamelCase	:					int   =		outputs.pixel_decoder_hidden_states[0]
      pixel_decoder_hidden_states.retain_grad()

      _lowerCamelCase	:					str   =		outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      _lowerCamelCase	:					Optional[int]   =		outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE)

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







UpperCAmelCase				=						1e-4

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image





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





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  int:
      return "facebook/mask2former-swin-small-coco-instance"





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Tuple   =		MaskaFormerModel.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		self.default_image_processor
      _lowerCamelCase	:					List[str]   =		prepare_img()
      _lowerCamelCase	:					Union[str, Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		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(SCREAMING_SNAKE_CASE      ,   (1, 3, 384, 384))

      with torch.no_grad():
         _lowerCamelCase	:					Dict   =		model(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		torch.tensor(
          [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))

      _lowerCamelCase	:					Any   =		torch.tensor(
          [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))

      _lowerCamelCase	:					Dict   =		torch.tensor(
          [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
      _lowerCamelCase	:					Optional[Any]   =		self.default_image_processor
      _lowerCamelCase	:					Any   =		prepare_img()
      _lowerCamelCase	:					Dict   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""pt""").to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		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(SCREAMING_SNAKE_CASE      ,   (1, 3, 384, 384))

      with torch.no_grad():
         _lowerCamelCase	:					List[str]   =		model(**SCREAMING_SNAKE_CASE)
      # masks_queries_logits
      _lowerCamelCase	:					str   =		outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape      ,   (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
      _lowerCamelCase	:					Any   =		[
          [-8.78_39, -9.00_56, -8.81_21],
          [-7.41_04, -7.03_13, -6.54_01],
          [-6.61_05, -6.34_27, -6.46_75],
      ]
      _lowerCamelCase	:					List[Any]   =		torch.tensor(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))
      # class_queries_logits
      _lowerCamelCase	:					List[str]   =		outputs.class_queries_logits
      self.assertEqual(class_queries_logits.shape      ,   (1, model.config.num_queries, model.config.num_labels + 1))
      _lowerCamelCase	:					Optional[Any]   =		torch.tensor(
          [
              [1.83_24, -8.08_35, -4.19_22],
              [0.84_50, -9.00_50, -3.60_53],
              [0.30_45, -7.72_93, -3.02_75],
          ]).to(SCREAMING_SNAKE_CASE)
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,   SCREAMING_SNAKE_CASE      ,   atol=SCREAMING_SNAKE_CASE))





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Tuple   =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE).eval()
      _lowerCamelCase	:					str   =		self.default_image_processor

      _lowerCamelCase	:					Tuple   =		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"""      ,   )

      _lowerCamelCase	:					Optional[Any]   =		inputs["""pixel_values"""].to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		[el.to(SCREAMING_SNAKE_CASE) for el in inputs["""mask_labels"""]]
      _lowerCamelCase	:					Union[str, Any]   =		[el.to(SCREAMING_SNAKE_CASE) for el in inputs["""class_labels"""]]

      with torch.no_grad():
         _lowerCamelCase	:					Any   =		model(**SCREAMING_SNAKE_CASE)

      self.assertTrue(outputs.loss is not None)

