



"""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       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=True    ,			SCREAMING_SNAKE_CASE_				:      Tuple=False    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=1_0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=3    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=3_2 * 8    ,			SCREAMING_SNAKE_CASE_				:      List[str]=3_2 * 8    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=4    ,			SCREAMING_SNAKE_CASE_				:      str=6_4    ,			):
        lowerCAmelCase_   :				Optional[Any]								= parent
        lowerCAmelCase_   :				Optional[int]								= batch_size
        lowerCAmelCase_   :				Tuple								= is_training
        lowerCAmelCase_   :				Union[str, Any]								= use_auxiliary_loss
        lowerCAmelCase_   :				int								= num_queries
        lowerCAmelCase_   :				Any								= num_channels
        lowerCAmelCase_   :				Any								= min_size
        lowerCAmelCase_   :				int								= max_size
        lowerCAmelCase_   :				List[Any]								= num_labels
        lowerCAmelCase_   :				Optional[int]								= hidden_dim
        lowerCAmelCase_   :				Dict								= hidden_dim



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				List[str]								= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
            SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Any								= torch.ones([self.batch_size, self.min_size, self.max_size]    ,			device=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[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      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Optional[Any]								= MaskaFormerConfig(
            hidden_size=self.hidden_dim    ,			)
        lowerCAmelCase_   :				int								= self.num_queries
        lowerCAmelCase_   :				Optional[int]								= self.num_labels

        lowerCAmelCase_   :				Any								= [1, 1, 1, 1]
        lowerCAmelCase_   :				Tuple								= self.num_channels

        lowerCAmelCase_   :				str								= 6_4
        lowerCAmelCase_   :				Union[str, Any]								= 1_2_8
        lowerCAmelCase_   :				str								= self.hidden_dim
        lowerCAmelCase_   :				Tuple								= self.hidden_dim
        lowerCAmelCase_   :				int								= self.hidden_dim
        return config



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Any								= self.prepare_config_and_inputs()
        lowerCAmelCase_   :				Tuple								= {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
        return config, inputs_dict



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				List[Any]								= output.encoder_hidden_states
        lowerCAmelCase_   :				Any								= output.pixel_decoder_hidden_states
        lowerCAmelCase_   :				List[Any]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False       ):
        with torch.no_grad():
          lowerCAmelCase_   :				Any								= MaskaFormerModel(config=SCREAMING_SNAKE_CASE_       )
          model.to(SCREAMING_SNAKE_CASE_       )
          model.eval()

          lowerCAmelCase_   :				Any								= model(pixel_values=SCREAMING_SNAKE_CASE_    ,			pixel_mask=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Optional[int]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				Tuple								= MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()

        def comm_check_on_output(SCREAMING_SNAKE_CASE_				:      List[str]       ):
          # 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_   :				int								= 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_   :				int								= 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       UpperCamelCase__   (	lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




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

      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				Optional[Any]								= MaskaFormerModelTester(self       )
        lowerCAmelCase_   :				Optional[int]								= ConfigTester(self    ,			config_class=SCREAMING_SNAKE_CASE_    ,			has_text_modality=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        self.config_tester.run_common_tests()



      def      SCREAMING_SNAKE_CASE__ (      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      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Any								= 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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        pass



      @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        pass



      @unittest.skip(reason='Mask2Former is not a generative model'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        pass



      @unittest.skip(reason='Mask2Former does not use token embeddings'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        pass



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



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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, 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_   :				List[Any]								= ['pixel_values']
          self.assertListEqual(arg_names[:1]    ,			SCREAMING_SNAKE_CASE_       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
          lowerCAmelCase_   :				Any								= MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				Any								= (self.model_tester.min_size,) * 2
        lowerCAmelCase_   :				Tuple								= {
            'pixel_values': torch.randn((2, 3, *size)    ,			device=SCREAMING_SNAKE_CASE_       ),
            'mask_labels': torch.randn((2, 1_0, *size)    ,			device=SCREAMING_SNAKE_CASE_       ),
            'class_labels': torch.zeros(2    ,			1_0    ,			device=SCREAMING_SNAKE_CASE_       ).long(),
        }
        lowerCAmelCase_   :				str								= self.model_tester.get_config()

        lowerCAmelCase_   :				Dict								= MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= model(**SCREAMING_SNAKE_CASE_       )
        self.assertTrue(outputs.loss is not None       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				int								= 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      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				List[Any]								= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				int								= model_class(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Optional[Any]								= model(**SCREAMING_SNAKE_CASE_    ,			output_attentions=SCREAMING_SNAKE_CASE_       )
          self.assertTrue(outputs.attentions is not None       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        if not self.model_tester.is_training:
          return

        lowerCAmelCase_   :				Dict								= self.all_model_classes[1]
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs()

        lowerCAmelCase_   :				Tuple								= model_class(SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.train()

        lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_    ,			mask_labels=SCREAMING_SNAKE_CASE_    ,			class_labels=SCREAMING_SNAKE_CASE_       ).loss
        loss.backward()





      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				Optional[int]								= self.all_model_classes[1]
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs()
        lowerCAmelCase_   :				Union[str, Any]								= True
        lowerCAmelCase_   :				Any								= True

        lowerCAmelCase_   :				Optional[Any]								= model_class(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
        model.train()

        lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_    ,			mask_labels=SCREAMING_SNAKE_CASE_    ,			class_labels=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[Any]								= outputs.encoder_hidden_states[0]
        encoder_hidden_states.retain_grad()

        lowerCAmelCase_   :				Any								= outputs.pixel_decoder_hidden_states[0]
        pixel_decoder_hidden_states.retain_grad()

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

        lowerCAmelCase_   :				Optional[Any]								= 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       )




lowercase__		:				List[str]          =	1e-4

def 				UpperCamelCase_    (    )						->   Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image







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




      """simple docstring"""




      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        return "facebook/mask2former-swin-small-coco-instance"



      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Optional[int]								= MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= self.default_image_processor
        lowerCAmelCase_   :				int								= prepare_img()
        lowerCAmelCase_   :				List[str]								= image_processor(SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       ).to(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= inputs['pixel_values'].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
        # check size
        self.assertEqual(SCREAMING_SNAKE_CASE_    ,			(1, 3, 3_8_4, 3_8_4)       )

        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_   :				str								= 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_   :				Optional[int]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Union[str, Any]								= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(SCREAMING_SNAKE_CASE_       ).eval()
        lowerCAmelCase_   :				Union[str, Any]								= self.default_image_processor
        lowerCAmelCase_   :				List[Any]								= prepare_img()
        lowerCAmelCase_   :				Any								= image_processor(SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       ).to(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Optional[Any]								= inputs['pixel_values'].shape
        # check size is divisible by 32
        self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
        # check size
        self.assertEqual(SCREAMING_SNAKE_CASE_    ,			(1, 3, 3_8_4, 3_8_4)       )

        with torch.no_grad():
          lowerCAmelCase_   :				Optional[int]								= 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_   :				Tuple								= [
            [-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_   :				str								= 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      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Optional[Any]								= MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(SCREAMING_SNAKE_CASE_       ).eval()
        lowerCAmelCase_   :				Optional[int]								= self.default_image_processor

        lowerCAmelCase_   :				Any								= image_processor(
            [np.zeros((3, 8_0_0, 1_3_3_3)       ), np.zeros((3, 8_0_0, 1_3_3_3)       )]    ,			segmentation_maps=[np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       ), np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       )]    ,			return_tensors='pt'    ,			)

        lowerCAmelCase_   :				Dict								= inputs['pixel_values'].to(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= [el.to(SCREAMING_SNAKE_CASE_       ) for el in inputs['mask_labels']]
        lowerCAmelCase_   :				str								= [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       )










"""simple docstring"""







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= inspect.getfile(accelerate.test_utils       )
        lowerCAmelCase_   :				Dict								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
        lowerCAmelCase_   :				int								= os.path.sep.join(
            mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
        lowerCAmelCase_   :				Union[str, Any]								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				Optional[int]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				int								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
        print(F"Command: {cmd}"       )
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )





      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
        lowerCAmelCase_   :				Any								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
        with patch_environment(omp_num_threads=1    ,			cuda_visible_devices='0,1'       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )


if __name__ == "__main__":
     lowercase__		:				Dict          =	Accelerator()
     lowercase__		:				List[Any]          =	(accelerator.state.process_index + 2, 1_0)
     lowercase__		:				Any          =	torch.randint(0, 1_0, shape).to(accelerator.device)

     lowercase__		:				List[Any]          =	""""""

     lowercase__		:				Union[str, Any]          =	accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowercase__		:				Optional[Any]          =	accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowercase__		:				Any          =	accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)





