





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser
def 	_A			(						SCREAMING_SNAKE_CASE	:  Dict=None			,			SCREAMING_SNAKE_CASE	:  str=None		):





  """simple docstring"""



  return field(default_factory=lambda: default			,			metadata=lowerCAmelCase_		)







@dataclass
class 							__lowerCAmelCase			:
      _lowercase							:   str          =     field(
          metadata={"""help""": """The csv file to plot."""}		,							)
      _lowercase							:   bool          =     field(
          default=_UpperCamelCase		,							metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,							)
      _lowercase							:   bool          =     field(
          default=_UpperCamelCase		,							metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,							)
      _lowercase							:   bool          =     field(
          default=_UpperCamelCase		,							metadata={"""help""": """Disable logarithmic scale when plotting"""}		,							)
      _lowercase							:   bool          =     field(
          default=_UpperCamelCase		,							metadata={
              """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
          }		,							)
      _lowercase							:   Optional[str]          =     field(
          default=_UpperCamelCase		,							metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,							)
      _lowercase							:   Optional[List[str]]          =     list_field(
          default=_UpperCamelCase		,							metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""})
def 	_A			(						SCREAMING_SNAKE_CASE	:  int		):





  """simple docstring"""



  try:
    int(lowerCAmelCase_		)
    return True
  except ValueError:
    return False
def 	_A			(						SCREAMING_SNAKE_CASE	:  Dict		):





  """simple docstring"""



  try:
    float(lowerCAmelCase_		)
    return True
  except ValueError:
    return False







class 							__lowerCAmelCase			:







      def __init__(       self						, lowerCAmelCase__				)  ->		Tuple:



        '''simple docstring'''




        a__      :   List[str]      =args
        a__      :   List[Any]      =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

        with open(self.args.csv_file						, newline=""				) as csv_file:
          a__      :   Optional[Any]      =csv.DictReader(_UpperCAmelCase				)
          for row in reader:
            a__      :   Optional[Any]      =row['model']
            self.result_dict[model_name]["bsz"].append(int(row["batch_size"]				)				)
            self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]				)				)
            if can_convert_to_int(row["result"]				):
              # value is not None
              a__      :   List[str]      =int(row["result"]				)
            elif can_convert_to_float(row["result"]				):
              # value is not None
              a__      :   Union[str, Any]      =float(row["result"]				)







      def   _lowercase    (       self				)  ->		Dict:



        '''simple docstring'''




        a__      :   str      =plt.subplots()
        a__      :   Dict      ='Time usage' if self.args.is_time else 'Memory usage'
        a__      :   List[Any]      =title_str + ' for training' if self.args.is_train else title_str + ' for inference'

        if not self.args.no_log_scale:
          # set logarithm scales
          ax.set_xscale("log"				)
          ax.set_yscale("log"				)

        for axis in [ax.xaxis, ax.yaxis]:
          axis.set_major_formatter(ScalarFormatter()				)

        for model_name_idx, model_name in enumerate(self.result_dict.keys()				):
          a__      :   Optional[Any]      =sorted(set(self.result_dict[model_name]["bsz"]				)				)
          a__      :   Tuple      =sorted(set(self.result_dict[model_name]["seq_len"]				)				)
          a__      :   Optional[Any]      =self.result_dict[model_name]['result']

          (a__)      :   Dict      =(
              (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
          )

          a__      :   str      =(
              model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
          )

          for inner_loop_value in inner_loop_array:
            if self.args.plot_along_batch:
              a__      :   List[str]      =np.asarray(
                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						, dtype=_UpperCAmelCase						, )
            else:
              a__      :   str      =np.asarray(
                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]						, dtype=np.floataa						, )

            (a__)      :   List[Any]      =(
                ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
            )

            a__      :   Any      =np.asarray(_UpperCAmelCase						, _UpperCAmelCase				)[: len(_UpperCAmelCase				)]
            plt.scatter(
                _UpperCAmelCase						, _UpperCAmelCase						, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''				)
            plt.plot(_UpperCAmelCase						, _UpperCAmelCase						, "--"				)

          title_str += F''' {label_model_name} vs.'''

        a__      :   Optional[Any]      =title_str[:-4]
        a__      :   Any      ='Time in s' if self.args.is_time else 'Memory in MB'

        # plot
        plt.title(_UpperCAmelCase				)
        plt.xlabel(_UpperCAmelCase				)
        plt.ylabel(_UpperCAmelCase				)
        plt.legend()

        if self.args.figure_png_file is not None:
          plt.savefig(self.args.figure_png_file				)
        else:
          plt.show()
def 	_A			(						):





  """simple docstring"""



  a__      :   Optional[int]      =HfArgumentParser(lowerCAmelCase_		)
  a__      :   List[str]      =parser.parse_args_into_dataclasses()[0]
  a__      :   Any      =Plot(args=lowerCAmelCase_		)
  plot.plot()


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       __magic_name__		:







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







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Union[str, Any]								=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
									    _UpperCAmelCase						)

									_a	: int								=				torch.ones([self.batch_size, self.min_size, self.max_size]      ,device=_UpperCAmelCase						)

									_a	: List[Any]								=				(
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,device=_UpperCAmelCase						) > 0.5
									).float()
									_a	: str								=				(torch.rand((self.batch_size, self.num_labels)      ,device=_UpperCAmelCase						) > 0.5).long()

									_a	: Union[str, Any]								=				self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels







							def 				__lowercase     (				self   :			List[str]						):
									_a	: int								=				MaskaFormerConfig(
									    hidden_size=self.hidden_dim      ,)
									_a	: List[str]								=				self.num_queries
									_a	: List[Any]								=				self.num_labels

									_a	: str								=				[1, 1, 1, 1]
									_a	: List[Any]								=				self.num_channels

									_a	: Optional[int]								=				64
									_a	: List[str]								=				128
									_a	: Union[str, Any]								=				self.hidden_dim
									_a	: Optional[Any]								=				self.hidden_dim
									_a	: Optional[int]								=				self.hidden_dim
									return config







							def 				__lowercase     (				self   :			str						):
									_a			,   _a			,   _a			,   _a			,   _a	: Tuple								=				self.prepare_config_and_inputs()
									_a	: List[Any]								=				{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
									return config, inputs_dict







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

											_a	: Tuple								=				model(pixel_values=_UpperCAmelCase      ,pixel_mask=_UpperCAmelCase						)
											_a	: Tuple								=				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 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			int      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Any						):
									_a	: List[Any]								=				MaskaFormerForUniversalSegmentation(config=_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()

									def comm_check_on_output(_UpperCAmelCase   :			Tuple						):
											# 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():
											_a	: str								=				model(pixel_values=_UpperCAmelCase      ,pixel_mask=_UpperCAmelCase						)
											_a	: int								=				model(_UpperCAmelCase						)

											comm_check_on_output(_UpperCAmelCase						)

											_a	: str								=				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       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Union[str, Any]  							=   (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							lowerCAmelCase			:       Optional[Any]  							=   {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

							lowerCAmelCase			:       Any  							=   False
							lowerCAmelCase			:       Any  							=   False
							lowerCAmelCase			:       str  							=   False
							lowerCAmelCase			:       str  							=   False







							def 				__lowercase     (				self   :			Dict						):
									_a	: int								=				MaskaFormerModelTester(self						)
									_a	: Tuple								=				ConfigTester(self      ,config_class=_UpperCAmelCase      ,has_text_modality=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[Any]						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Dict						):
									_a			,   _a	: Union[str, Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase      ,**_UpperCAmelCase      ,output_hidden_states=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[Any]								=				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 				__lowercase     (				self   :			Tuple						):
									pass







							@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'						)
							def 				__lowercase     (				self   :			Union[str, Any]						):
									pass







							@unittest.skip(reason='Mask2Former is not a generative model'						)
							def 				__lowercase     (				self   :			Tuple						):
									pass







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







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







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







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

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

											_a	: Optional[int]								=				['pixel_values']
											self.assertListEqual(arg_names[:1]      ,_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			List[str]						):
									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
											_a	: Union[str, Any]								=				MaskaFormerModel.from_pretrained(_UpperCAmelCase						)
											self.assertIsNotNone(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict						):
									_a	: Dict								=				(self.model_tester.min_size,) * 2
									_a	: Union[str, Any]								=				{
									    '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(),
									}
									_a	: Dict								=				self.model_tester.get_config()

									_a	: List[str]								=				MaskaFormerForUniversalSegmentation(_UpperCAmelCase						).to(_UpperCAmelCase						)
									_a	: Tuple								=				model(**_UpperCAmelCase						)
									self.assertTrue(outputs.loss is not None						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a			,   _a	: Dict								=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(_UpperCAmelCase      ,**_UpperCAmelCase      ,output_hidden_states=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a			,   _a	: str								=				self.model_tester.prepare_config_and_inputs_for_common()

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







							def 				__lowercase     (				self   :			List[Any]						):
									if not self.model_tester.is_training:
											return

									_a	: List[Any]								=				self.all_model_classes[1]
									_a			,   _a			,   _a			,   _a			,   _a	: int								=				self.model_tester.prepare_config_and_inputs()

									_a	: Any								=				model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.train()

									_a	: int								=				model(_UpperCAmelCase      ,mask_labels=_UpperCAmelCase      ,class_labels=_UpperCAmelCase						).loss
									loss.backward()







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				self.all_model_classes[1]
									_a			,   _a			,   _a			,   _a			,   _a	: Any								=				self.model_tester.prepare_config_and_inputs()
									_a	: Optional[Any]								=				True
									_a	: Tuple								=				True

									_a	: int								=				model_class(_UpperCAmelCase						).to(_UpperCAmelCase						)
									model.train()

									_a	: int								=				model(_UpperCAmelCase      ,mask_labels=_UpperCAmelCase      ,class_labels=_UpperCAmelCase						)

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

									_a	: Tuple								=				outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

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

									_a	: Optional[Any]								=				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						)
__lowerCAmelCase		     =  1e-4



def 	__lowerCamelCase	( )     -> Any:
		_a	: Union[str, Any]								=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
		return image

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







							@cached_property
							def 				__lowercase     (				self   :			Union[str, Any]						):
									return "facebook/mask2former-swin-small-coco-instance"







							@cached_property
							def 				__lowercase     (				self   :			List[Any]						):
									return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(_UpperCAmelCase						)
									_a	: Optional[int]								=				self.default_image_processor
									_a	: List[str]								=				prepare_img()
									_a	: int								=				image_processor(_UpperCAmelCase      ,return_tensors='pt'						).to(_UpperCAmelCase						)
									_a	: int								=				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():
											_a	: List[str]								=				model(**_UpperCAmelCase						)

									_a	: Dict								=				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(_UpperCAmelCase						)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,_UpperCAmelCase      ,atol=_UpperCAmelCase						)						)

									_a	: List[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(_UpperCAmelCase						)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,_UpperCAmelCase      ,atol=_UpperCAmelCase						)						)

									_a	: Any								=				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(_UpperCAmelCase						)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,_UpperCAmelCase      ,atol=_UpperCAmelCase						)						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Tuple								=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(_UpperCAmelCase						).eval()
									_a	: List[str]								=				self.default_image_processor
									_a	: Union[str, Any]								=				prepare_img()
									_a	: Optional[Any]								=				image_processor(_UpperCAmelCase      ,return_tensors='pt'						).to(_UpperCAmelCase						)
									_a	: 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(_UpperCAmelCase      ,(1, 3, 384, 384)						)

									with torch.no_grad():
											_a	: Any								=				model(**_UpperCAmelCase						)
									# masks_queries_logits
									_a	: List[str]								=				outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape      ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
									_a	: Optional[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],
									]
									_a	: List[str]								=				torch.tensor(_UpperCAmelCase						).to(_UpperCAmelCase						)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,_UpperCAmelCase      ,atol=_UpperCAmelCase						)						)
									# class_queries_logits
									_a	: Any								=				outputs.class_queries_logits
									self.assertEqual(class_queries_logits.shape      ,(1, model.config.num_queries, model.config.num_labels + 1)						)
									_a	: Dict								=				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(_UpperCAmelCase						)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,_UpperCAmelCase      ,atol=_UpperCAmelCase						)						)







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(_UpperCAmelCase						).eval()
									_a	: List[Any]								=				self.default_image_processor

									_a	: Union[str, Any]								=				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'      ,)

									_a	: int								=				inputs['pixel_values'].to(_UpperCAmelCase						)
									_a	: List[Any]								=				[el.to(_UpperCAmelCase						) for el in inputs['mask_labels']]
									_a	: Any								=				[el.to(_UpperCAmelCase						) for el in inputs['class_labels']]

									with torch.no_grad():
											_a	: Optional[int]								=				model(**_UpperCAmelCase						)

									self.assertTrue(outputs.loss is not None						)




