



"""simple docstring"""






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

   if is_vision_available():
      from transformers import MaskFormerImageProcessor

if is_vision_available():
   from PIL import Image






class        __snake_case       :




  def __init__( self     :						Tuple						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						Any=2						,					__lowerCAmelCase     :						Any=True						,					__lowerCAmelCase     :						Optional[Any]=False						,					__lowerCAmelCase     :						Optional[Any]=1_0						,					__lowerCAmelCase     :						List[str]=3						,					__lowerCAmelCase     :						int=3_2 * 4						,					__lowerCAmelCase     :						Dict=3_2 * 6						,					__lowerCAmelCase     :						Any=4						,					__lowerCAmelCase     :						List[str]=3_2						,					):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= parent
         _lowerCamelCase						:				str    			= batch_size
         _lowerCamelCase						:				Dict    			= is_training
         _lowerCamelCase						:				str    			= use_auxiliary_loss
         _lowerCamelCase						:				Any    			= num_queries
         _lowerCamelCase						:				List[Any]    			= num_channels
         _lowerCamelCase						:				int    			= min_size
         _lowerCamelCase						:				Any    			= max_size
         _lowerCamelCase						:				int    			= num_labels
         _lowerCamelCase						:				List[str]    			= mask_feature_size




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
             __lowerCAmelCase		)

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

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

         _lowerCamelCase						:				Dict    			= self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         return MaskFormerConfig.from_backbone_and_decoder_configs(
             backbone_config=SwinConfig(
                 depths=[1, 1, 1, 1]						,					)						,					decoder_config=DetrConfig(
                 decoder_ffn_dim=1_2_8						,					num_queries=self.num_queries						,					decoder_attention_heads=2						,					d_model=self.mask_feature_size						,					)						,					mask_feature_size=self.mask_feature_size						,					fpn_feature_size=self.mask_feature_size						,					num_channels=self.num_channels						,					num_labels=self.num_labels						,					)




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase						:				Dict    			= self.prepare_config_and_inputs()
         _lowerCamelCase						:				List[str]    			= {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
         return config, inputs_dict




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= output.encoder_hidden_states
         _lowerCamelCase						:				Tuple    			= output.pixel_decoder_hidden_states
         _lowerCamelCase						:				Dict    			= output.transformer_decoder_hidden_states

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple						,					__lowerCAmelCase     :						Dict						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						List[Any]=False		):






         """simple docstring"""






         with torch.no_grad():
                _lowerCamelCase						:				Optional[int]    			= MaskFormerModel(config=__lowerCAmelCase		)
                model.to(__lowerCAmelCase		)
                model.eval()

                _lowerCamelCase						:				Tuple    			= model(pixel_values=__lowerCAmelCase						,					pixel_mask=__lowerCAmelCase		)
                _lowerCamelCase						:				List[str]    			= model(__lowerCAmelCase						,					output_hidden_states=__lowerCAmelCase		)
         # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
         # encoder and pixel decoder
         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape						,					(self.batch_size, self.num_queries, self.mask_feature_size)						,					)
         # 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(__lowerCAmelCase						,					__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= MaskFormerForInstanceSegmentation(config=__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()

         def comm_check_on_output(__lowerCAmelCase     :						Dict		):
                # 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						:				str    			= model(pixel_values=__lowerCAmelCase						,					pixel_mask=__lowerCAmelCase		)
                _lowerCamelCase						:				Optional[Any]    			= model(__lowerCAmelCase		)

                comm_check_on_output(__lowerCAmelCase		)

                _lowerCamelCase						:				List[str]    			= model(
                    pixel_values=__lowerCAmelCase						,					pixel_mask=__lowerCAmelCase						,					mask_labels=__lowerCAmelCase						,					class_labels=__lowerCAmelCase		)

         comm_check_on_output(__lowerCAmelCase		)

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






@require_torch
class        __snake_case       (	_lowercase  ,     _lowercase  ,     unittest.TestCase):
  snake_case__				:   Dict  			=						(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  snake_case__				:   Any  			=						(
      {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  snake_case__				:   List[str]  			=						False
  snake_case__				:   List[str]  			=						False
  snake_case__				:   Optional[int]  			=						False
  snake_case__				:   Dict  			=						False




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= MaskFormerModelTester(self		)
         _lowerCamelCase						:				Optional[Any]    			= ConfigTester(self						,					config_class=__lowerCAmelCase						,					has_text_modality=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         self.config_tester.run_common_tests()




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				Dict    			= self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase						,					**__lowerCAmelCase						,					output_hidden_states=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowerCAmelCase		)




  @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         pass




  @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         pass




  @unittest.skip(reason='''MaskFormer is not a generative model'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         pass




  @unittest.skip(reason='''MaskFormer does not use token embeddings'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         pass




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






         """simple docstring"""






         pass




  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				Any    			= self.model_tester.prepare_config_and_inputs_for_common()

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

                _lowerCamelCase						:				Optional[Any]    			= ['''pixel_values''']
                self.assertListEqual(arg_names[:1]						,					__lowerCAmelCase		)




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         for model_name in ["facebook/maskformer-swin-small-coco"]:
                _lowerCamelCase						:				Union[str, Any]    			= MaskFormerModel.from_pretrained(__lowerCAmelCase		)
                self.assertIsNotNone(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= (self.model_tester.min_size,) * 2
         _lowerCamelCase						:				Union[str, Any]    			= {
             '''pixel_values''': torch.randn((2, 3, *size)						,					device=__lowerCAmelCase		),
             '''mask_labels''': torch.randn((2, 1_0, *size)						,					device=__lowerCAmelCase		),
             '''class_labels''': torch.zeros(2						,					1_0						,					device=__lowerCAmelCase		).long(),
         }

         _lowerCamelCase						:				int    			= MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= model(**__lowerCAmelCase		)
         self.assertTrue(outputs.loss is not None		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				List[str]    			= self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskformer_model(__lowerCAmelCase						,					**__lowerCAmelCase						,					output_hidden_states=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				Union[str, Any]    			= self.model_tester.prepare_config_and_inputs_for_common()

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         if not self.model_tester.is_training:
                return
         # only MaskFormerForInstanceSegmentation has the loss
         _lowerCamelCase						:				Union[str, Any]    			= self.all_model_classes[1]
         _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase						:				int    			= self.model_tester.prepare_config_and_inputs()

         _lowerCamelCase						:				Any    			= model_class(__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.train()

         _lowerCamelCase						:				List[Any]    			= model(__lowerCAmelCase						,					mask_labels=__lowerCAmelCase						,					class_labels=__lowerCAmelCase		).loss
         loss.backward()




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self.all_model_classes[1]
         _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase						:				str    			= self.model_tester.prepare_config_and_inputs()
         _lowerCamelCase						:				List[str]    			= True
         _lowerCamelCase						:				Optional[int]    			= True

         _lowerCamelCase						:				Optional[Any]    			= model_class(__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.train()

         _lowerCamelCase						:				Optional[Any]    			= model(__lowerCAmelCase						,					mask_labels=__lowerCAmelCase						,					class_labels=__lowerCAmelCase		)

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

         _lowerCamelCase						:				Tuple    			= outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()
         # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
         _lowerCamelCase						:				List[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=__lowerCAmelCase		)

         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        snake_case_  (      ):


       '''simple docstring'''





       _lowerCamelCase						:				Union[str, Any]    			= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       return image







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




  @cached_property
  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         return (
             MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
             if is_vision_available()
             else None
         )




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''		).to(__lowerCAmelCase		)
         _lowerCamelCase						:				Any    			= self.default_image_processor
         _lowerCamelCase						:				List[Any]    			= prepare_img()
         _lowerCamelCase						:				Any    			= image_processor(__lowerCAmelCase						,					return_tensors='''pt'''		).to(__lowerCAmelCase		)
         _lowerCamelCase						:				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(__lowerCAmelCase						,					(1, 3, 8_0_0, 1_0_8_8)		)

         with torch.no_grad():
                _lowerCamelCase						:				int    			= model(**__lowerCAmelCase		)

         _lowerCamelCase						:				str    			= torch.tensor(
             [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]		).to(__lowerCAmelCase		)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]						,					__lowerCAmelCase						,					atol=__lowerCAmelCase		)		)

         _lowerCamelCase						:				Union[str, Any]    			= torch.tensor(
             [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]		).to(__lowerCAmelCase		)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]						,					__lowerCAmelCase						,					atol=__lowerCAmelCase		)		)

         _lowerCamelCase						:				Optional[int]    			= torch.tensor(
             [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]		).to(__lowerCAmelCase		)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]						,					__lowerCAmelCase						,					atol=__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= (
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
             .to(__lowerCAmelCase		)
             .eval()
         )
         _lowerCamelCase						:				int    			= self.default_image_processor
         _lowerCamelCase						:				str    			= prepare_img()
         _lowerCamelCase						:				int    			= image_processor(__lowerCAmelCase						,					return_tensors='''pt'''		).to(__lowerCAmelCase		)
         _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(__lowerCAmelCase						,					(1, 3, 8_0_0, 1_0_8_8)		)

         with torch.no_grad():
                _lowerCamelCase						:				Optional[int]    			= model(**__lowerCAmelCase		)
         # masks_queries_logits
         _lowerCamelCase						:				Optional[int]    			= outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape						,					(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,					)
         _lowerCamelCase						:				List[str]    			= [
             [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
             [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
             [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
         ]
         _lowerCamelCase						:				Any    			= torch.tensor(__lowerCAmelCase		).to(__lowerCAmelCase		)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,					__lowerCAmelCase						,					atol=__lowerCAmelCase		)		)
         # class_queries_logits
         _lowerCamelCase						:				List[Any]    			= outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape						,					(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
         _lowerCamelCase						:				str    			= torch.tensor(
             [
                 [1.6512E00, -5.2572E00, -3.3519E00],
                 [3.6169E-02, -5.9025E00, -2.9313E00],
                 [1.0766E-04, -7.7630E00, -5.1263E00],
             ]		).to(__lowerCAmelCase		)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,					__lowerCAmelCase						,					atol=__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= (
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''		)
             .to(__lowerCAmelCase		)
             .eval()
         )
         _lowerCamelCase						:				Tuple    			= self.default_image_processor
         _lowerCamelCase						:				Tuple    			= prepare_img()
         _lowerCamelCase						:				Optional[Any]    			= image_processor(__lowerCAmelCase						,					return_tensors='''pt'''		).to(__lowerCAmelCase		)
         _lowerCamelCase						:				List[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(__lowerCAmelCase						,					(1, 3, 8_0_0, 1_0_8_8)		)

         with torch.no_grad():
                _lowerCamelCase						:				Optional[Any]    			= model(**__lowerCAmelCase		)
         # masks_queries_logits
         _lowerCamelCase						:				List[Any]    			= outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape						,					(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						,					)
         _lowerCamelCase						:				int    			= [[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
         _lowerCamelCase						:				List[Any]    			= torch.tensor(__lowerCAmelCase		).to(__lowerCAmelCase		)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]						,					__lowerCAmelCase						,					atol=__lowerCAmelCase		)		)
         # class_queries_logits
         _lowerCamelCase						:				Dict    			= outputs.class_queries_logits
         self.assertEqual(
             class_queries_logits.shape						,					(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
         _lowerCamelCase						:				Any    			= torch.tensor(
             [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]		).to(__lowerCAmelCase		)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]						,					__lowerCAmelCase						,					atol=__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= (
             MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''		)
             .to(__lowerCAmelCase		)
             .eval()
         )
         _lowerCamelCase						:				int    			= self.default_image_processor

         _lowerCamelCase						:				List[str]    			= 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						:				Union[str, Any]    			= inputs['''pixel_values'''].to(__lowerCAmelCase		)
         _lowerCamelCase						:				Dict    			= [el.to(__lowerCAmelCase		) for el in inputs['''mask_labels''']]
         _lowerCamelCase						:				Optional[Any]    			= [el.to(__lowerCAmelCase		) for el in inputs['''class_labels''']]

         with torch.no_grad():
                _lowerCamelCase						:				Tuple    			= model(**__lowerCAmelCase		)

         self.assertTrue(outputs.loss is not None		)





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  Any   	=						logging.get_logger(__name__)


lowerCAmelCase							:  Tuple   	=						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

lowerCAmelCase							:  Optional[int]   	=						{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

lowerCAmelCase							:  Optional[Any]   	=						{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Any  		=       (
						    list(range(ord("!"    )				,	ord("~"    ) + 1    )    ) + list(range(ord("¡"    )				,	ord("¬"    ) + 1    )    ) + list(range(ord("®"    )				,	ord("ÿ"    ) + 1    )    )
						)
						SCREAMING_SNAKE_CASE_: Tuple  		=       bs[:]
						SCREAMING_SNAKE_CASE_: str  		=       0
						for b in range(2**8    ):
												if b not in bs:
																		bs.append(_UpperCAmelCase    )
																		cs.append(2**8 + n    )
																		n += 1
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       [chr(_UpperCAmelCase    ) for n in cs]
						return dict(zip(_UpperCAmelCase				,	_UpperCAmelCase    )    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: str  		=       set()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)    )
												SCREAMING_SNAKE_CASE_: Tuple  		=       char
						return pairs



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Any										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							Dict										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							str										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase   :							List[str]										=     ['''input_ids''', '''attention_mask''']



			def __init__( self  :     str   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]="replace"   ,						lowerCAmelCase__  :     Optional[Any]="<s>"   ,						lowerCAmelCase__  :     int="</s>"   ,						lowerCAmelCase__  :     Optional[Any]="</s>"   ,						lowerCAmelCase__  :     int="<s>"   ,						lowerCAmelCase__  :     Optional[Any]="<unk>"   ,						lowerCAmelCase__  :     List[Any]="<pad>"   ,						lowerCAmelCase__  :     Any="<mask>"   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						**lowerCAmelCase__  :     Tuple   ,						):
									SCREAMING_SNAKE_CASE_: int  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_: str  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_: int  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else unk_token
									SCREAMING_SNAKE_CASE_: Any  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__   ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) else mask_token

									super().__init__(
									    errors=lowerCAmelCase__   ,						bos_token=lowerCAmelCase__   ,						eos_token=lowerCAmelCase__   ,						unk_token=lowerCAmelCase__   ,						sep_token=lowerCAmelCase__   ,						cls_token=lowerCAmelCase__   ,						pad_token=lowerCAmelCase__   ,						mask_token=lowerCAmelCase__   ,						add_prefix_space=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

									with open(lowerCAmelCase__   ,						encoding="utf-8") as vocab_handle:
															SCREAMING_SNAKE_CASE_: Tuple  		=       json.load(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       {v: k for k, v in self.encoder.items()}
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       errors  # how to handle errors in decoding
									SCREAMING_SNAKE_CASE_: List[Any]  		=       bytes_to_unicode()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       {v: k for k, v in self.byte_encoder.items()}
									with open(lowerCAmelCase__   ,						encoding="utf-8") as merges_handle:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       merges_handle.read().split("\n")[1:-1]
									SCREAMING_SNAKE_CASE_: str  		=       [tuple(merge.split()) for merge in bpe_merges]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       dict(zip(lowerCAmelCase__   ,						range(len(lowerCAmelCase__))))
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									SCREAMING_SNAKE_CASE_: List[Any]  		=       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return len(self.encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return dict(self.encoder   ,						**self.added_tokens_encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									if token in self.cache:
															return self.cache[token]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       tuple(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_pairs(lowerCAmelCase__)

									if not pairs:
															return token

									while True:
															SCREAMING_SNAKE_CASE_: int  		=       min(lowerCAmelCase__   ,						key=lambda lowerCAmelCase__: self.bpe_ranks.get(lowerCAmelCase__   ,						float("inf")))
															if bigram not in self.bpe_ranks:
																					break
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       bigram
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       []
															SCREAMING_SNAKE_CASE_: List[Any]  		=       0
															while i < len(lowerCAmelCase__):
																					try:
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       word.index(lowerCAmelCase__   ,						lowerCAmelCase__)
																					except ValueError:
																											new_word.extend(word[i:])
																											break
																					else:
																											new_word.extend(word[i:j])
																											SCREAMING_SNAKE_CASE_: Tuple  		=       j

																					if word[i] == first and i < len(lowerCAmelCase__) - 1 and word[i + 1] == second:
																											new_word.append(first + second)
																											i += 2
																					else:
																											new_word.append(word[i])
																											i += 1
															SCREAMING_SNAKE_CASE_: str  		=       tuple(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: int  		=       new_word
															if len(lowerCAmelCase__) == 1:
																					break
															else:
																					SCREAMING_SNAKE_CASE_: Dict  		=       get_pairs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       " ".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       word
									return word



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
									for token in re.findall(self.pat   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: str  		=       "".join(
															    self.byte_encoder[b] for b in token.encode("utf-8"))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__).split(" "))
									return bpe_tokens



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return self.encoder.get(lowerCAmelCase__   ,						self.encoder.get(self.unk_token))



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Union[str, Any]):
									return self.decoder.get(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Any  		=       "".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"   ,						errors=self.errors)
									return text



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = None):
									if not os.path.isdir(lowerCAmelCase__):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_: Any  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_: Any  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

									with open(lowerCAmelCase__   ,						"w"   ,						encoding="utf-8") as f:
															f.write(json.dumps(self.encoder   ,						indent=2   ,						sort_keys=lowerCAmelCase__   ,						ensure_ascii=lowerCAmelCase__) + "\n")

									SCREAMING_SNAKE_CASE_: List[Any]  		=       0
									with open(lowerCAmelCase__   ,						"w"   ,						encoding="utf-8") as writer:
															writer.write("#version: 0.2\n")
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,						key=lambda lowerCAmelCase__: kv[1]):
																					if index != token_index:
																											logger.warning(
																											    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																											    " Please check that the tokenizer is not corrupted!")
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       token_index
																					writer.write(" ".join(lowerCAmelCase__) + "\n")
																					index += 1

									return vocab_file, merge_file



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [self.cls_token_id]
									SCREAMING_SNAKE_CASE_: Dict  		=       [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None   ,						lowerCAmelCase__  :     bool = False):
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__   ,						token_ids_a=lowerCAmelCase__   ,						already_has_special_tokens=lowerCAmelCase__)

									if token_ids_a is None:
															return [1] + ([0] * len(lowerCAmelCase__)) + [1]
									return [1] + ([0] * len(lowerCAmelCase__)) + [1, 1] + ([0] * len(lowerCAmelCase__)) + [1]



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: int  		=       [self.cls_token_id]

									if token_ids_a is None:
															return len(cls + token_ids_a + sep) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]




			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[str]=False   ,						**lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       kwargs.pop("add_prefix_space"   ,						self.add_prefix_space)
									if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__) > 0 and not text[0].isspace()):
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       " " + text
									return (text, kwargs)






