


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     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    Dict=True      ,   __lowerCamelCase							:    Optional[int]=False      ,   __lowerCamelCase							:    Optional[Any]=10      ,   __lowerCamelCase							:    str=3      ,   __lowerCamelCase							:    Optional[int]=32 * 4      ,   __lowerCamelCase							:    Optional[Any]=32 * 6      ,   __lowerCamelCase							:    List[Any]=4      ,   __lowerCamelCase							:    int=32      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						parent
             lowerCamelCase__	:       Union[str, Any]					=						batch_size
             lowerCamelCase__	:       int					=						is_training
             lowerCamelCase__	:       Tuple					=						use_auxiliary_loss
             lowerCamelCase__	:       Tuple					=						num_queries
             lowerCamelCase__	:       Tuple					=						num_channels
             lowerCamelCase__	:       List[Any]					=						min_size
             lowerCamelCase__	:       int					=						max_size
             lowerCamelCase__	:       int					=						num_labels
             lowerCamelCase__	:       Optional[int]					=						mask_feature_size





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


             '''simple docstring'''



             lowerCamelCase__	:       str					=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
                 UpperCamelCase_ )

             lowerCamelCase__	:       str					=						torch.ones([self.batch_size, self.min_size, self.max_size]      ,   device=UpperCamelCase_ )

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

             lowerCamelCase__	:       Tuple					=						self.get_config()
             return config, pixel_values, pixel_mask, mask_labels, class_labels





       def 		lowerCAmelCase  (     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=128      ,   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 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						self.prepare_config_and_inputs()
             lowerCamelCase__	:       Optional[Any]					=						{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
             return config, inputs_dict





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						output.encoder_hidden_states
             lowerCamelCase__	:       Union[str, Any]					=						output.pixel_decoder_hidden_states
             lowerCamelCase__	:       List[Any]					=						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_config.decoder_layers )





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Tuple=False ):


             '''simple docstring'''



             with torch.no_grad():
                   lowerCamelCase__	:       Dict					=						MaskFormerModel(config=UpperCamelCase_ )
                   model.to(UpperCamelCase_ )
                   model.eval()

                   lowerCamelCase__	:       List[str]					=						model(pixel_values=UpperCamelCase_      ,   pixel_mask=UpperCamelCase_ )
                   lowerCamelCase__	:       List[str]					=						model(UpperCamelCase_      ,   output_hidden_states=UpperCamelCase_ )
             # 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(UpperCamelCase_      ,   UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						MaskFormerForInstanceSegmentation(config=UpperCamelCase_ )
             model.to(UpperCamelCase_ )
             model.eval()

             def comm_check_on_output(__lowerCamelCase							:    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():
                   lowerCamelCase__	:       List[Any]					=						model(pixel_values=UpperCamelCase_      ,   pixel_mask=UpperCamelCase_ )
                   lowerCamelCase__	:       Any					=						model(UpperCamelCase_ )

                   comm_check_on_output(UpperCamelCase_ )

                   lowerCamelCase__	:       Any					=						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     _lowercase				(							__lowerCamelCase							,						__lowerCamelCase							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
       A__      		=    (
           {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
           if is_torch_available()
           else {}
       )

       A__      		=    False
       A__      		=    False
       A__      		=    False
       A__      		=    False





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						MaskFormerModelTester(self )
             lowerCamelCase__	:       Dict					=						ConfigTester(self      ,   config_class=UpperCamelCase_      ,   has_text_modality=UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       List[Any]					=						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskformer_model(UpperCamelCase_      ,   **UpperCamelCase_      ,   output_hidden_states=UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*UpperCamelCase_ )





       @unittest.skip(reason="MaskFormer does not use inputs_embeds" )
       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="MaskFormer does not have a get_input_embeddings method" )
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="MaskFormer is not a generative model" )
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="MaskFormer does not use token embeddings" )
       def 		lowerCAmelCase  (     self							:    List[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 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             pass





       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       str					=						self.model_tester.prepare_config_and_inputs_for_common()

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

                   lowerCamelCase__	:       Any					=						["pixel_values"]
                   self.assertListEqual(arg_names[:1]      ,   UpperCamelCase_ )





       @slow
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             for model_name in ["facebook/maskformer-swin-small-coco"]:
                   lowerCamelCase__	:       str					=						MaskFormerModel.from_pretrained(UpperCamelCase_ )
                   self.assertIsNotNone(UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						(self.model_tester.min_size,) * 2
             lowerCamelCase__	:       Optional[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(),
             }

             lowerCamelCase__	:       Any					=						MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(UpperCamelCase_ )
             lowerCamelCase__	:       Dict					=						model(**UpperCamelCase_ )
             self.assertTrue(outputs.loss is not None )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__	:       List[Any]					=						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskformer_model(UpperCamelCase_      ,   **UpperCamelCase_      ,   output_hidden_states=UpperCamelCase_ )





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



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

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





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


             '''simple docstring'''



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

             lowerCamelCase__	:       List[Any]					=						model_class(UpperCamelCase_ )
             model.to(UpperCamelCase_ )
             model.train()

             lowerCamelCase__	:       Dict					=						model(UpperCamelCase_      ,   mask_labels=UpperCamelCase_      ,   class_labels=UpperCamelCase_ ).loss
             loss.backward()





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



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

             lowerCamelCase__	:       List[str]					=						model_class(UpperCamelCase_ )
             model.to(UpperCamelCase_ )
             model.train()

             lowerCamelCase__	:       List[Any]					=						model(UpperCamelCase_      ,   mask_labels=UpperCamelCase_      ,   class_labels=UpperCamelCase_ )

             lowerCamelCase__	:       Optional[Any]					=						outputs.encoder_hidden_states[0]
             encoder_hidden_states.retain_grad()

             lowerCamelCase__	:       Optional[int]					=						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__	:       Optional[int]					=						outputs.transformer_decoder_hidden_states[0]
             transformer_decoder_hidden_states.retain_grad()

             lowerCamelCase__	:       List[str]					=						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 )



A   :					List[str]	=      1E-4

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       int					=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
      return image







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


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco" ).to(UpperCamelCase_ )
             lowerCamelCase__	:       Any					=						self.default_image_processor
             lowerCamelCase__	:       List[str]					=						prepare_img()
             lowerCamelCase__	:       str					=						image_processor(UpperCamelCase_      ,   return_tensors="pt" ).to(UpperCamelCase_ )
             lowerCamelCase__	:       List[str]					=						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, 800, 1088) )

             with torch.no_grad():
                   lowerCamelCase__	:       Optional[int]					=						model(**UpperCamelCase_ )

             lowerCamelCase__	:       Any					=						torch.tensor(
                 [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(UpperCamelCase_ )
             self.assertTrue(
                 torch.allclose(
                     outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,   UpperCamelCase_      ,   atol=UpperCamelCase_ ) )

             lowerCamelCase__	:       str					=						torch.tensor(
                 [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(UpperCamelCase_ )
             self.assertTrue(
                 torch.allclose(
                     outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,   UpperCamelCase_      ,   atol=UpperCamelCase_ ) )

             lowerCamelCase__	:       Tuple					=						torch.tensor(
                 [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(UpperCamelCase_ )
             self.assertTrue(
                 torch.allclose(
                     outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,   UpperCamelCase_      ,   atol=UpperCamelCase_ ) )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						(
                 MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
                 .to(UpperCamelCase_ )
                 .eval()
             )
             lowerCamelCase__	:       str					=						self.default_image_processor
             lowerCamelCase__	:       List[Any]					=						prepare_img()
             lowerCamelCase__	:       Optional[int]					=						image_processor(UpperCamelCase_      ,   return_tensors="pt" ).to(UpperCamelCase_ )
             lowerCamelCase__	:       List[str]					=						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, 800, 1088) )

             with torch.no_grad():
                   lowerCamelCase__	:       Optional[int]					=						model(**UpperCamelCase_ )
             # masks_queries_logits
             lowerCamelCase__	:       Union[str, 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__	:       Optional[int]					=						[
                 [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
                 [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
                 [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
             ]
             lowerCamelCase__	:       Optional[int]					=						torch.tensor(UpperCamelCase_ ).to(UpperCamelCase_ )
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,   UpperCamelCase_      ,   atol=UpperCamelCase_ ) )
             # class_queries_logits
             lowerCamelCase__	:       Tuple					=						outputs.class_queries_logits
             self.assertEqual(
                 class_queries_logits.shape      ,   (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
             lowerCamelCase__	:       int					=						torch.tensor(
                 [
                     [1.6_5_1_2E0_0, -5.2_5_7_2E0_0, -3.3_5_1_9E0_0],
                     [3.6_1_6_9E-0_2, -5.9_0_2_5E0_0, -2.9_3_1_3E0_0],
                     [1.0_7_6_6E-0_4, -7.7_6_3_0E0_0, -5.1_2_6_3E0_0],
                 ] ).to(UpperCamelCase_ )
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,   UpperCamelCase_      ,   atol=UpperCamelCase_ ) )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						(
                 MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff" )
                 .to(UpperCamelCase_ )
                 .eval()
             )
             lowerCamelCase__	:       Union[str, Any]					=						self.default_image_processor
             lowerCamelCase__	:       List[Any]					=						prepare_img()
             lowerCamelCase__	:       List[str]					=						image_processor(UpperCamelCase_      ,   return_tensors="pt" ).to(UpperCamelCase_ )
             lowerCamelCase__	:       Tuple					=						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, 800, 1088) )

             with torch.no_grad():
                   lowerCamelCase__	:       Optional[Any]					=						model(**UpperCamelCase_ )
             # masks_queries_logits
             lowerCamelCase__	:       Union[str, 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__	:       List[Any]					=						[[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
             lowerCamelCase__	:       Union[str, Any]					=						torch.tensor(UpperCamelCase_ ).to(UpperCamelCase_ )
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,   UpperCamelCase_      ,   atol=UpperCamelCase_ ) )
             # 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__	:       Optional[Any]					=						torch.tensor(
                 [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(UpperCamelCase_ )
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,   UpperCamelCase_      ,   atol=UpperCamelCase_ ) )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						(
                 MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco" )
                 .to(UpperCamelCase_ )
                 .eval()
             )
             lowerCamelCase__	:       Optional[int]					=						self.default_image_processor

             lowerCamelCase__	:       Optional[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"      ,   )

             lowerCamelCase__	:       List[str]					=						inputs["pixel_values"].to(UpperCamelCase_ )
             lowerCamelCase__	:       List[Any]					=						[el.to(UpperCamelCase_ ) for el in inputs["mask_labels"]]
             lowerCamelCase__	:       int					=						[el.to(UpperCamelCase_ ) for el in inputs["class_labels"]]

             with torch.no_grad():
                   lowerCamelCase__	:       Dict					=						model(**UpperCamelCase_ )

             self.assertTrue(outputs.loss is not None )






import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
    print("Googling.....")
    A   :					str	=      "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
    A   :					Optional[int]	=      requests.get(url, headers={"UserAgent": UserAgent().random})
    # res.raise_for_status()
    with open("project1a.html", "wb") as out_file:  # only for knowing the class
        for data in res.iter_content(10000):
            out_file.write(data)
    A   :					int	=      BeautifulSoup(res.text, "html.parser")
    A   :					Any	=      list(soup.select(".eZt8xd"))[:5]

    print(len(links))
    for link in links:
        if link.text == "Maps":
            webbrowser.open(link.get("href"))
        else:
            webbrowser.open(f'https://google.com{link.get("href")}')


