
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 __snake_case		:
    def __init__(  self		,snake_case		,snake_case=2		,snake_case=True		,snake_case=False		,snake_case=10		,snake_case=3		,snake_case=32 * 8		,snake_case=32 * 8		,snake_case=4		,snake_case=64		,):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				parent
           lowercase							:					Optional[Any]	     =				batch_size
           lowercase							:					int	     =				is_training
           lowercase							:					Any	     =				use_auxiliary_loss
           lowercase							:					Any	     =				num_queries
           lowercase							:					str	     =				num_channels
           lowercase							:					str	     =				min_size
           lowercase							:					Union[str, Any]	     =				max_size
           lowercase							:					Optional[Any]	     =				num_labels
           lowercase							:					Tuple	     =				hidden_dim
           lowercase							:					Union[str, Any]	     =				hidden_dim
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]				).to(
               snake_case				)

           lowercase							:					Dict	     =				torch.ones([self.batch_size, self.min_size, self.max_size]		,device=snake_case				)

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

           lowercase							:					str	     =				self.get_config()
           return config, pixel_values, pixel_mask, mask_labels, class_labels
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				MaskaFormerConfig(
               hidden_size=self.hidden_dim		,)
           lowercase							:					Union[str, Any]	     =				self.num_queries
           lowercase							:					List[Any]	     =				self.num_labels

           lowercase							:					Dict	     =				[1, 1, 1, 1]
           lowercase							:					List[Any]	     =				self.num_channels

           lowercase							:					List[Any]	     =				64
           lowercase							:					Dict	     =				128
           lowercase							:					str	     =				self.hidden_dim
           lowercase							:					Union[str, Any]	     =				self.hidden_dim
           lowercase							:					List[str]	     =				self.hidden_dim
           return config
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.prepare_config_and_inputs()
           lowercase							:					Optional[Any]	     =				{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
           return config, inputs_dict
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				output.encoder_hidden_states
           lowercase							:					List[str]	     =				output.pixel_decoder_hidden_states
           lowercase							:					str	     =				output.transformer_decoder_hidden_states

           self.parent.assertTrue(len(snake_case				)		,len(config.backbone_config.depths				)				)
           self.parent.assertTrue(len(snake_case				)		,len(config.backbone_config.depths				)				)
           self.parent.assertTrue(len(snake_case				)		,config.decoder_layers				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case=False				):



           '''simple docstring'''







           with torch.no_grad():
                  lowercase							:					Dict	     =				MaskaFormerModel(config=snake_case				)
                  model.to(snake_case				)
                  model.eval()

                  lowercase							:					int	     =				model(pixel_values=snake_case		,pixel_mask=snake_case				)
                  lowercase							:					List[Any]	     =				model(snake_case		,output_hidden_states=snake_case				)

           self.parent.assertEqual(
               output.transformer_decoder_last_hidden_state.shape		,(self.batch_size, self.num_queries, self.hidden_dim)		,)
           # let's ensure the other two hidden state exists
           self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None				)
           self.parent.assertTrue(output.encoder_last_hidden_state is not None				)

           if output_hidden_states:
                  self.check_output_hidden_state(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				MaskaFormerForUniversalSegmentation(config=snake_case				)
           model.to(snake_case				)
           model.eval()

           def comm_check_on_output(snake_case				):
                  # let's still check that all the required stuff is there
                  self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None				)
                  self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None				)
                  self.parent.assertTrue(result.encoder_last_hidden_state is not None				)
                  # okay, now we need to check the logits shape
                  # due to the encoder compression, masks have a //4 spatial size
                  self.parent.assertEqual(
                      result.masks_queries_logits.shape		,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)		,)
                  # + 1 for null class
                  self.parent.assertEqual(
                      result.class_queries_logits.shape		,(self.batch_size, self.num_queries, self.num_labels + 1)				)

           with torch.no_grad():
                  lowercase							:					int	     =				model(pixel_values=snake_case		,pixel_mask=snake_case				)
                  lowercase							:					Optional[Any]	     =				model(snake_case				)

                  comm_check_on_output(snake_case				)

                  lowercase							:					Tuple	     =				model(
                      pixel_values=snake_case		,pixel_mask=snake_case		,mask_labels=snake_case		,class_labels=snake_case				)

           comm_check_on_output(snake_case				)

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






@require_torch
class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Union[str, Any]=    (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
    _a		:						Optional[Any]=    {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

    _a		:						Optional[Any]=    False
    _a		:						List[Any]=    False
    _a		:						Union[str, Any]=    False
    _a		:						Tuple=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				MaskaFormerModelTester(self				)
           lowercase							:					int	     =				ConfigTester(self		,config_class=snake_case		,has_text_modality=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(snake_case		,**snake_case		,output_hidden_states=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*snake_case				)
    @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           pass
    @unittest.skip(reason="""Mask2Former is not a generative model"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""Mask2Former does not use token embeddings"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''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				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowercase							:					Tuple	     =				["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,snake_case				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
                  lowercase							:					Optional[int]	     =				MaskaFormerModel.from_pretrained(snake_case				)
                  self.assertIsNotNone(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				(self.model_tester.min_size,) * 2
           lowercase							:					Optional[Any]	     =				{
               """pixel_values""": torch.randn((2, 3, *size)		,device=snake_case				),
               """mask_labels""": torch.randn((2, 10, *size)		,device=snake_case				),
               """class_labels""": torch.zeros(2		,10		,device=snake_case				).long(),
           }
           lowercase							:					Union[str, Any]	     =				self.model_tester.get_config()

           lowercase							:					str	     =				MaskaFormerForUniversalSegmentation(snake_case				).to(snake_case				)
           lowercase							:					List[Any]	     =				model(**snake_case				)
           self.assertTrue(outputs.loss is not None				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(snake_case		,**snake_case		,output_hidden_states=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.model_tester.prepare_config_and_inputs_for_common()

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



           '''simple docstring'''







           if not self.model_tester.is_training:
                  return

           lowercase							:					Optional[int]	     =				self.all_model_classes[1]
           lowercase							:					Dict	     =				self.model_tester.prepare_config_and_inputs()

           lowercase							:					str	     =				model_class(snake_case				)
           model.to(snake_case				)
           model.train()

           lowercase							:					int	     =				model(snake_case		,mask_labels=snake_case		,class_labels=snake_case				).loss
           loss.backward()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.all_model_classes[1]
           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs()
           lowercase							:					str	     =				True
           lowercase							:					Optional[int]	     =				True

           lowercase							:					List[Any]	     =				model_class(snake_case				).to(snake_case				)
           model.train()

           lowercase							:					Optional[Any]	     =				model(snake_case		,mask_labels=snake_case		,class_labels=snake_case				)

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

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

           lowercase							:					Optional[Any]	     =				outputs.transformer_decoder_hidden_states[0]
           transformer_decoder_hidden_states.retain_grad()

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

           outputs.loss.backward(retain_graph=snake_case				)

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






lowercase  : Any						   =							1e-4







def 						_snake_case(  ):
       lowercase							:					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				):



           '''simple docstring'''







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



           '''simple docstring'''







           return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints				) if is_vision_available() else None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				MaskaFormerModel.from_pretrained(self.model_checkpoints				).to(snake_case				)
           lowercase							:					Optional[Any]	     =				self.default_image_processor
           lowercase							:					Any	     =				prepare_img()
           lowercase							:					Optional[Any]	     =				image_processor(snake_case		,return_tensors="""pt"""				).to(snake_case				)
           lowercase							:					Dict	     =				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(snake_case		,(1, 3, 384, 384)				)

           with torch.no_grad():
                  lowercase							:					Tuple	     =				model(**snake_case				)

           lowercase							:					Optional[int]	     =				torch.tensor(
               [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]				).to(snake_case				)
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3]		,snake_case		,atol=snake_case				)				)

           lowercase							:					int	     =				torch.tensor(
               [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]				).to(snake_case				)
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,snake_case		,atol=snake_case				)				)

           lowercase							:					str	     =				torch.tensor(
               [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]				).to(snake_case				)
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,snake_case		,atol=snake_case				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(snake_case				).eval()
           lowercase							:					Dict	     =				self.default_image_processor
           lowercase							:					str	     =				prepare_img()
           lowercase							:					Optional[Any]	     =				image_processor(snake_case		,return_tensors="""pt"""				).to(snake_case				)
           lowercase							:					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(snake_case		,(1, 3, 384, 384)				)

           with torch.no_grad():
                  lowercase							:					Union[str, Any]	     =				model(**snake_case				)
           # masks_queries_logits
           lowercase							:					List[Any]	     =				outputs.masks_queries_logits
           self.assertEqual(
               masks_queries_logits.shape		,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)				)
           lowercase							:					List[str]	     =				[
               [-8.7_839, -9.0_056, -8.8_121],
               [-7.4_104, -7.0_313, -6.5_401],
               [-6.6_105, -6.3_427, -6.4_675],
           ]
           lowercase							:					int	     =				torch.tensor(snake_case				).to(snake_case				)
           self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,snake_case		,atol=snake_case				)				)
           # class_queries_logits
           lowercase							:					Any	     =				outputs.class_queries_logits
           self.assertEqual(class_queries_logits.shape		,(1, model.config.num_queries, model.config.num_labels + 1)				)
           lowercase							:					List[str]	     =				torch.tensor(
               [
                   [1.8_324, -8.0_835, -4.1_922],
                   [0.8_450, -9.0_050, -3.6_053],
                   [0.3_045, -7.7_293, -3.0_275],
               ]				).to(snake_case				)
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,snake_case		,atol=snake_case				)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints				).to(snake_case				).eval()
           lowercase							:					str	     =				self.default_image_processor

           lowercase							:					List[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"""		,)

           lowercase							:					List[str]	     =				inputs["""pixel_values"""].to(snake_case				)
           lowercase							:					str	     =				[el.to(snake_case				) for el in inputs["""mask_labels"""]]
           lowercase							:					List[Any]	     =				[el.to(snake_case				) for el in inputs["""class_labels"""]]

           with torch.no_grad():
                  lowercase							:					List[Any]	     =				model(**snake_case				)

           self.assertTrue(outputs.loss is not None				)


from ...processing_utils import ProcessorMixin

class __snake_case		(				lowerCAmelCase					):
    _a		:						Union[str, Any]=    "WhisperFeatureExtractor"
    _a		:						int=    "WhisperTokenizer"
    def __init__(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case		,snake_case				)
           lowercase							:					Optional[int]	     =				self.feature_extractor
           lowercase							:					Tuple	     =				False
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=None		,snake_case=None		,snake_case=True				):



           '''simple docstring'''







           return self.tokenizer.get_decoder_prompt_ids(task=snake_case		,language=snake_case		,no_timestamps=snake_case				)
    def __call__(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor(*snake_case		,**snake_case				)

           lowercase							:					Optional[Any]	     =				kwargs.pop("""audio"""		,snake_case				)
           lowercase							:					str	     =				kwargs.pop("""sampling_rate"""		,snake_case				)
           lowercase							:					Dict	     =				kwargs.pop("""text"""		,snake_case				)
           if len(snake_case				) > 0:
                  lowercase							:					List[Any]	     =				args[0]
                  lowercase							:					Tuple	     =				args[1:]

           if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

           if audio is not None:
                  lowercase							:					Any	     =				self.feature_extractor(snake_case		,*snake_case		,sampling_rate=snake_case		,**snake_case				)
           if text is not None:
                  lowercase							:					str	     =				self.tokenizer(snake_case		,**snake_case				)

           if text is None:
                  return inputs

           elif audio is None:
                  return encodings
           else:
                  lowercase							:					List[Any]	     =				encodings["""input_ids"""]
                  return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case="np"				):



           '''simple docstring'''







           return self.tokenizer.get_prompt_ids(snake_case		,return_tensors=snake_case				)
