





import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        MODEL_MAPPING,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
    )
    from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    import PIL
    from PIL import Image

    from transformers import BeitImageProcessor

class   lowerCAmelCase				:
 def __init__( self    : Optional[int]     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Tuple=100     ,	UpperCAmelCase    : Any=13     ,	UpperCAmelCase    : Optional[Any]=30     ,	UpperCAmelCase    : Tuple=2     ,	UpperCAmelCase    : Optional[int]=3     ,	UpperCAmelCase    : Any=True     ,	UpperCAmelCase    : List[Any]=True     ,	UpperCAmelCase    : List[Any]=32     ,	UpperCAmelCase    : Optional[Any]=4     ,	UpperCAmelCase    : Union[str, Any]=4     ,	UpperCAmelCase    : Tuple=37     ,	UpperCAmelCase    : List[str]="gelu"     ,	UpperCAmelCase    : Union[str, Any]=0.1     ,	UpperCAmelCase    : int=0.1     ,	UpperCAmelCase    : Optional[int]=10     ,	UpperCAmelCase    : Tuple=0.0_2     ,	UpperCAmelCase    : List[str]=3     ,	UpperCAmelCase    : List[str]=None     ,	UpperCAmelCase    : Union[str, Any]=[0, 1, 2, 3]     ,	)     ->      List[str]:
     lowerCamelCase__       : Tuple   					=						parent
     lowerCamelCase__       : Tuple   					=						100
     lowerCamelCase__       : Dict   					=						batch_size
     lowerCamelCase__       : Tuple   					=						image_size
     lowerCamelCase__       : Any   					=						patch_size
     lowerCamelCase__       : int   					=						num_channels
     lowerCamelCase__       : Dict   					=						is_training
     lowerCamelCase__       : List[Any]   					=						use_labels
     lowerCamelCase__       : List[str]   					=						hidden_size
     lowerCamelCase__       : Any   					=						num_hidden_layers
     lowerCamelCase__       : Dict   					=						num_attention_heads
     lowerCamelCase__       : int   					=						intermediate_size
     lowerCamelCase__       : Optional[Any]   					=						hidden_act
     lowerCamelCase__       : Union[str, Any]   					=						hidden_dropout_prob
     lowerCamelCase__       : Dict   					=						attention_probs_dropout_prob
     lowerCamelCase__       : List[Any]   					=						type_sequence_label_size
     lowerCamelCase__       : Optional[int]   					=						initializer_range
     lowerCamelCase__       : Any   					=						scope
     lowerCamelCase__       : Tuple   					=						out_indices
     lowerCamelCase__       : Tuple   					=						num_labels

     # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     lowerCamelCase__       : List[Any]   					=						(image_size // patch_size) ** 2
     lowerCamelCase__       : Optional[Any]   					=						num_patches + 1
 def 	A_      ( self    : List[Any]      )     ->      Any:
     lowerCamelCase__       : int   					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

     lowerCamelCase__       : List[Any]   					=						None
     lowerCamelCase__       : Tuple   					=						None
     if self.use_labels:
         lowerCamelCase__       : Tuple   					=						ids_tensor([self.batch_size]     ,	self.type_sequence_label_size      )
         lowerCamelCase__       : Union[str, Any]   					=						ids_tensor([self.batch_size, self.image_size, self.image_size]     ,	self.num_labels      )

     lowerCamelCase__       : Dict   					=						self.get_config()

     return config, pixel_values, labels, pixel_labels
 def 	A_      ( self    : Dict      )     ->      List[Any]:
     return BeitConfig(
         vocab_size=self.vocab_size     ,	image_size=self.image_size     ,	patch_size=self.patch_size     ,	num_channels=self.num_channels     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	is_decoder=UpperCAmelCase     ,	initializer_range=self.initializer_range     ,	out_indices=self.out_indices     ,	)
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : List[Any]      )     ->      Union[str, Any]:
     lowerCamelCase__       : Union[str, Any]   					=						BeitModel(config=UpperCAmelCase      )
     model.to(UpperCAmelCase      )
     model.eval()
     lowerCamelCase__       : Any   					=						model(UpperCAmelCase      )
     self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)      )
 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : str     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Optional[Any]      )     ->      Any:
     lowerCamelCase__       : List[str]   					=						BeitForMaskedImageModeling(config=UpperCAmelCase      )
     model.to(UpperCAmelCase      )
     model.eval()
     lowerCamelCase__       : Any   					=						model(UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length - 1, self.vocab_size)      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : Optional[int]     ,	UpperCAmelCase    : Union[str, Any]     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Tuple      )     ->      List[str]:
     lowerCamelCase__       : Dict   					=						self.type_sequence_label_size
     lowerCamelCase__       : Dict   					=						BeitForImageClassification(UpperCAmelCase      )
     model.to(UpperCAmelCase      )
     model.eval()
     lowerCamelCase__       : Any   					=						model(UpperCAmelCase     ,	labels=UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

     # test greyscale images
     lowerCamelCase__       : List[Any]   					=						1
     lowerCamelCase__       : Dict   					=						BeitForImageClassification(UpperCAmelCase      )
     model.to(UpperCAmelCase      )
     model.eval()

     lowerCamelCase__       : str   					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
     lowerCamelCase__       : Tuple   					=						model(UpperCAmelCase     ,	labels=UpperCAmelCase      )
     self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )
 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : Optional[Any]      )     ->      Union[str, Any]:
     lowerCamelCase__       : List[str]   					=						self.num_labels
     lowerCamelCase__       : Union[str, Any]   					=						BeitForSemanticSegmentation(UpperCAmelCase      )
     model.to(UpperCAmelCase      )
     model.eval()
     lowerCamelCase__       : Tuple   					=						model(UpperCAmelCase      )
     self.parent.assertEqual(
         result.logits.shape     ,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
     lowerCamelCase__       : Dict   					=						model(UpperCAmelCase     ,	labels=UpperCAmelCase      )
     self.parent.assertEqual(
         result.logits.shape     ,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )







 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						self.prepare_config_and_inputs()
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : int   					=						config_and_inputs
     lowerCamelCase__       : Optional[int]   					=						{'pixel_values': pixel_values}
     return config, inputs_dict


@require_torch
class   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						(
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__						=						(
     {
         """feature-extraction""": BeitModel,
         """image-classification""": BeitForImageClassification,
         """image-segmentation""": BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase__						=						False
 UpperCAmelCase__						=						False
 UpperCAmelCase__						=						False
 def 	A_      ( self    : int      )     ->      Union[str, Any]:
     lowerCamelCase__       : Tuple   					=						BeitModelTester(self      )
     lowerCamelCase__       : Dict   					=						ConfigTester(self     ,	config_class=UpperCAmelCase     ,	has_text_modality=UpperCAmelCase     ,	hidden_size=37      )
 def 	A_      ( self    : Tuple      )     ->      int:
     self.config_tester.run_common_tests()
 @unittest.skip(reason='BEiT does not use inputs_embeds'      )
 def 	A_      ( self    : Tuple      )     ->      int:
     pass
 @require_torch_multi_gpu
 @unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'      )
 def 	A_      ( self    : Tuple      )     ->      Optional[Any]:
     pass
 def 	A_      ( self    : Dict      )     ->      str:
     lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__       : str   					=						model_class(UpperCAmelCase      )
         self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)      )
         lowerCamelCase__       : Tuple   					=						model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase     ,	nn.Linear      )      )
 def 	A_      ( self    : List[str]      )     ->      int:
     lowerCamelCase__					,				lowerCamelCase__       : Any   					=						self.model_tester.prepare_config_and_inputs_for_common()

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

         lowerCamelCase__       : Optional[int]   					=						['pixel_values']
         self.assertListEqual(arg_names[:1]     ,	UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[int]:
     lowerCamelCase__       : Union[str, Any]   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase      )
 def 	A_      ( self    : Dict      )     ->      Any:
     lowerCamelCase__       : Optional[Any]   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[Any]:
     lowerCamelCase__       : str   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : int   					=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase      )
 def 	A_      ( self    : Dict      )     ->      Optional[int]:
     if not self.model_tester.is_training:
         return

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

     for model_class in self.all_model_classes:
         # we don't test BeitForMaskedImageModeling
         if model_class in [*get_values(UpperCAmelCase      ), BeitForMaskedImageModeling]:
             continue

         lowerCamelCase__       : List[str]   					=						model_class(UpperCAmelCase      )
         model.to(UpperCAmelCase      )
         model.train()
         lowerCamelCase__       : Tuple   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase     ,	return_labels=UpperCAmelCase      )
         lowerCamelCase__       : Optional[int]   					=						model(**UpperCAmelCase      ).loss
         loss.backward()
 def 	A_      ( self    : Optional[int]      )     ->      Any:
     lowerCamelCase__					,				lowerCamelCase__       : Any   					=						self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
         return

     lowerCamelCase__       : Optional[int]   					=						False
     lowerCamelCase__       : Dict   					=						True

     for model_class in self.all_model_classes:
         # we don't test BeitForMaskedImageModeling
         if (
             model_class in [*get_values(UpperCAmelCase      ), BeitForMaskedImageModeling]
             or not model_class.supports_gradient_checkpointing
         ):
             continue

         lowerCamelCase__       : Union[str, Any]   					=						model_class(UpperCAmelCase      )
         model.gradient_checkpointing_enable()
         model.to(UpperCAmelCase      )
         model.train()
         lowerCamelCase__       : Union[str, Any]   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase     ,	return_labels=UpperCAmelCase      )
         lowerCamelCase__       : Dict   					=						model(**UpperCAmelCase      ).loss
         loss.backward()
 def 	A_      ( self    : Optional[int]      )     ->      List[str]:
     lowerCamelCase__					,				lowerCamelCase__       : List[Any]   					=						self.model_tester.prepare_config_and_inputs_for_common()

     lowerCamelCase__       : List[Any]   					=						_config_zero_init(UpperCAmelCase      )
     for model_class in self.all_model_classes:
         lowerCamelCase__       : Union[str, Any]   					=						model_class(config=UpperCAmelCase      )
         for name, param in model.named_parameters():
             # we skip lambda parameters as these require special initial values
             # determined by config.layer_scale_init_value
             if "lambda" in name:
                 continue
             if param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1e9).round() / 1e9).item()     ,	[0.0, 1.0]     ,	msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,	)







 @slow
 def 	A_      ( self    : List[str]      )     ->      Dict:
     for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__       : Dict   					=						BeitModel.from_pretrained(UpperCAmelCase      )
         self.assertIsNotNone(UpperCAmelCase      )


def 			SCREAMING_SNAKE_CASE (			)     ->	Optional[int]:
    lowerCamelCase__       : Any   					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image


@require_torch
@require_vision
class   lowerCAmelCase				(   unittest.TestCase     ):
 @cached_property
 def 	A_      ( self    : Optional[int]      )     ->      Dict:
     return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'      ) if is_vision_available() else None
 @slow
 def 	A_      ( self    : List[str]      )     ->      int:
     lowerCamelCase__       : Union[str, Any]   					=						BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'      ).to(UpperCAmelCase      )

     lowerCamelCase__       : Tuple   					=						self.default_image_processor
     lowerCamelCase__       : Optional[Any]   					=						prepare_img()
     lowerCamelCase__       : Optional[int]   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='pt'      ).pixel_values.to(UpperCAmelCase      )

     # prepare bool_masked_pos
     lowerCamelCase__       : Tuple   					=						torch.ones((1, 196)     ,	dtype=torch.bool      ).to(UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         lowerCamelCase__       : Optional[Any]   					=						model(pixel_values=UpperCAmelCase     ,	bool_masked_pos=UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						outputs.logits

     # verify the logits
     lowerCamelCase__       : Any   					=						torch.Size((1, 196, 8192)      )
     self.assertEqual(logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						torch.tensor(
         [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]      ).to(UpperCAmelCase      )

     self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,	UpperCAmelCase     ,	atol=1e-2      )      )
 @slow
 def 	A_      ( self    : Optional[Any]      )     ->      Any:
     lowerCamelCase__       : Dict   					=						BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'      ).to(UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						self.default_image_processor
     lowerCamelCase__       : List[str]   					=						prepare_img()
     lowerCamelCase__       : int   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='pt'      ).to(UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         lowerCamelCase__       : Tuple   					=						model(**UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						outputs.logits

     # verify the logits
     lowerCamelCase__       : Optional[int]   					=						torch.Size((1, 1000)      )
     self.assertEqual(logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : int   					=						torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]      ).to(UpperCAmelCase      )

     self.assertTrue(torch.allclose(logits[0, :3]     ,	UpperCAmelCase     ,	atol=1e-4      )      )

     lowerCamelCase__       : Dict   					=						281
     self.assertEqual(logits.argmax(-1      ).item()     ,	UpperCAmelCase      )
 @slow
 def 	A_      ( self    : Tuple      )     ->      Any:
     lowerCamelCase__       : Dict   					=						BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'      ).to(
         UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						self.default_image_processor
     lowerCamelCase__       : Dict   					=						prepare_img()
     lowerCamelCase__       : Any   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='pt'      ).to(UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         lowerCamelCase__       : str   					=						model(**UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						outputs.logits

     # verify the logits
     lowerCamelCase__       : Union[str, Any]   					=						torch.Size((1, 21841)      )
     self.assertEqual(logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : List[Any]   					=						torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]      ).to(UpperCAmelCase      )

     self.assertTrue(torch.allclose(logits[0, :3]     ,	UpperCAmelCase     ,	atol=1e-4      )      )

     lowerCamelCase__       : List[str]   					=						2396
     self.assertEqual(logits.argmax(-1      ).item()     ,	UpperCAmelCase      )
 @slow
 def 	A_      ( self    : List[Any]      )     ->      str:
     lowerCamelCase__       : Any   					=						BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'      )
     lowerCamelCase__       : List[Any]   					=						model.to(UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						BeitImageProcessor(do_resize=UpperCAmelCase     ,	size=640     ,	do_center_crop=UpperCAmelCase      )

     lowerCamelCase__       : List[Any]   					=						load_dataset('hf-internal-testing/fixtures_ade20k'     ,	split='test'      )
     lowerCamelCase__       : List[str]   					=						Image.open(ds[0]['file']      )
     lowerCamelCase__       : Optional[Any]   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='pt'      ).to(UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         lowerCamelCase__       : int   					=						model(**UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						outputs.logits

     # verify the logits
     lowerCamelCase__       : Optional[int]   					=						torch.Size((1, 150, 160, 160)      )
     self.assertEqual(logits.shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						version.parse(PIL.__version__      ) < version.parse('9.0.0'      )

     if is_pillow_less_than_a:
         lowerCamelCase__       : Optional[int]   					=						torch.tensor(
             [
                 [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                 [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                 [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
             ]     ,	device=UpperCAmelCase     ,	)
     else:
         lowerCamelCase__       : Tuple   					=						torch.tensor(
             [
                 [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
                 [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
                 [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
             ]     ,	device=UpperCAmelCase     ,	)

     self.assertTrue(torch.allclose(logits[0, :3, :3, :3]     ,	UpperCAmelCase     ,	atol=1e-4      )      )







 @slow
 def 	A_      ( self    : Dict      )     ->      Optional[int]:
     lowerCamelCase__       : Tuple   					=						BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'      )
     lowerCamelCase__       : Optional[Any]   					=						model.to(UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						BeitImageProcessor(do_resize=UpperCAmelCase     ,	size=640     ,	do_center_crop=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						load_dataset('hf-internal-testing/fixtures_ade20k'     ,	split='test'      )
     lowerCamelCase__       : List[Any]   					=						Image.open(ds[0]['file']      )
     lowerCamelCase__       : List[Any]   					=						image_processor(images=UpperCAmelCase     ,	return_tensors='pt'      ).to(UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
         lowerCamelCase__       : int   					=						model(**UpperCAmelCase      )

     lowerCamelCase__       : int   					=						outputs.logits.detach().cpu()

     lowerCamelCase__       : Optional[Any]   					=						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase     ,	target_sizes=[(500, 300)]      )
     lowerCamelCase__       : Optional[Any]   					=						torch.Size((500, 300)      )
     self.assertEqual(segmentation[0].shape     ,	UpperCAmelCase      )

     lowerCamelCase__       : str   					=						image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						torch.Size((160, 160)      )
     self.assertEqual(segmentation[0].shape     ,	UpperCAmelCase      )












import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : Optional[Any]   					=						tempfile.mkdtemp()

     # fmt: off
     lowerCamelCase__       : Tuple   					=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     lowerCamelCase__       : int   					=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['vocab_file']      )
     with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )

     lowerCamelCase__       : Any   					=						{
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     lowerCamelCase__       : int   					=						os.path.join(self.tmpdirname     ,	UpperCAmelCase      )
     with open(self.image_processor_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
         json.dump(UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	**UpperCAmelCase    : str      )     ->      int:
     return BertTokenizer.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	**UpperCAmelCase    : List[str]      )     ->      List[str]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : int      )     ->      Any:
     shutil.rmtree(self.tmpdirname      )
 def 	A_      ( self    : str      )     ->      str:
     lowerCamelCase__       : List[Any]   					=						[np.random.randint(255     ,	size=(3, 30, 400)     ,	dtype=np.uinta      )]

     lowerCamelCase__       : Union[str, Any]   					=						[Image.fromarray(np.moveaxis(UpperCAmelCase     ,	0     ,	-1      )      ) for x in image_inputs]

     return image_inputs
 def 	A_      ( self    : Dict      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						self.get_tokenizer()
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     processor.save_pretrained(self.tmpdirname      )
     lowerCamelCase__       : List[str]   					=						VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

     self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer.get_vocab()      )
     self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

     self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor.to_json_string()      )
     self.assertIsInstance(processor.image_processor     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()      )
     processor.save_pretrained(self.tmpdirname      )

     lowerCamelCase__       : Tuple   					=						self.get_tokenizer(bos_token='(BOS)'     ,	eos_token='(EOS)'      )
     lowerCamelCase__       : int   					=						self.get_image_processor(do_normalize=UpperCAmelCase     ,	padding_value=1.0      )

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname     ,	bos_token='(BOS)'     ,	eos_token='(EOS)'     ,	do_normalize=UpperCAmelCase     ,	padding_value=1.0      )

     self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer_add_kwargs.get_vocab()      )
     self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

     self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor_add_kwargs.to_json_string()      )
     self.assertIsInstance(processor.image_processor     ,	UpperCAmelCase      )
 def 	A_      ( self    : Any      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						self.get_image_processor()
     lowerCamelCase__       : Any   					=						self.get_tokenizer()

     lowerCamelCase__       : Optional[int]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Optional[int]   					=						self.prepare_image_inputs()

     lowerCamelCase__       : int   					=						image_processor(UpperCAmelCase     ,	return_tensors='np'      )
     lowerCamelCase__       : List[str]   					=						processor(images=UpperCAmelCase     ,	return_tensors='np'      )

     for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()     ,	input_processor[key].sum()     ,	delta=1e-2      )
 def 	A_      ( self    : Tuple      )     ->      List[Any]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : str   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Tuple   					=						'lower newer'

     lowerCamelCase__       : Tuple   					=						processor(text=UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						tokenizer(UpperCAmelCase      )

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]     ,	encoded_processor[key]      )
 def 	A_      ( self    : Optional[int]      )     ->      Any:
     lowerCamelCase__       : Optional[int]   					=						self.get_image_processor()
     lowerCamelCase__       : Tuple   					=						self.get_tokenizer()

     lowerCamelCase__       : Tuple   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						'lower newer'
     lowerCamelCase__       : Dict   					=						self.prepare_image_inputs()

     lowerCamelCase__       : int   					=						processor(text=UpperCAmelCase     ,	images=UpperCAmelCase      )

     self.assertListEqual(list(inputs.keys()      )     ,	['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']      )

     # test if it raises when no input is passed
     with self.assertRaises(UpperCAmelCase      ):
         processor()
 def 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : Union[str, Any]   					=						self.get_tokenizer()

     lowerCamelCase__       : List[str]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__       : Dict   					=						processor.batch_decode(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						tokenizer.batch_decode(UpperCAmelCase      )

     self.assertListEqual(UpperCAmelCase     ,	UpperCAmelCase      )







 def 	A_      ( self    : Optional[int]      )     ->      List[str]:
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						'lower newer'
     lowerCamelCase__       : List[Any]   					=						self.prepare_image_inputs()

     lowerCamelCase__       : Union[str, Any]   					=						processor(text=UpperCAmelCase     ,	images=UpperCAmelCase      )

     self.assertListEqual(list(inputs.keys()      )     ,	processor.model_input_names      )





