






'''simple docstring'''
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      __SCREAMING_SNAKE_CASE      :




	def __init__(      self	:							Optional[Any]					,      __lowercase	:							int					,      __lowercase	:							List[Any]=1_00					,      __lowercase	:							Optional[int]=13					,      __lowercase	:							Union[str, Any]=30					,      __lowercase	:							List[Any]=2					,      __lowercase	:							Any=3					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Any=True					,      __lowercase	:							Optional[Any]=32					,      __lowercase	:							Tuple=4					,      __lowercase	:							str=4					,      __lowercase	:							Optional[Any]=37					,      __lowercase	:							Union[str, Any]="gelu"					,      __lowercase	:							List[str]=0.1					,      __lowercase	:							Union[str, Any]=0.1					,      __lowercase	:							Any=10					,      __lowercase	:							Optional[Any]=0.02					,      __lowercase	:							Union[str, Any]=3					,      __lowercase	:							Union[str, Any]=None					,      __lowercase	:							Tuple=[0, 1, 2, 3]					,      )	->						Optional[Any]:
							SCREAMING_SNAKE_CASE__     :				Dict									=parent
							SCREAMING_SNAKE_CASE__     :				Dict									=1_00
							SCREAMING_SNAKE_CASE__     :				Dict									=batch_size
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=image_size
							SCREAMING_SNAKE_CASE__     :				str									=patch_size
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=num_channels
							SCREAMING_SNAKE_CASE__     :				List[Any]									=is_training
							SCREAMING_SNAKE_CASE__     :				Any									=use_labels
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=hidden_size
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=num_hidden_layers
							SCREAMING_SNAKE_CASE__     :				int									=num_attention_heads
							SCREAMING_SNAKE_CASE__     :				List[str]									=intermediate_size
							SCREAMING_SNAKE_CASE__     :				List[str]									=hidden_act
							SCREAMING_SNAKE_CASE__     :				str									=hidden_dropout_prob
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE__     :				Any									=type_sequence_label_size
							SCREAMING_SNAKE_CASE__     :				List[Any]									=initializer_range
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=scope
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=out_indices
							SCREAMING_SNAKE_CASE__     :				Any									=num_labels

							# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							SCREAMING_SNAKE_CASE__     :				Tuple									=(image_size // patch_size) ** 2
							SCREAMING_SNAKE_CASE__     :				Dict									=num_patches + 1




	def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
							SCREAMING_SNAKE_CASE__     :				int									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

							SCREAMING_SNAKE_CASE__     :				Dict									=None
							SCREAMING_SNAKE_CASE__     :				List[Any]									=None
							if self.use_labels:
													SCREAMING_SNAKE_CASE__     :				Optional[int]									=ids_tensor([self.batch_size]					,      self.type_sequence_label_size      )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=ids_tensor([self.batch_size, self.image_size, self.image_size]					,      self.num_labels      )

							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.get_config()

							return config, pixel_values, labels, pixel_labels




	def      __magic_name__	(      self	:							Any      )	->						int:
							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=__lowercase					,      initializer_range=self.initializer_range					,      out_indices=self.out_indices					,      )




	def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							Tuple					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=BeitModel(config=__lowercase      )
							model.to(__lowercase      )
							model.eval()
							SCREAMING_SNAKE_CASE__     :				List[Any]									=model(__lowercase      )
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)      )




	def      __magic_name__	(      self	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							str					,      __lowercase	:							List[str]					,      __lowercase	:							str      )	->						Optional[Any]:
							SCREAMING_SNAKE_CASE__     :				str									=BeitForMaskedImageModeling(config=__lowercase      )
							model.to(__lowercase      )
							model.eval()
							SCREAMING_SNAKE_CASE__     :				List[Any]									=model(__lowercase      )
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length - 1, self.vocab_size)      )




	def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Optional[int]					,      __lowercase	:							Any					,      __lowercase	:							Any					,      __lowercase	:							Tuple      )	->						Optional[int]:
							SCREAMING_SNAKE_CASE__     :				Tuple									=self.type_sequence_label_size
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=BeitForImageClassification(__lowercase      )
							model.to(__lowercase      )
							model.eval()
							SCREAMING_SNAKE_CASE__     :				List[str]									=model(__lowercase					,      labels=__lowercase      )
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)      )

							# test greyscale images
							SCREAMING_SNAKE_CASE__     :				Tuple									=1
							SCREAMING_SNAKE_CASE__     :				Tuple									=BeitForImageClassification(__lowercase      )
							model.to(__lowercase      )
							model.eval()

							SCREAMING_SNAKE_CASE__     :				List[Any]									=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(__lowercase					,      labels=__lowercase      )
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)      )




	def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Optional[int]					,      __lowercase	:							List[str]					,      __lowercase	:							Optional[int]      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				List[str]									=self.num_labels
							SCREAMING_SNAKE_CASE__     :				str									=BeitForSemanticSegmentation(__lowercase      )
							model.to(__lowercase      )
							model.eval()
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(__lowercase      )
							self.parent.assertEqual(
							    result.logits.shape					,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(__lowercase					,      labels=__lowercase      )
							self.parent.assertEqual(
							    result.logits.shape					,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )




	def      __magic_name__	(      self	:							int      )	->						Dict:
							SCREAMING_SNAKE_CASE__     :				str									=self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE__     :				Tuple									=config_and_inputs
							SCREAMING_SNAKE_CASE__     :				List[Any]									={'''pixel_values''': pixel_values}
							return config, inputs_dict






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

	snake_case_  							=							False
	snake_case_  							=							False
	snake_case_  							=							False




	def      __magic_name__	(      self	:							int      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=BeitModelTester(self      )
							SCREAMING_SNAKE_CASE__     :				str									=ConfigTester(self					,      config_class=__lowercase					,      has_text_modality=__lowercase					,      hidden_size=37      )




	def      __magic_name__	(      self	:							int      )	->						int:
							self.config_tester.run_common_tests()




	@unittest.skip(reason='''BEiT does not use inputs_embeds'''      )
	def      __magic_name__	(      self	:							Optional[Any]      )	->						Optional[Any]:
							pass




	@require_torch_multi_gpu
	@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''      )
	def      __magic_name__	(      self	:							Tuple      )	->						Any:
							pass




	def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model_class(__lowercase      )
													self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)      )
													SCREAMING_SNAKE_CASE__     :				Dict									=model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__lowercase					,      nn.Linear      )      )




	def      __magic_name__	(      self	:							Dict      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.model_tester.prepare_config_and_inputs_for_common()

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

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




	def      __magic_name__	(      self	:							str      )	->						Optional[int]:
							SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase      )




	def      __magic_name__	(      self	:							Dict      )	->						Optional[Any]:
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowercase      )




	def      __magic_name__	(      self	:							Optional[Any]      )	->						str:
							SCREAMING_SNAKE_CASE__     :				str									=self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase      )




	def      __magic_name__	(      self	:							Optional[Any]      )	->						Tuple:
							SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase      )




	def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[Any]:
							if not self.model_tester.is_training:
													return

							SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE__     :				List[Any]									=True

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

													SCREAMING_SNAKE_CASE__     :				Tuple									=model_class(__lowercase      )
													model.to(__lowercase      )
													model.train()
													SCREAMING_SNAKE_CASE__     :				Optional[int]									=self._prepare_for_class(__lowercase					,      __lowercase					,      return_labels=__lowercase      )
													SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(**__lowercase      ).loss
													loss.backward()




	def      __magic_name__	(      self	:							Optional[int]      )	->						Optional[Any]:
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
													return

							SCREAMING_SNAKE_CASE__     :				int									=False
							SCREAMING_SNAKE_CASE__     :				str									=True

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

													SCREAMING_SNAKE_CASE__     :				Optional[int]									=model_class(__lowercase      )
													model.gradient_checkpointing_enable()
													model.to(__lowercase      )
													model.train()
													SCREAMING_SNAKE_CASE__     :				str									=self._prepare_for_class(__lowercase					,      __lowercase					,      return_labels=__lowercase      )
													SCREAMING_SNAKE_CASE__     :				Dict									=model(**__lowercase      ).loss
													loss.backward()




	def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[str]:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.model_tester.prepare_config_and_inputs_for_common()

							SCREAMING_SNAKE_CASE__     :				List[str]									=_config_zero_init(__lowercase      )
							for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE__     :				str									=model_class(config=__lowercase      )
													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      __magic_name__	(      self	:							Dict      )	->						str:
							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=BeitModel.from_pretrained(__lowercase      )
													self.assertIsNotNone(__lowercase      )





def  _a(  ):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__     :				List[str]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
						return image







@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




	@cached_property
	def      __magic_name__	(      self	:							List[Any]      )	->						Dict:
							return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None




	@slow
	def      __magic_name__	(      self	:							Any      )	->						int:
							SCREAMING_SNAKE_CASE__     :				Any									=BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      ).to(__lowercase      )

							SCREAMING_SNAKE_CASE__     :				List[Any]									=self.default_image_processor
							SCREAMING_SNAKE_CASE__     :				List[str]									=prepare_img()
							SCREAMING_SNAKE_CASE__     :				Any									=image_processor(images=__lowercase					,      return_tensors='''pt'''      ).pixel_values.to(__lowercase      )

							# prepare bool_masked_pos
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.ones((1, 1_96)					,      dtype=torch.bool      ).to(__lowercase      )

							# forward pass
							with torch.no_grad():
													SCREAMING_SNAKE_CASE__     :				List[Any]									=model(pixel_values=__lowercase					,      bool_masked_pos=__lowercase      )
							SCREAMING_SNAKE_CASE__     :				List[Any]									=outputs.logits

							# verify the logits
							SCREAMING_SNAKE_CASE__     :				str									=torch.Size((1, 1_96, 81_92)      )
							self.assertEqual(logits.shape					,      __lowercase      )

							SCREAMING_SNAKE_CASE__     :				str									=torch.tensor(
							    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]      ).to(__lowercase      )

							self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]					,      __lowercase					,      atol=1e-2      )      )




	@slow
	def      __magic_name__	(      self	:							List[Any]      )	->						Dict:
							SCREAMING_SNAKE_CASE__     :				List[str]									=BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      ).to(__lowercase      )

							SCREAMING_SNAKE_CASE__     :				int									=self.default_image_processor
							SCREAMING_SNAKE_CASE__     :				int									=prepare_img()
							SCREAMING_SNAKE_CASE__     :				int									=image_processor(images=__lowercase					,      return_tensors='''pt'''      ).to(__lowercase      )

							# forward pass
							with torch.no_grad():
													SCREAMING_SNAKE_CASE__     :				Any									=model(**__lowercase      )
							SCREAMING_SNAKE_CASE__     :				str									=outputs.logits

							# verify the logits
							SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Size((1, 10_00)      )
							self.assertEqual(logits.shape					,      __lowercase      )

							SCREAMING_SNAKE_CASE__     :				Tuple									=torch.tensor([-1.2385, -1.0987, -1.0108]      ).to(__lowercase      )

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

							SCREAMING_SNAKE_CASE__     :				Any									=2_81
							self.assertEqual(logits.argmax(-1      ).item()					,      __lowercase      )




	@slow
	def      __magic_name__	(      self	:							Optional[int]      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Dict									=BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      ).to(
							    __lowercase      )

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

							# forward pass
							with torch.no_grad():
													SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(**__lowercase      )
							SCREAMING_SNAKE_CASE__     :				int									=outputs.logits

							# verify the logits
							SCREAMING_SNAKE_CASE__     :				int									=torch.Size((1, 2_18_41)      )
							self.assertEqual(logits.shape					,      __lowercase      )

							SCREAMING_SNAKE_CASE__     :				Any									=torch.tensor([1.6881, -0.2787, 0.5901]      ).to(__lowercase      )

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

							SCREAMING_SNAKE_CASE__     :				List[str]									=23_96
							self.assertEqual(logits.argmax(-1      ).item()					,      __lowercase      )




	@slow
	def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
							SCREAMING_SNAKE_CASE__     :				int									=BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''      )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model.to(__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=BeitImageProcessor(do_resize=__lowercase					,      size=6_40					,      do_center_crop=__lowercase      )

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

							# forward pass
							with torch.no_grad():
													SCREAMING_SNAKE_CASE__     :				Any									=model(**__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=outputs.logits

							# verify the logits
							SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Size((1, 1_50, 1_60, 1_60)      )
							self.assertEqual(logits.shape					,      __lowercase      )

							SCREAMING_SNAKE_CASE__     :				List[Any]									=version.parse(PIL.__version__      ) < version.parse('''9.0.0'''      )

							if is_pillow_less_than_a:
													SCREAMING_SNAKE_CASE__     :				int									=torch.tensor(
													    [
													        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
													        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
													        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
													    ]					,      device=__lowercase					,      )
							else:
													SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.tensor(
													    [
													        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
													        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
													        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
													    ]					,      device=__lowercase					,      )

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




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

							SCREAMING_SNAKE_CASE__     :				str									=BeitImageProcessor(do_resize=__lowercase					,      size=6_40					,      do_center_crop=__lowercase      )

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

							# forward pass
							with torch.no_grad():
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(**__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Optional[int]									=outputs.logits.detach().cpu()

							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=image_processor.post_process_semantic_segmentation(outputs=__lowercase					,      target_sizes=[(5_00, 3_00)]      )
							SCREAMING_SNAKE_CASE__     :				Any									=torch.Size((5_00, 3_00)      )
							self.assertEqual(segmentation[0].shape					,      __lowercase      )

							SCREAMING_SNAKE_CASE__     :				Any									=image_processor.post_process_semantic_segmentation(outputs=__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.Size((1_60, 1_60)      )
							self.assertEqual(segmentation[0].shape					,      __lowercase      )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)