






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 		UpperCAmelCase_						:


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=100		,				_SCREAMING_SNAKE_CASE=13		,				_SCREAMING_SNAKE_CASE=30		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=37		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=[0, 1, 2, 3]		,				) ->				Tuple:
								snake_case_				:							str						 =       parent
								snake_case_				:							List[Any]						 =       100
								snake_case_				:							List[str]						 =       batch_size
								snake_case_				:							Any						 =       image_size
								snake_case_				:							Optional[int]						 =       patch_size
								snake_case_				:							Any						 =       num_channels
								snake_case_				:							Optional[int]						 =       is_training
								snake_case_				:							List[Any]						 =       use_labels
								snake_case_				:							Tuple						 =       hidden_size
								snake_case_				:							List[Any]						 =       num_hidden_layers
								snake_case_				:							List[str]						 =       num_attention_heads
								snake_case_				:							str						 =       intermediate_size
								snake_case_				:							int						 =       hidden_act
								snake_case_				:							List[str]						 =       hidden_dropout_prob
								snake_case_				:							str						 =       attention_probs_dropout_prob
								snake_case_				:							Optional[int]						 =       type_sequence_label_size
								snake_case_				:							Dict						 =       initializer_range
								snake_case_				:							Dict						 =       scope
								snake_case_				:							Optional[Any]						 =       out_indices
								snake_case_				:							Union[str, Any]						 =       num_labels

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






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_				:							str						 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

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

								return config, pixel_values, labels, pixel_labels






						def       _lowerCAmelCase       (	self				) ->				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=_SCREAMING_SNAKE_CASE		,				initializer_range=self.initializer_range		,				out_indices=self.out_indices		,				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								snake_case_				:							Tuple						 =       BeitModel(config=_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							List[Any]						 =       model(_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)				)






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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							str						 =       self.type_sequence_label_size
								snake_case_				:							int						 =       BeitForImageClassification(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()
								snake_case_				:							Optional[Any]						 =       model(_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)				)

								# test greyscale images
								snake_case_				:							Tuple						 =       1
								snake_case_				:							List[Any]						 =       BeitForImageClassification(_SCREAMING_SNAKE_CASE				)
								model.to(_SCREAMING_SNAKE_CASE				)
								model.eval()

								snake_case_				:							List[Any]						 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
								snake_case_				:							Any						 =       model(_SCREAMING_SNAKE_CASE		,				labels=_SCREAMING_SNAKE_CASE				)
								self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)				)






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






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							Dict						 =       self.prepare_config_and_inputs()
								snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_				:							Any						 =       config_and_inputs
								snake_case_				:							Optional[Any]						 =       {"pixel_values": pixel_values}
								return config, inputs_dict




@require_torch
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   int						=					(
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						A     :   List[str]						=					(
						    {
						        'feature-extraction': BeitModel,
						        'image-classification': BeitForImageClassification,
						        'image-segmentation': BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						A     :   int						=					False
						A     :   List[Any]						=					False
						A     :   List[Any]						=					False






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_				:							Optional[Any]						 =       BeitModelTester(self				)
								snake_case_				:							Dict						 =       ConfigTester(self		,				config_class=_SCREAMING_SNAKE_CASE		,				has_text_modality=_SCREAMING_SNAKE_CASE		,				hidden_size=37				)






						def       _lowerCAmelCase       (	self				) ->				str:
								self.config_tester.run_common_tests()






						@unittest.skip(reason="BEiT does not use inputs_embeds"				)
						def       _lowerCAmelCase       (	self				) ->				Dict:
								pass






						@require_torch_multi_gpu
						@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"				)
						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								pass






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_ ,       snake_case_				:							Any						 =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case_				:							str						 =       model_class(_SCREAMING_SNAKE_CASE				)
										self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module)				)
										snake_case_				:							Optional[int]						 =       model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE		,				nn.Linear				)				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_ ,       snake_case_				:							Optional[Any]						 =       self.model_tester.prepare_config_and_inputs_for_common()

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

										snake_case_				:							Dict						 =       ["pixel_values"]
										self.assertListEqual(arg_names[:1]		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							List[str]						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Any						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Tuple						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							int						 =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								if not self.model_tester.is_training:
										return

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

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

										snake_case_				:							Optional[Any]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										model.to(_SCREAMING_SNAKE_CASE				)
										model.train()
										snake_case_				:							Dict						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Optional[Any]						 =       model(**_SCREAMING_SNAKE_CASE				).loss
										loss.backward()






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_ ,       snake_case_				:							Optional[int]						 =       self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
										return

								snake_case_				:							Dict						 =       False
								snake_case_				:							List[Any]						 =       True

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

										snake_case_				:							Optional[int]						 =       model_class(_SCREAMING_SNAKE_CASE				)
										model.gradient_checkpointing_enable()
										model.to(_SCREAMING_SNAKE_CASE				)
										model.train()
										snake_case_				:							Dict						 =       self._prepare_for_class(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				return_labels=_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[Any]						 =       model(**_SCREAMING_SNAKE_CASE				).loss
										loss.backward()






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_ ,       snake_case_				:							Dict						 =       self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_				:							List[Any]						 =       _config_zero_init(_SCREAMING_SNAKE_CASE				)
								for model_class in self.all_model_classes:
										snake_case_				:							Union[str, Any]						 =       model_class(config=_SCREAMING_SNAKE_CASE				)
										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       _lowerCAmelCase       (	self				) ->				List[Any]:
								for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case_				:							str						 =       BeitModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)




def      lowerCAmelCase__	(    ):
		snake_case_				:							Any						 =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
		return image




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


						'''simple docstring'''




						@cached_property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"				) if is_vision_available() else None






						@slow
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Optional[int]						 =       BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"				).to(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       self.default_image_processor
								snake_case_				:							Optional[int]						 =       prepare_img()
								snake_case_				:							int						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).pixel_values.to(_SCREAMING_SNAKE_CASE				)

								# prepare bool_masked_pos
								snake_case_				:							str						 =       torch.ones((1, 196)		,				dtype=torch.bool				).to(_SCREAMING_SNAKE_CASE				)

								# forward pass
								with torch.no_grad():
										snake_case_				:							str						 =       model(pixel_values=_SCREAMING_SNAKE_CASE		,				bool_masked_pos=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       outputs.logits

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

								snake_case_				:							Tuple						 =       torch.tensor(
								    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]				).to(_SCREAMING_SNAKE_CASE				)

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






						@slow
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							str						 =       BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"				).to(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       self.default_image_processor
								snake_case_				:							Union[str, Any]						 =       prepare_img()
								snake_case_				:							Tuple						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)

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

								# verify the logits
								snake_case_				:							str						 =       torch.Size((1, 1000)				)
								self.assertEqual(logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       torch.tensor([-1.2385, -1.0987, -1.0108]				).to(_SCREAMING_SNAKE_CASE				)

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

								snake_case_				:							List[Any]						 =       281
								self.assertEqual(logits.argmax(-1				).item()		,				_SCREAMING_SNAKE_CASE				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Union[str, Any]						 =       BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"				).to(
								    _SCREAMING_SNAKE_CASE				)

								snake_case_				:							Dict						 =       self.default_image_processor
								snake_case_				:							Optional[Any]						 =       prepare_img()
								snake_case_				:							Dict						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)

								# forward pass
								with torch.no_grad():
										snake_case_				:							Union[str, Any]						 =       model(**_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       outputs.logits

								# verify the logits
								snake_case_				:							List[str]						 =       torch.Size((1, 2_1841)				)
								self.assertEqual(logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							int						 =       torch.tensor([1.6881, -0.2787, 0.5901]				).to(_SCREAMING_SNAKE_CASE				)

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

								snake_case_				:							List[str]						 =       2396
								self.assertEqual(logits.argmax(-1				).item()		,				_SCREAMING_SNAKE_CASE				)






						@slow
						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							int						 =       BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"				)
								snake_case_				:							Tuple						 =       model.to(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       BeitImageProcessor(do_resize=_SCREAMING_SNAKE_CASE		,				size=640		,				do_center_crop=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Union[str, Any]						 =       load_dataset("hf-internal-testing/fixtures_ade20k"		,				split="test"				)
								snake_case_				:							List[str]						 =       Image.open(ds[0]["file"]				)
								snake_case_				:							Any						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)

								# forward pass
								with torch.no_grad():
										snake_case_				:							Tuple						 =       model(**_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       outputs.logits

								# verify the logits
								snake_case_				:							Optional[Any]						 =       torch.Size((1, 150, 160, 160)				)
								self.assertEqual(logits.shape		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							str						 =       version.parse(PIL.__version__				) < version.parse("9.0.0"				)

								if is_pillow_less_than_a:
										snake_case_				:							List[Any]						 =       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=_SCREAMING_SNAKE_CASE		,				)
								else:
										snake_case_				:							Dict						 =       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=_SCREAMING_SNAKE_CASE		,				)

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






						@slow
						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							List[Any]						 =       BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"				)
								snake_case_				:							Optional[int]						 =       model.to(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       BeitImageProcessor(do_resize=_SCREAMING_SNAKE_CASE		,				size=640		,				do_center_crop=_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Any						 =       load_dataset("hf-internal-testing/fixtures_ade20k"		,				split="test"				)
								snake_case_				:							List[Any]						 =       Image.open(ds[0]["file"]				)
								snake_case_				:							Union[str, Any]						 =       image_processor(images=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				).to(_SCREAMING_SNAKE_CASE				)

								# forward pass
								with torch.no_grad():
										snake_case_				:							str						 =       model(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Dict						 =       outputs.logits.detach().cpu()

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

								snake_case_				:							List[Any]						 =       image_processor.post_process_semantic_segmentation(outputs=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							int						 =       torch.Size((160, 160)				)
								self.assertEqual(segmentation[0].shape		,				_SCREAMING_SNAKE_CASE				)








import qiskit

def      lowerCAmelCase__	(    _a       :			int  ,      _a       :			int    ):
		snake_case_				:							Tuple						 =       qiskit.Aer.get_backend("aer_simulator"    )

		# Create a Quantum Circuit acting on the q register
		snake_case_				:							Any						 =       qiskit.QuantumCircuit(_a  ,      _a    )

		# Apply X (NOT) Gate to Qubits 0 & 1
		circuit.x(0    )
		circuit.x(1    )

		# Map the quantum measurement to the classical bits
		circuit.measure([0, 1]  ,      [0, 1]    )

		# Execute the circuit on the qasm simulator
		snake_case_				:							Any						 =       qiskit.execute(_a  ,      _a  ,      shots=10_00    )

		# Return the histogram data of the results of the experiment.
		return job.result().get_counts(_a    )


if __name__ == "__main__":
							lowercase  :							List[Any]			  =    single_qubit_measure(2, 2)
							print(F"""Total count for various states are: {counts}""")
