



"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


A		=						logging.get_logger(__name__)

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     ["""pixel_values"""]



			def __init__(			self							:				Dict   ,	_lowercase							:				bool = True   ,	_lowercase							:				Dict[str, int] = None   ,	_lowercase							:				float = None   ,	_lowercase							:				PILImageResampling = PILImageResampling.BILINEAR   ,	_lowercase							:				bool = True   ,	_lowercase							:				Union[int, float] = 1 / 255   ,	_lowercase							:				bool = True   ,	_lowercase							:				Optional[Union[float, List[float]]] = None   ,	_lowercase							:				Optional[Union[float, List[float]]] = None   ,	**_lowercase							:				Union[str, Any]   ,	)							->					None:
									super().__init__(**_lowercase		)
									snake_case   :			Optional[int]						    = size if size is not None else {"shortest_edge": 384}
									snake_case   :			List[str]						    = get_size_dict(_lowercase   ,	default_to_square=_lowercase		)

									snake_case   :			Any						    = do_resize
									snake_case   :			str						    = size
									# Default value set here for backwards compatibility where the value in config is None
									snake_case   :			Dict						    = crop_pct if crop_pct is not None else 224 / 256
									snake_case   :			Union[str, Any]						    = resample
									snake_case   :			Union[str, Any]						    = do_rescale
									snake_case   :			Optional[Any]						    = rescale_factor
									snake_case   :			Optional[int]						    = do_normalize
									snake_case   :			Any						    = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									snake_case   :			Tuple						    = image_std if image_std is not None else IMAGENET_STANDARD_STD



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				Dict[str, int]   ,	_lowercase							:				float   ,	_lowercase							:				PILImageResampling = PILImageResampling.BICUBIC   ,	_lowercase							:				Optional[Union[str, ChannelDimension]] = None   ,	**_lowercase							:				Any   ,	)							->					np.ndarray:
									snake_case   :			str						    = get_size_dict(_lowercase   ,	default_to_square=_lowercase		)
									if "shortest_edge" not in size:
															raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''		)
									snake_case   :			List[str]						    = size["shortest_edge"]

									if shortest_edge < 384:
															# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
															snake_case   :			Tuple						    = int(shortest_edge / crop_pct		)
															snake_case   :			int						    = get_resize_output_image_size(_lowercase   ,	size=_lowercase   ,	default_to_square=_lowercase		)
															snake_case   :			List[Any]						    = resize(image=_lowercase   ,	size=_lowercase   ,	resample=_lowercase   ,	data_format=_lowercase   ,	**_lowercase		)
															# then crop to (shortest_edge, shortest_edge)
															return center_crop(image=_lowercase   ,	size=(shortest_edge, shortest_edge)   ,	data_format=_lowercase   ,	**_lowercase		)
									else:
															# warping (no cropping) when evaluated at 384 or larger
															return resize(
															    _lowercase   ,	size=(shortest_edge, shortest_edge)   ,	resample=_lowercase   ,	data_format=_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				Union[int, float]   ,	_lowercase							:				Optional[Union[str, ChannelDimension]] = None   ,	**_lowercase							:				List[Any]   ,	)							->					int:
									return rescale(_lowercase   ,	scale=_lowercase   ,	data_format=_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				np.ndarray   ,	_lowercase							:				Union[float, List[float]]   ,	_lowercase							:				Union[float, List[float]]   ,	_lowercase							:				Optional[Union[str, ChannelDimension]] = None   ,	**_lowercase							:				Optional[Any]   ,	)							->					np.ndarray:
									return normalize(_lowercase   ,	mean=_lowercase   ,	std=_lowercase   ,	data_format=_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				ImageInput   ,	_lowercase							:				bool = None   ,	_lowercase							:				Dict[str, int] = None   ,	_lowercase							:				float = None   ,	_lowercase							:				PILImageResampling = None   ,	_lowercase							:				bool = None   ,	_lowercase							:				float = None   ,	_lowercase							:				bool = None   ,	_lowercase							:				Optional[Union[float, List[float]]] = None   ,	_lowercase							:				Optional[Union[float, List[float]]] = None   ,	_lowercase							:				Optional[Union[str, TensorType]] = None   ,	_lowercase							:				ChannelDimension = ChannelDimension.FIRST   ,	**_lowercase							:				Tuple   ,	)							->					PIL.Image.Image:
									snake_case   :			List[Any]						    = do_resize if do_resize is not None else self.do_resize
									snake_case   :			Optional[int]						    = crop_pct if crop_pct is not None else self.crop_pct
									snake_case   :			Optional[Any]						    = resample if resample is not None else self.resample
									snake_case   :			Optional[Any]						    = do_rescale if do_rescale is not None else self.do_rescale
									snake_case   :			Optional[int]						    = rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case   :			Optional[Any]						    = do_normalize if do_normalize is not None else self.do_normalize
									snake_case   :			Union[str, Any]						    = image_mean if image_mean is not None else self.image_mean
									snake_case   :			Tuple						    = image_std if image_std is not None else self.image_std

									snake_case   :			str						    = size if size is not None else self.size
									snake_case   :			Optional[Any]						    = get_size_dict(_lowercase   ,	default_to_square=_lowercase		)

									snake_case   :			Union[str, Any]						    = make_list_of_images(_lowercase		)

									if not valid_images(_lowercase		):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray."		)

									if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."		)

									if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
															raise ValueError("crop_pct must be specified if size < 384."		)

									if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."		)

									if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True."		)

									# All transformations expect numpy arrays.
									snake_case   :			List[Any]						    = [to_numpy_array(_lowercase		) for image in images]

									if do_resize:
															snake_case   :			List[Any]						    = [self.resize(image=_lowercase   ,	size=_lowercase   ,	crop_pct=_lowercase   ,	resample=_lowercase		) for image in images]

									if do_rescale:
															snake_case   :			Any						    = [self.rescale(image=_lowercase   ,	scale=_lowercase		) for image in images]

									if do_normalize:
															snake_case   :			Union[str, Any]						    = [self.normalize(image=_lowercase   ,	mean=_lowercase   ,	std=_lowercase		) for image in images]

									snake_case   :			int						    = [to_channel_dimension_format(_lowercase   ,	_lowercase		) for image in images]

									snake_case   :			int						    = {"pixel_values": images}
									return BatchFeature(data=_lowercase   ,	tensor_type=_lowercase		)








"""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       _a  :



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

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



			def 				__lowercase					(			self							:				Tuple		)							->					Union[str, Any]:
									snake_case   :			Any						    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									snake_case   :			Optional[int]						    = None
									snake_case   :			Optional[int]						    = None
									if self.use_labels:
															snake_case   :			List[str]						    = ids_tensor([self.batch_size]   ,	self.type_sequence_label_size		)
															snake_case   :			Tuple						    = ids_tensor([self.batch_size, self.image_size, self.image_size]   ,	self.num_labels		)

									snake_case   :			str						    = self.get_config()

									return config, pixel_values, labels, pixel_labels



			def 				__lowercase					(			self							:				str		)							->					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=_lowercase   ,	initializer_range=self.initializer_range   ,	out_indices=self.out_indices   ,	)



			def 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				int   ,	_lowercase							:				Any   ,	_lowercase							:				List[Any]   ,	_lowercase							:				List[Any]		)							->					Any:
									snake_case   :			int						    = BeitModel(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			List[str]						    = model(_lowercase		)
									self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size)		)



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



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

									# test greyscale images
									snake_case   :			Dict						    = 1
									snake_case   :			Tuple						    = BeitForImageClassification(_lowercase		)
									model.to(_lowercase		)
									model.eval()

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



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				Dict   ,	_lowercase							:				Dict   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				List[Any]		)							->					List[Any]:
									snake_case   :			List[Any]						    = self.num_labels
									snake_case   :			Union[str, Any]						    = BeitForSemanticSegmentation(_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Tuple						    = model(_lowercase		)
									self.parent.assertEqual(
									    result.logits.shape   ,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)
									snake_case   :			Any						    = 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 				__lowercase					(			self							:				Any		)							->					Dict:
									snake_case   :			List[Any]						    = self.prepare_config_and_inputs()
									snake_case    ,			snake_case    ,			snake_case    ,			snake_case   :			Optional[int]						    = config_and_inputs
									snake_case   :			Dict						    = {"pixel_values": pixel_values}
									return config, inputs_dict



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

			__magic_name__     					=     False
			__magic_name__     					=     False
			__magic_name__     					=     False



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



			def 				__lowercase					(			self							:				int		)							->					str:
									self.config_tester.run_common_tests()



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



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



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

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



			def 				__lowercase					(			self							:				List[Any]		)							->					List[Any]:
									snake_case    ,			snake_case   :			List[Any]						    = self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case   :			int						    = ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,	_lowercase		)



			def 				__lowercase					(			self							:				Any		)							->					List[str]:
									snake_case   :			List[str]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]		)							->					str:
									snake_case   :			Optional[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_lowercase		)



			def 				__lowercase					(			self							:				List[Any]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase		)



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase		)



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

									snake_case    ,			snake_case   :			Dict						    = 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(_lowercase		), BeitForMaskedImageModeling]:
																					continue

															snake_case   :			List[str]						    = model_class(_lowercase		)
															model.to(_lowercase		)
															model.train()
															snake_case   :			Dict						    = self._prepare_for_class(_lowercase   ,	_lowercase   ,	return_labels=_lowercase		)
															snake_case   :			Tuple						    = model(**_lowercase		).loss
															loss.backward()



			def 				__lowercase					(			self							:				Dict		)							->					str:
									snake_case    ,			snake_case   :			List[str]						    = self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
															return

									snake_case   :			Dict						    = False
									snake_case   :			Dict						    = 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

															snake_case   :			str						    = model_class(_lowercase		)
															model.gradient_checkpointing_enable()
															model.to(_lowercase		)
															model.train()
															snake_case   :			Dict						    = self._prepare_for_class(_lowercase   ,	_lowercase   ,	return_labels=_lowercase		)
															snake_case   :			str						    = model(**_lowercase		).loss
															loss.backward()



			def 				__lowercase					(			self							:				List[Any]		)							->					Optional[int]:
									snake_case    ,			snake_case   :			Union[str, Any]						    = self.model_tester.prepare_config_and_inputs_for_common()

									snake_case   :			List[Any]						    = _config_zero_init(_lowercase		)
									for model_class in self.all_model_classes:
															snake_case   :			Union[str, Any]						    = 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 				__lowercase					(			self							:				Optional[Any]		)							->					List[Any]:
									for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case   :			Tuple						    = BeitModel.from_pretrained(_lowercase		)
															self.assertIsNotNone(_lowercase		)



def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






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

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



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



			@slow
			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			int						    = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"		).to(_lowercase		)

									snake_case   :			str						    = self.default_image_processor
									snake_case   :			int						    = prepare_img()
									snake_case   :			int						    = image_processor(images=_lowercase   ,	return_tensors="pt"		).pixel_values.to(_lowercase		)

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

									# forward pass
									with torch.no_grad():
															snake_case   :			Optional[Any]						    = model(pixel_values=_lowercase   ,	bool_masked_pos=_lowercase		)
									snake_case   :			Any						    = outputs.logits

									# verify the logits
									snake_case   :			int						    = torch.Size((1, 196, 8192)		)
									self.assertEqual(logits.shape   ,	_lowercase		)

									snake_case   :			List[Any]						    = 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 				__lowercase					(			self							:				List[str]		)							->					Tuple:
									snake_case   :			List[Any]						    = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"		).to(_lowercase		)

									snake_case   :			Tuple						    = self.default_image_processor
									snake_case   :			Dict						    = prepare_img()
									snake_case   :			int						    = image_processor(images=_lowercase   ,	return_tensors="pt"		).to(_lowercase		)

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

									# verify the logits
									snake_case   :			Dict						    = torch.Size((1, 1000)		)
									self.assertEqual(logits.shape   ,	_lowercase		)

									snake_case   :			Optional[Any]						    = torch.tensor([-1.2385, -1.0987, -1.0108]		).to(_lowercase		)

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

									snake_case   :			Optional[int]						    = 281
									self.assertEqual(logits.argmax(-1		).item()   ,	_lowercase		)



			@slow
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					List[str]:
									snake_case   :			str						    = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"		).to(
									    _lowercase		)

									snake_case   :			int						    = self.default_image_processor
									snake_case   :			List[str]						    = prepare_img()
									snake_case   :			Tuple						    = image_processor(images=_lowercase   ,	return_tensors="pt"		).to(_lowercase		)

									# forward pass
									with torch.no_grad():
															snake_case   :			Optional[Any]						    = model(**_lowercase		)
									snake_case   :			str						    = outputs.logits

									# verify the logits
									snake_case   :			int						    = torch.Size((1, 21841)		)
									self.assertEqual(logits.shape   ,	_lowercase		)

									snake_case   :			Optional[Any]						    = torch.tensor([1.6881, -0.2787, 0.5901]		).to(_lowercase		)

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

									snake_case   :			Dict						    = 2396
									self.assertEqual(logits.argmax(-1		).item()   ,	_lowercase		)



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

									snake_case   :			Optional[Any]						    = BeitImageProcessor(do_resize=_lowercase   ,	size=640   ,	do_center_crop=_lowercase		)

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

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

									# verify the logits
									snake_case   :			Union[str, Any]						    = torch.Size((1, 150, 160, 160)		)
									self.assertEqual(logits.shape   ,	_lowercase		)

									snake_case   :			Dict						    = version.parse(PIL.__version__		) < version.parse("9.0.0"		)

									if is_pillow_less_than_a:
															snake_case   :			Dict						    = 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:
															snake_case   :			List[Any]						    = 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 				__lowercase					(			self							:				List[str]		)							->					Tuple:
									snake_case   :			str						    = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"		)
									snake_case   :			Tuple						    = model.to(_lowercase		)

									snake_case   :			List[str]						    = BeitImageProcessor(do_resize=_lowercase   ,	size=640   ,	do_center_crop=_lowercase		)

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

									# forward pass
									with torch.no_grad():
															snake_case   :			Tuple						    = model(**_lowercase		)

									snake_case   :			Tuple						    = outputs.logits.detach().cpu()

									snake_case   :			Optional[Any]						    = image_processor.post_process_semantic_segmentation(outputs=_lowercase   ,	target_sizes=[(500, 300)]		)
									snake_case   :			List[str]						    = torch.Size((500, 300)		)
									self.assertEqual(segmentation[0].shape   ,	_lowercase		)

									snake_case   :			Optional[Any]						    = image_processor.post_process_semantic_segmentation(outputs=_lowercase		)
									snake_case   :			List[str]						    = torch.Size((160, 160)		)
									self.assertEqual(segmentation[0].shape   ,	_lowercase		)



