



"""simple docstring"""







import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
			import torch

			from transformers import CvtForImageClassification, CvtModel
			from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class       _a  ( SCREAMING_SNAKE_CASE__):



			def 				__lowercase					(			self							:				List[Any]		)							->					Optional[Any]:
									snake_case   :			List[Any]						    = self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(_lowercase   ,	"embed_dim"		)		)
									self.parent.assertTrue(hasattr(_lowercase   ,	"num_heads"		)		)



class       _a  :



			def __init__(			self							:				str   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Optional[Any]=13   ,	_lowercase							:				Optional[int]=64   ,	_lowercase							:				List[str]=3   ,	_lowercase							:				Tuple=[16, 48, 96]   ,	_lowercase							:				List[str]=[1, 3, 6]   ,	_lowercase							:				Any=[1, 2, 10]   ,	_lowercase							:				Optional[int]=[7, 3, 3]   ,	_lowercase							:				Any=[4, 2, 2]   ,	_lowercase							:				Optional[int]=[2, 1, 1]   ,	_lowercase							:				int=[2, 2, 2]   ,	_lowercase							:				Dict=[False, False, True]   ,	_lowercase							:				Optional[Any]=[0.0, 0.0, 0.0]   ,	_lowercase							:				int=0.02   ,	_lowercase							:				Union[str, Any]=1E-12   ,	_lowercase							:				Union[str, Any]=True   ,	_lowercase							:				Dict=True   ,	_lowercase							:				Optional[Any]=2   ,	)							->					Optional[int]:
									snake_case   :			int						    = parent
									snake_case   :			Union[str, Any]						    = batch_size
									snake_case   :			Union[str, Any]						    = image_size
									snake_case   :			str						    = patch_sizes
									snake_case   :			int						    = patch_stride
									snake_case   :			Union[str, Any]						    = patch_padding
									snake_case   :			Optional[int]						    = is_training
									snake_case   :			int						    = use_labels
									snake_case   :			str						    = num_labels
									snake_case   :			List[str]						    = num_channels
									snake_case   :			Tuple						    = embed_dim
									snake_case   :			List[str]						    = num_heads
									snake_case   :			Union[str, Any]						    = stride_kv
									snake_case   :			Optional[Any]						    = depth
									snake_case   :			Dict						    = cls_token
									snake_case   :			Union[str, Any]						    = attention_drop_rate
									snake_case   :			int						    = initializer_range
									snake_case   :			Union[str, Any]						    = layer_norm_eps



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

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

									snake_case   :			List[str]						    = self.get_config()
									return config, pixel_values, labels



			def 				__lowercase					(			self							:				Any		)							->					int:
									return CvtConfig(
									    image_size=self.image_size   ,	num_labels=self.num_labels   ,	num_channels=self.num_channels   ,	embed_dim=self.embed_dim   ,	num_heads=self.num_heads   ,	patch_sizes=self.patch_sizes   ,	patch_padding=self.patch_padding   ,	patch_stride=self.patch_stride   ,	stride_kv=self.stride_kv   ,	depth=self.depth   ,	cls_token=self.cls_token   ,	attention_drop_rate=self.attention_drop_rate   ,	initializer_range=self.initializer_range   ,	)



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				List[str]   ,	_lowercase							:				int		)							->					Any:
									snake_case   :			List[Any]						    = CvtModel(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Optional[int]						    = model(_lowercase		)
									snake_case   :			List[str]						    = (self.image_size, self.image_size)
									snake_case    ,			snake_case   :			Union[str, Any]						    = image_size[0], image_size[1]
									for i in range(len(self.depth		)		):
															snake_case   :			Optional[int]						    = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
															snake_case   :			Any						    = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
									self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.embed_dim[-1], height, width)		)



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				str   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Tuple		)							->					List[Any]:
									snake_case   :			Tuple						    = self.num_labels
									snake_case   :			Dict						    = CvtForImageClassification(_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.num_labels)		)



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



@require_torch
class       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     (CvtModel, CvtForImageClassification) if is_torch_available() else ()
			__magic_name__     					=     (
			    {"""feature-extraction""": CvtModel, """image-classification""": CvtForImageClassification}
			    if is_torch_available()
			    else {}
			)

			__magic_name__     					=     False
			__magic_name__     					=     False
			__magic_name__     					=     False
			__magic_name__     					=     False
			__magic_name__     					=     False



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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[Any]:
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()



			def 				__lowercase					(			self							:				Tuple		)							->					Dict:
									return



			@unittest.skip(reason="Cvt does not output attentions"		)
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					str:
									pass



			@unittest.skip(reason="Cvt does not use inputs_embeds"		)
			def 				__lowercase					(			self							:				List[str]		)							->					Tuple:
									pass



			@unittest.skip(reason="Cvt does not support input and output embeddings"		)
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Dict:
									pass



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

									for model_class in self.all_model_classes:
															snake_case   :			Optional[int]						    = 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   :			Optional[int]						    = ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,	_lowercase		)



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



			def 				__lowercase					(			self							:				Dict		)							->					str:
									def check_hidden_states_output(_lowercase							:				Optional[int]   ,	_lowercase							:				int   ,	_lowercase							:				List[str]		):
															snake_case   :			int						    = model_class(_lowercase		)
															model.to(_lowercase		)
															model.eval()

															with torch.no_grad():
																					snake_case   :			str						    = model(**self._prepare_for_class(_lowercase   ,	_lowercase		)		)

															snake_case   :			Dict						    = outputs.hidden_states

															snake_case   :			Any						    = len(self.model_tester.depth		)
															self.assertEqual(len(_lowercase		)   ,	_lowercase		)

															# verify the first hidden states (first block)
															self.assertListEqual(
															    list(hidden_states[0].shape[-3:]		)   ,	[
															        self.model_tester.embed_dim[0],
															        self.model_tester.image_size // 4,
															        self.model_tester.image_size // 4,
															    ]   ,	)

									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						    = True
															check_hidden_states_output(_lowercase   ,	_lowercase   ,	_lowercase		)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case   :			Union[str, Any]						    = True

															check_hidden_states_output(_lowercase   ,	_lowercase   ,	_lowercase		)



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



			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
			def 				__lowercase					(			self							:				List[str]		)							->					Union[str, Any]:
									pass



			@slow
			def 				__lowercase					(			self							:				str		)							->					Optional[int]:
									for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case   :			Union[str, Any]						    = CvtModel.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 AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)



			@slow
			def 				__lowercase					(			self							:				Any		)							->					Dict:
									snake_case   :			List[str]						    = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_lowercase		)

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

									# forward pass
									with torch.no_grad():
															snake_case   :			List[Any]						    = model(**_lowercase		)

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

									snake_case   :			Optional[int]						    = torch.tensor([0.9285, 0.9015, -0.3150]		).to(_lowercase		)

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








"""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		)



