
'''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 		_UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=1_00					,     lowerCAmelCase_=13					,     lowerCAmelCase_=30					,     lowerCAmelCase_=2					,     lowerCAmelCase_=3					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=32					,     lowerCAmelCase_=4					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=10					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=3					,     lowerCAmelCase_=None					,     lowerCAmelCase_=[0, 1, 2, 3]					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	1_00
								_snake_case     		=	batch_size
								_snake_case     		=	image_size
								_snake_case     		=	patch_size
								_snake_case     		=	num_channels
								_snake_case     		=	is_training
								_snake_case     		=	use_labels
								_snake_case     		=	hidden_size
								_snake_case     		=	num_hidden_layers
								_snake_case     		=	num_attention_heads
								_snake_case     		=	intermediate_size
								_snake_case     		=	hidden_act
								_snake_case     		=	hidden_dropout_prob
								_snake_case     		=	attention_probs_dropout_prob
								_snake_case     		=	type_sequence_label_size
								_snake_case     		=	initializer_range
								_snake_case     		=	scope
								_snake_case     		=	out_indices
								_snake_case     		=	num_labels

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								_snake_case     		=	None
								_snake_case     		=	None
								if self.use_labels:
									_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
									_snake_case     		=	ids_tensor([self.batch_size, self.image_size, self.image_size]					,     self.num_labels						)

								_snake_case     		=	self.get_config()

								return config, pixel_values, labels, pixel_labels


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								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 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	BeitModel(config=_lowercase						)
								model.to(_lowercase						)
								model.eval()
								_snake_case     		=	model(_lowercase						)
								self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	BeitForMaskedImageModeling(config=_lowercase						)
								model.to(_lowercase						)
								model.eval()
								_snake_case     		=	model(_lowercase						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length - 1, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.type_sequence_label_size
								_snake_case     		=	BeitForImageClassification(_lowercase						)
								model.to(_lowercase						)
								model.eval()
								_snake_case     		=	model(_lowercase					,     labels=_lowercase						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)						)

								# test greyscale images
								_snake_case     		=	1
								_snake_case     		=	BeitForImageClassification(_lowercase						)
								model.to(_lowercase						)
								model.eval()

								_snake_case     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
								_snake_case     		=	model(_lowercase					,     labels=_lowercase						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	BeitForSemanticSegmentation(_lowercase						)
								model.to(_lowercase						)
								model.eval()
								_snake_case     		=	model(_lowercase						)
								self.parent.assertEqual(
								    result.logits.shape					,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
								_snake_case     		=	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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								_snake_case     		=	config_and_inputs
								_snake_case     		=	{"""pixel_values""": pixel_values}
								return config, inputs_dict


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

							__lowercase   	= False
							__lowercase   	= False
							__lowercase   	= False


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BeitModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=_lowercase					,     has_text_modality=_lowercase					,     hidden_size=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							@unittest.skip(reason='BEiT does not use inputs_embeds'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								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						):





								"""simple docstring"""






								pass


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									_snake_case     		=	model_class(_lowercase						)
									self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)						)
									_snake_case     		=	model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_lowercase					,     nn.Linear						)						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_lowercase						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								if not self.model_tester.is_training:
									return

								_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case     		=	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     		=	model_class(_lowercase						)
									model.to(_lowercase						)
									model.train()
									_snake_case     		=	self._prepare_for_class(_lowercase					,     _lowercase					,     return_labels=_lowercase						)
									_snake_case     		=	model(**_lowercase						).loss
									loss.backward()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
									return

								_snake_case     		=	False
								_snake_case     		=	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     		=	model_class(_lowercase						)
									model.gradient_checkpointing_enable()
									model.to(_lowercase						)
									model.train()
									_snake_case     		=	self._prepare_for_class(_lowercase					,     _lowercase					,     return_labels=_lowercase						)
									_snake_case     		=	model(**_lowercase						).loss
									loss.backward()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs_for_common()

								_snake_case     		=	_config_zero_init(_lowercase						)
								for model_class in self.all_model_classes:
									_snake_case     		=	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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	BeitModel.from_pretrained(_lowercase						)
									self.assertIsNotNone(_lowercase						)







def        SCREAMING_SNAKE_CASE__			(     ) ->			int:
	_snake_case     		=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image





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


							@cached_property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'						) if is_vision_available() else None


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'						).to(_lowercase						)

								_snake_case     		=	self.default_image_processor
								_snake_case     		=	prepare_img()
								_snake_case     		=	image_processor(images=_lowercase					,     return_tensors='pt'						).pixel_values.to(_lowercase						)

								# prepare bool_masked_pos
								_snake_case     		=	torch.ones((1, 1_96)					,     dtype=torch.bool						).to(_lowercase						)

								# forward pass
								with torch.no_grad():
									_snake_case     		=	model(pixel_values=_lowercase					,     bool_masked_pos=_lowercase						)
								_snake_case     		=	outputs.logits

								# verify the logits
								_snake_case     		=	torch.Size((1, 1_96, 81_92)						)
								self.assertEqual(logits.shape					,     _lowercase						)

								_snake_case     		=	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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'						).to(_lowercase						)

								_snake_case     		=	self.default_image_processor
								_snake_case     		=	prepare_img()
								_snake_case     		=	image_processor(images=_lowercase					,     return_tensors='pt'						).to(_lowercase						)

								# forward pass
								with torch.no_grad():
									_snake_case     		=	model(**_lowercase						)
								_snake_case     		=	outputs.logits

								# verify the logits
								_snake_case     		=	torch.Size((1, 10_00)						)
								self.assertEqual(logits.shape					,     _lowercase						)

								_snake_case     		=	torch.tensor([-1.2385, -1.0987, -1.0108]						).to(_lowercase						)

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

								_snake_case     		=	2_81
								self.assertEqual(logits.argmax(-1						).item()					,     _lowercase						)


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'						).to(
								    _lowercase						)

								_snake_case     		=	self.default_image_processor
								_snake_case     		=	prepare_img()
								_snake_case     		=	image_processor(images=_lowercase					,     return_tensors='pt'						).to(_lowercase						)

								# forward pass
								with torch.no_grad():
									_snake_case     		=	model(**_lowercase						)
								_snake_case     		=	outputs.logits

								# verify the logits
								_snake_case     		=	torch.Size((1, 2_18_41)						)
								self.assertEqual(logits.shape					,     _lowercase						)

								_snake_case     		=	torch.tensor([1.6881, -0.2787, 0.5901]						).to(_lowercase						)

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

								_snake_case     		=	23_96
								self.assertEqual(logits.argmax(-1						).item()					,     _lowercase						)


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
								_snake_case     		=	model.to(_lowercase						)

								_snake_case     		=	BeitImageProcessor(do_resize=_lowercase					,     size=6_40					,     do_center_crop=_lowercase						)

								_snake_case     		=	load_dataset('hf-internal-testing/fixtures_ade20k'					,     split='test'						)
								_snake_case     		=	Image.open(ds[0]['file']						)
								_snake_case     		=	image_processor(images=_lowercase					,     return_tensors='pt'						).to(_lowercase						)

								# forward pass
								with torch.no_grad():
									_snake_case     		=	model(**_lowercase						)
								_snake_case     		=	outputs.logits

								# verify the logits
								_snake_case     		=	torch.Size((1, 1_50, 1_60, 1_60)						)
								self.assertEqual(logits.shape					,     _lowercase						)

								_snake_case     		=	version.parse(PIL.__version__						) < version.parse('9.0.0'						)

								if is_pillow_less_than_a:
									_snake_case     		=	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     		=	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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
								_snake_case     		=	model.to(_lowercase						)

								_snake_case     		=	BeitImageProcessor(do_resize=_lowercase					,     size=6_40					,     do_center_crop=_lowercase						)

								_snake_case     		=	load_dataset('hf-internal-testing/fixtures_ade20k'					,     split='test'						)
								_snake_case     		=	Image.open(ds[0]['file']						)
								_snake_case     		=	image_processor(images=_lowercase					,     return_tensors='pt'						).to(_lowercase						)

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

								_snake_case     		=	outputs.logits.detach().cpu()

								_snake_case     		=	image_processor.post_process_semantic_segmentation(outputs=_lowercase					,     target_sizes=[(5_00, 3_00)]						)
								_snake_case     		=	torch.Size((5_00, 3_00)						)
								self.assertEqual(segmentation[0].shape					,     _lowercase						)

								_snake_case     		=	image_processor.post_process_semantic_segmentation(outputs=_lowercase						)
								_snake_case     		=	torch.Size((1_60, 1_60)						)
								self.assertEqual(segmentation[0].shape					,     _lowercase						)



'''simple docstring'''







import math







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * power_factor







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			float:
	if (
	    not isinstance(__A    ,       (int, float)			)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError('power_factor must be a valid float value between -1 and 1.'			)
	return apparent_power * math.sqrt(1 - power_factor**2			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

