



"""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    _lowerCAmelCase    :





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_0_0							,    _lowercase=1_3							,    _lowercase=3_0							,    _lowercase=2							,    _lowercase=3							,    _lowercase=True							,    _lowercase=True							,    _lowercase=3_2							,    _lowercase=4							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=1_0							,    _lowercase=0.02							,    _lowercase=3							,    _lowercase=None							,    _lowercase=[0, 1, 2, 3]							,    ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			parent
								snake_case_   : str						=			1_0_0
								snake_case_   : str						=			batch_size
								snake_case_   : Dict						=			image_size
								snake_case_   : Union[str, Any]						=			patch_size
								snake_case_   : Any						=			num_channels
								snake_case_   : List[str]						=			is_training
								snake_case_   : List[str]						=			use_labels
								snake_case_   : Any						=			hidden_size
								snake_case_   : Tuple						=			num_hidden_layers
								snake_case_   : Dict						=			num_attention_heads
								snake_case_   : str						=			intermediate_size
								snake_case_   : List[Any]						=			hidden_act
								snake_case_   : Union[str, Any]						=			hidden_dropout_prob
								snake_case_   : int						=			attention_probs_dropout_prob
								snake_case_   : Any						=			type_sequence_label_size
								snake_case_   : List[str]						=			initializer_range
								snake_case_   : Optional[int]						=			scope
								snake_case_   : Dict						=			out_indices
								snake_case_   : int						=			num_labels

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





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : str						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

								snake_case_   : Tuple						=			self.get_config()

								return config, pixel_values, labels, pixel_labels





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''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    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Any						=			BeitModel(config=_lowercase   )
								model.to(_lowercase   )
								model.eval()
								snake_case_   : Optional[int]						=			model(_lowercase   )
								self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Dict						=			BeitForMaskedImageModeling(config=_lowercase   )
								model.to(_lowercase   )
								model.eval()
								snake_case_   : Optional[Any]						=			model(_lowercase   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length - 1, self.vocab_size)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			self.type_sequence_label_size
								snake_case_   : Dict						=			BeitForImageClassification(_lowercase   )
								model.to(_lowercase   )
								model.eval()
								snake_case_   : int						=			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_   : Union[str, Any]						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
								snake_case_   : Optional[int]						=			model(_lowercase							,    labels=_lowercase   )
								self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.type_sequence_label_size)   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

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







								'''simple docstring'''

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




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





		"""simple docstring"""






		_lowerCamelCase			  =       (
		    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		_lowerCamelCase			  =       (
		    {
		        '''feature-extraction''': BeitModel,
		        '''image-classification''': BeitForImageClassification,
		        '''image-segmentation''': BeitForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			BeitModelTester(self   )
								snake_case_   : str						=			ConfigTester(self							,    config_class=_lowercase							,    has_text_modality=_lowercase							,    hidden_size=3_7   )





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								self.config_tester.run_common_tests()





		@unittest.skip(reason="""BEiT does not use inputs_embeds"""   )
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''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    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_			,		snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs_for_common()

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





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_			,		snake_case_   : Any						=			self.model_tester.prepare_config_and_inputs_for_common()

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

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





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Any						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : List[str]						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Any						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								if not self.model_tester.is_training:
														return

								snake_case_			,		snake_case_   : List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_   : Tuple						=			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_   : Optional[int]						=			model_class(_lowercase   )
														model.to(_lowercase   )
														model.train()
														snake_case_   : Tuple						=			self._prepare_for_class(_lowercase							,    _lowercase							,    return_labels=_lowercase   )
														snake_case_   : str						=			model(**_lowercase   ).loss
														loss.backward()





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_			,		snake_case_   : 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[str]						=			True

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

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





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_			,		snake_case_   : List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

								snake_case_   : int						=			_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    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_   : Union[str, Any]						=			BeitModel.from_pretrained(_lowercase   )
														self.assertIsNotNone(_lowercase   )




def 	__lowerCAmelCase						(		):


						'''simple docstring'''






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






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





		"""simple docstring"""






		@cached_property
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

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





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : List[str]						=			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_   : Optional[int]						=			image_processor(images=_lowercase							,    return_tensors="""pt"""   ).pixel_values.to(_lowercase   )

								# prepare bool_masked_pos
								snake_case_   : Optional[Any]						=			torch.ones((1, 1_9_6)							,    dtype=torch.bool   ).to(_lowercase   )

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

								# verify the logits
								snake_case_   : str						=			torch.Size((1, 1_9_6, 8_1_9_2)   )
								self.assertEqual(logits.shape							,    _lowercase   )

								snake_case_   : int						=			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    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Tuple						=			BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""   ).to(_lowercase   )

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

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

								# verify the logits
								snake_case_   : Optional[Any]						=			torch.Size((1, 1_0_0_0)   )
								self.assertEqual(logits.shape							,    _lowercase   )

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

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

								snake_case_   : int						=			2_8_1
								self.assertEqual(logits.argmax(-1   ).item()							,    _lowercase   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : str						=			BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""   ).to(
								    _lowercase   )

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

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

								# verify the logits
								snake_case_   : List[str]						=			torch.Size((1, 2_1_8_4_1)   )
								self.assertEqual(logits.shape							,    _lowercase   )

								snake_case_   : Union[str, 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						=			2_3_9_6
								self.assertEqual(logits.argmax(-1   ).item()							,    _lowercase   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : str						=			BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""   )
								snake_case_   : int						=			model.to(_lowercase   )

								snake_case_   : Any						=			BeitImageProcessor(do_resize=_lowercase							,    size=6_4_0							,    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_   : Any						=			image_processor(images=_lowercase							,    return_tensors="""pt"""   ).to(_lowercase   )

								# forward pass
								with torch.no_grad():
														snake_case_   : List[str]						=			model(**_lowercase   )
								snake_case_   : str						=			outputs.logits

								# verify the logits
								snake_case_   : Optional[Any]						=			torch.Size((1, 1_5_0, 1_6_0, 1_6_0)   )
								self.assertEqual(logits.shape							,    _lowercase   )

								snake_case_   : int						=			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[str]						=			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    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Dict						=			BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""   )
								snake_case_   : Optional[Any]						=			model.to(_lowercase   )

								snake_case_   : List[Any]						=			BeitImageProcessor(do_resize=_lowercase							,    size=6_4_0							,    do_center_crop=_lowercase   )

								snake_case_   : Tuple						=			load_dataset("""hf-internal-testing/fixtures_ade20k"""							,    split="""test"""   )
								snake_case_   : Any						=			Image.open(ds[0]["""file"""]   )
								snake_case_   : Optional[int]						=			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.detach().cpu()

								snake_case_   : List[str]						=			image_processor.post_process_semantic_segmentation(outputs=_lowercase							,    target_sizes=[(5_0_0, 3_0_0)]   )
								snake_case_   : int						=			torch.Size((5_0_0, 3_0_0)   )
								self.assertEqual(segmentation[0].shape							,    _lowercase   )

								snake_case_   : List[str]						=			image_processor.post_process_semantic_segmentation(outputs=_lowercase   )
								snake_case_   : Optional[Any]						=			torch.Size((1_6_0, 1_6_0)   )
								self.assertEqual(segmentation[0].shape							,    _lowercase   )






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						return " ".join(input_str.split()[::-1]    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

