
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		,     _snake_case		,     _snake_case=100		,     _snake_case=13		,     _snake_case=30		,     _snake_case=2		,     _snake_case=3		,     _snake_case=True		,     _snake_case=True		,     _snake_case=32		,     _snake_case=4		,     _snake_case=4		,     _snake_case=37		,     _snake_case="gelu"		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=10		,     _snake_case=0.02		,     _snake_case=3		,     _snake_case=None		,     _snake_case=[0, 1, 2, 3]		,     )    ->      List[str]:


								'''simple docstring'''




								__a										=    parent
								__a										=    100
								__a										=    batch_size
								__a										=    image_size
								__a										=    patch_size
								__a										=    num_channels
								__a										=    is_training
								__a										=    use_labels
								__a										=    hidden_size
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    intermediate_size
								__a										=    hidden_act
								__a										=    hidden_dropout_prob
								__a										=    attention_probs_dropout_prob
								__a										=    type_sequence_label_size
								__a										=    initializer_range
								__a										=    scope
								__a										=    out_indices
								__a										=    num_labels

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




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


								'''simple docstring'''




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

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

								__a										=    self.get_config()

								return config, pixel_values, labels, pixel_labels




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


								'''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=_snake_case		,     initializer_range=self.initializer_range		,     out_indices=self.out_indices		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




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




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


								'''simple docstring'''




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

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    self.prepare_config_and_inputs()
								__a							,     __a							,     __a							,     __a										=    config_and_inputs
								__a										=    {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class 			__A( a  ,   a  ,   unittest.TestCase			):
				snake_case_						   =    (
				    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				snake_case_						   =    (
				    {
				        '''feature-extraction''': BeitModel,
				        '''image-classification''': BeitForImageClassification,
				        '''image-segmentation''': BeitForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				snake_case_						   =    False
				snake_case_						   =    False
				snake_case_						   =    False




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


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								self.config_tester.run_common_tests()




				@unittest.skip(reason='''BEiT does not use inputs_embeds'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								__a							,     __a										=    self.model_tester.prepare_config_and_inputs_for_common()

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a							,     __a										=    self.model_tester.prepare_config_and_inputs_for_common()

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

												__a										=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]		,     _snake_case				)




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


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




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




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


								'''simple docstring'''




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




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


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								if not self.model_tester.is_training:
												return

								__a							,     __a										=    self.model_tester.prepare_config_and_inputs_for_common()
								__a										=    True

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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

								__a										=    False
								__a										=    True

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a							,     __a										=    self.model_tester.prepare_config_and_inputs_for_common()

								__a										=    _config_zero_init(_snake_case				)
								for model_class in self.all_model_classes:
												__a										=    model_class(config=_snake_case				)
												for name, param in model.named_parameters():
																# we skip lambda parameters as these require special initial values
																# determined by config.layer_scale_init_value
																if "lambda" in name:
																				continue
																if param.requires_grad:
																				self.assertIn(
																				    ((param.data.mean() * 1E9).round() / 1E9).item()		,     [0.0, 1.0]		,     msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )




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


								'''simple docstring'''




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



def     __lowerCAmelCase			(			)       ->  Optional[int]:
				__a										=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
				return image







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




				@cached_property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




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




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


								'''simple docstring'''




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

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

								# prepare bool_masked_pos
								__a										=    torch.ones((1, 196)		,     dtype=torch.bool				).to(_snake_case				)

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

								# verify the logits
								__a										=    torch.Size((1, 196, 8_192)				)
								self.assertEqual(logits.shape		,     _snake_case				)

								__a										=    torch.tensor(
								    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]				).to(_snake_case				)

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




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




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

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

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

								# verify the logits
								__a										=    torch.Size((1, 1_000)				)
								self.assertEqual(logits.shape		,     _snake_case				)

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

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

								__a										=    281
								self.assertEqual(logits.argmax(-1				).item()		,     _snake_case				)




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




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

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

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

								# verify the logits
								__a										=    torch.Size((1, 21_841)				)
								self.assertEqual(logits.shape		,     _snake_case				)

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

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

								__a										=    2_396
								self.assertEqual(logits.argmax(-1				).item()		,     _snake_case				)




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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

								__a										=    BeitImageProcessor(do_resize=_snake_case		,     size=640		,     do_center_crop=_snake_case				)

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

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

								# verify the logits
								__a										=    torch.Size((1, 150, 160, 160)				)
								self.assertEqual(logits.shape		,     _snake_case				)

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

								if is_pillow_less_than_a:
												__a										=    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=_snake_case		,     )
								else:
												__a										=    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=_snake_case		,     )

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




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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

								__a										=    BeitImageProcessor(do_resize=_snake_case		,     size=640		,     do_center_crop=_snake_case				)

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

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

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

								__a										=    image_processor.post_process_semantic_segmentation(outputs=_snake_case		,     target_sizes=[(500, 300)]				)
								__a										=    torch.Size((500, 300)				)
								self.assertEqual(segmentation[0].shape		,     _snake_case				)

								__a										=    image_processor.post_process_semantic_segmentation(outputs=_snake_case				)
								__a										=    torch.Size((160, 160)				)
								self.assertEqual(segmentation[0].shape		,     _snake_case				)

# flake8: noqa
# Lint as: python3
A     :	Optional[Any]    = [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental