



"""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_      :



						"""simple docstring"""






						def __init__( self			:Union[str, Any]       ,  lowercase_			:List[str]       ,  lowercase_			:List[Any]=1_00       ,  lowercase_			:int=13       ,  lowercase_			:str=30       ,  lowercase_			:Tuple=2       ,  lowercase_			:List[str]=3       ,  lowercase_			:List[str]=True       ,  lowercase_			:Any=True       ,  lowercase_			:List[Any]=32       ,  lowercase_			:Union[str, Any]=4       ,  lowercase_			:int=4       ,  lowercase_			:List[Any]=37       ,  lowercase_			:Optional[Any]="gelu"       ,  lowercase_			:Any=0.1       ,  lowercase_			:Optional[Any]=0.1       ,  lowercase_			:Dict=10       ,  lowercase_			:Dict=0.02       ,  lowercase_			:Optional[Any]=3       ,  lowercase_			:str=None       ,  lowercase_			:Tuple=[0, 1, 2, 3]       ,  )    ->      Union[str, Any]:
												UpperCAmelCase					=			parent
												UpperCAmelCase					=			1_00
												UpperCAmelCase					=			batch_size
												UpperCAmelCase					=			image_size
												UpperCAmelCase					=			patch_size
												UpperCAmelCase					=			num_channels
												UpperCAmelCase					=			is_training
												UpperCAmelCase					=			use_labels
												UpperCAmelCase					=			hidden_size
												UpperCAmelCase					=			num_hidden_layers
												UpperCAmelCase					=			num_attention_heads
												UpperCAmelCase					=			intermediate_size
												UpperCAmelCase					=			hidden_act
												UpperCAmelCase					=			hidden_dropout_prob
												UpperCAmelCase					=			attention_probs_dropout_prob
												UpperCAmelCase					=			type_sequence_label_size
												UpperCAmelCase					=			initializer_range
												UpperCAmelCase					=			scope
												UpperCAmelCase					=			out_indices
												UpperCAmelCase					=			num_labels

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






						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												UpperCAmelCase					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

												UpperCAmelCase					=			self.get_config()

												return config, pixel_values, labels, pixel_labels






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      str:
												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			:Tuple       ,  lowercase_			:Any       ,  lowercase_			:List[Any]       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Optional[Any] )    ->      int:
												UpperCAmelCase					=			BeitModel(config=lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_ )
												self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size) )






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Optional[Any]       ,  lowercase_			:str       ,  lowercase_			:Any       ,  lowercase_			:Union[str, Any] )    ->      List[str]:
												UpperCAmelCase					=			BeitForMaskedImageModeling(config=lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_ )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length - 1, self.vocab_size) )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Optional[int]       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:int       ,  lowercase_			:str )    ->      List[str]:
												UpperCAmelCase					=			self.type_sequence_label_size
												UpperCAmelCase					=			BeitForImageClassification(lowercase_ )
												model.to(lowercase_ )
												model.eval()
												UpperCAmelCase					=			model(lowercase_       ,  labels=lowercase_ )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size) )

												# test greyscale images
												UpperCAmelCase					=			1
												UpperCAmelCase					=			BeitForImageClassification(lowercase_ )
												model.to(lowercase_ )
												model.eval()

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






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







@require_torch
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							(
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						__UpperCamelCase		    =							(
						    {
						        """feature-extraction""": BeitModel,
						        """image-classification""": BeitForImageClassification,
						        """image-segmentation""": BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						__UpperCamelCase		    =							False
						__UpperCamelCase		    =							False
						__UpperCamelCase		    =							False






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Any:
												UpperCAmelCase					=			BeitModelTester(self )
												UpperCAmelCase					=			ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_       ,  hidden_size=37 )






						def      UpperCAmelCase__					( self			:List[Any] )    ->      Dict:
												self.config_tester.run_common_tests()






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






						def      UpperCAmelCase__					( self			:Tuple )    ->      Any:
												UpperCAmelCase				,   UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs_for_common()

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






						def      UpperCAmelCase__					( self			:Any )    ->      int:
												UpperCAmelCase				,   UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCAmelCase					=			['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,  lowercase_ )






						def      UpperCAmelCase__					( self			:List[Any] )    ->      int:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*lowercase_ )






						def      UpperCAmelCase__					( self			:Dict )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      Optional[int]:
												UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      Any:
												if not self.model_tester.is_training:
																		return

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

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

																		UpperCAmelCase					=			model_class(lowercase_ )
																		model.to(lowercase_ )
																		model.train()
																		UpperCAmelCase					=			self._prepare_for_class(lowercase_       ,  lowercase_       ,  return_labels=lowercase_ )
																		UpperCAmelCase					=			model(**lowercase_ ).loss
																		loss.backward()






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
												UpperCAmelCase				,   UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																		return

												UpperCAmelCase					=			False
												UpperCAmelCase					=			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

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






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      int:
												UpperCAmelCase				,   UpperCAmelCase					=			self.model_tester.prepare_config_and_inputs_for_common()

												UpperCAmelCase					=			_config_zero_init(lowercase_ )
												for model_class in self.all_model_classes:
																		UpperCAmelCase					=			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			:List[str] )    ->      Union[str, Any]:
												for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase					=			BeitModel.from_pretrained(lowercase_ )
																		self.assertIsNotNone(lowercase_ )


def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						return image







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



						"""simple docstring"""






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






						@slow
						def      UpperCAmelCase__					( self			:Tuple )    ->      int:
												UpperCAmelCase					=			BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(lowercase_ )

												UpperCAmelCase					=			self.default_image_processor
												UpperCAmelCase					=			prepare_img()
												UpperCAmelCase					=			image_processor(images=lowercase_       ,  return_tensors='pt' ).pixel_values.to(lowercase_ )

												# prepare bool_masked_pos
												UpperCAmelCase					=			torch.ones((1, 1_96)       ,  dtype=torch.bool ).to(lowercase_ )

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

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

												UpperCAmelCase					=			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			:str )    ->      List[str]:
												UpperCAmelCase					=			BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(lowercase_ )

												UpperCAmelCase					=			self.default_image_processor
												UpperCAmelCase					=			prepare_img()
												UpperCAmelCase					=			image_processor(images=lowercase_       ,  return_tensors='pt' ).to(lowercase_ )

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase					=			model(**lowercase_ )
												UpperCAmelCase					=			outputs.logits

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

												UpperCAmelCase					=			torch.tensor([-1.2385, -1.0987, -1.0108] ).to(lowercase_ )

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

												UpperCAmelCase					=			2_81
												self.assertEqual(logits.argmax(-1 ).item()       ,  lowercase_ )






						@slow
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												UpperCAmelCase					=			BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to(
												    lowercase_ )

												UpperCAmelCase					=			self.default_image_processor
												UpperCAmelCase					=			prepare_img()
												UpperCAmelCase					=			image_processor(images=lowercase_       ,  return_tensors='pt' ).to(lowercase_ )

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase					=			model(**lowercase_ )
												UpperCAmelCase					=			outputs.logits

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

												UpperCAmelCase					=			torch.tensor([1.6881, -0.2787, 0.5901] ).to(lowercase_ )

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

												UpperCAmelCase					=			23_96
												self.assertEqual(logits.argmax(-1 ).item()       ,  lowercase_ )






						@slow
						def      UpperCAmelCase__					( self			:Tuple )    ->      Any:
												UpperCAmelCase					=			BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
												UpperCAmelCase					=			model.to(lowercase_ )

												UpperCAmelCase					=			BeitImageProcessor(do_resize=lowercase_       ,  size=6_40       ,  do_center_crop=lowercase_ )

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

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase					=			model(**lowercase_ )
												UpperCAmelCase					=			outputs.logits

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

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

												if is_pillow_less_than_a:
																		UpperCAmelCase					=			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:
																		UpperCAmelCase					=			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			:Tuple )    ->      List[Any]:
												UpperCAmelCase					=			BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' )
												UpperCAmelCase					=			model.to(lowercase_ )

												UpperCAmelCase					=			BeitImageProcessor(do_resize=lowercase_       ,  size=6_40       ,  do_center_crop=lowercase_ )

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

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase					=			model(**lowercase_ )

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

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

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










"""simple docstring"""


def 			_lowerCAmelCase				(       lowercase_						):

						if not isinstance(lowercase_		,     lowercase_						):
												UpperCAmelCase					=			F"""Input value of [number={number}] must be an integer"""
												raise TypeError(lowercase_						)

						if number < 1:
												UpperCAmelCase					=			F"""Input value of [number={number}] must be > 0"""
												raise ValueError(lowercase_						)

						UpperCAmelCase					=			1

						for i in range(1		,     lowercase_						):
												current_number *= 4 * i - 2
												current_number //= i + 1

						return current_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()





