



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							:    Union[str, Any]				,	snake_case_							:    Any				,	snake_case_							:    List[str]=1_00				,	snake_case_							:    Optional[int]=13				,	snake_case_							:    Any=30				,	snake_case_							:    Tuple=2				,	snake_case_							:    str=3				,	snake_case_							:    Dict=True				,	snake_case_							:    Any=True				,	snake_case_							:    Optional[Any]=32				,	snake_case_							:    List[Any]=4				,	snake_case_							:    Optional[Any]=4				,	snake_case_							:    List[str]=37				,	snake_case_							:    str="gelu"				,	snake_case_							:    List[str]=0.1				,	snake_case_							:    str=0.1				,	snake_case_							:    Any=10				,	snake_case_							:    Dict=0.0_2				,	snake_case_							:    Optional[Any]=3				,	snake_case_							:    str=None				,	snake_case_							:    List[str]=[0, 1, 2, 3]				,	)->							Optional[Any]:
						__lowerCAmelCase	   =parent
						__lowerCAmelCase	   =1_00
						__lowerCAmelCase	   =batch_size
						__lowerCAmelCase	   =image_size
						__lowerCAmelCase	   =patch_size
						__lowerCAmelCase	   =num_channels
						__lowerCAmelCase	   =is_training
						__lowerCAmelCase	   =use_labels
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =num_hidden_layers
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =intermediate_size
						__lowerCAmelCase	   =hidden_act
						__lowerCAmelCase	   =hidden_dropout_prob
						__lowerCAmelCase	   =attention_probs_dropout_prob
						__lowerCAmelCase	   =type_sequence_label_size
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =scope
						__lowerCAmelCase	   =out_indices
						__lowerCAmelCase	   =num_labels

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



	def 		UpperCamelCase							(						self							:    str)->							str:
						__lowerCAmelCase	   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

						__lowerCAmelCase	   =self.get_config()

						return config, pixel_values, labels, pixel_labels



	def 		UpperCamelCase							(						self							:    Tuple)->							Optional[Any]:
						return BeitConfig(
						    vocab_size=self.vocab_size				,	image_size=self.image_size				,	patch_size=self.patch_size				,	num_channels=self.num_channels				,	hidden_size=self.hidden_size				,	num_hidden_layers=self.num_hidden_layers				,	num_attention_heads=self.num_attention_heads				,	intermediate_size=self.intermediate_size				,	hidden_act=self.hidden_act				,	hidden_dropout_prob=self.hidden_dropout_prob				,	attention_probs_dropout_prob=self.attention_probs_dropout_prob				,	is_decoder=_lowercase				,	initializer_range=self.initializer_range				,	out_indices=self.out_indices				,	)



	def 		UpperCamelCase							(						self							:    List[str]				,	snake_case_							:    Any				,	snake_case_							:    Optional[int]				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[Any])->							Any:
						__lowerCAmelCase	   =BeitModel(config=_lowercase)
						model.to(_lowercase)
						model.eval()
						__lowerCAmelCase	   =model(_lowercase)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))



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



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    List[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    int				,	snake_case_							:    Tuple)->							Union[str, Any]:
						__lowerCAmelCase	   =self.type_sequence_label_size
						__lowerCAmelCase	   =BeitForImageClassification(_lowercase)
						model.to(_lowercase)
						model.eval()
						__lowerCAmelCase	   =model(_lowercase				,	labels=_lowercase)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.type_sequence_label_size))

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

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



	def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Any				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    Optional[Any])->							Dict:
						__lowerCAmelCase	   =self.num_labels
						__lowerCAmelCase	   =BeitForSemanticSegmentation(_lowercase)
						model.to(_lowercase)
						model.eval()
						__lowerCAmelCase	   =model(_lowercase)
						self.parent.assertEqual(
						    result.logits.shape				,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
						__lowerCAmelCase	   =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							:    Any)->							int:
						__lowerCAmelCase	   =self.prepare_config_and_inputs()
						__lowerCAmelCase	   =config_and_inputs
						__lowerCAmelCase	   ={'pixel_values': pixel_values}
						return config, inputs_dict






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

	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       False



	def 		UpperCamelCase							(						self							:    Optional[int])->							int:
						__lowerCAmelCase	   =BeitModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=_lowercase				,	has_text_modality=_lowercase				,	hidden_size=37)



	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						self.config_tester.run_common_tests()



	@unittest.skip(reason="""BEiT does not use inputs_embeds""")
	def 		UpperCamelCase							(						self							:    int)->							Union[str, Any]:
						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])->							Union[str, Any]:
						pass



	def 		UpperCamelCase							(						self							:    List[Any])->							Dict:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()

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



	def 		UpperCamelCase							(						self							:    str)->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCAmelCase	   =['pixel_values']
											self.assertListEqual(arg_names[:1]				,	_lowercase)



	def 		UpperCamelCase							(						self							:    Optional[Any])->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_lowercase)



	def 		UpperCamelCase							(						self							:    int)->							Optional[Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*_lowercase)



	def 		UpperCamelCase							(						self							:    Dict)->							List[Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*_lowercase)



	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase)



	def 		UpperCamelCase							(						self							:    str)->							Optional[Any]:
						if not self.model_tester.is_training:
											return

						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase	   =True

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

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



	def 		UpperCamelCase							(						self							:    str)->							Optional[int]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
											return

						__lowerCAmelCase	   =False
						__lowerCAmelCase	   =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

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



	def 		UpperCamelCase							(						self							:    Tuple)->							Dict:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCAmelCase	   =_config_zero_init(_lowercase)
						for model_class in self.all_model_classes:
											__lowerCAmelCase	   =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])->							List[Any]:
						for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase	   =BeitModel.from_pretrained(_lowercase)
											self.assertIsNotNone(_lowercase)






def 					__lowerCAmelCase		(  )  ->						List[Any]:
					__lowerCAmelCase	   =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
					return image






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



	@cached_property
	def 		UpperCamelCase							(						self							:    Optional[Any])->							Dict:
						return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""") if is_vision_available() else None



	@slow
	def 		UpperCamelCase							(						self							:    List[Any])->							int:
						__lowerCAmelCase	   =BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""").to(_lowercase)

						__lowerCAmelCase	   =self.default_image_processor
						__lowerCAmelCase	   =prepare_img()
						__lowerCAmelCase	   =image_processor(images=_lowercase				,	return_tensors="""pt""").pixel_values.to(_lowercase)

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

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

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

						__lowerCAmelCase	   =torch.tensor(
						    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]).to(_lowercase)

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



	@slow
	def 		UpperCamelCase							(						self							:    Optional[Any])->							List[str]:
						__lowerCAmelCase	   =BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""").to(_lowercase)

						__lowerCAmelCase	   =self.default_image_processor
						__lowerCAmelCase	   =prepare_img()
						__lowerCAmelCase	   =image_processor(images=_lowercase				,	return_tensors="""pt""").to(_lowercase)

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

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

						__lowerCAmelCase	   =torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]).to(_lowercase)

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

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



	@slow
	def 		UpperCamelCase							(						self							:    List[str])->							int:
						__lowerCAmelCase	   =BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""").to(
						    _lowercase)

						__lowerCAmelCase	   =self.default_image_processor
						__lowerCAmelCase	   =prepare_img()
						__lowerCAmelCase	   =image_processor(images=_lowercase				,	return_tensors="""pt""").to(_lowercase)

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

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

						__lowerCAmelCase	   =torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]).to(_lowercase)

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

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



	@slow
	def 		UpperCamelCase							(						self							:    Union[str, Any])->							int:
						__lowerCAmelCase	   =BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""")
						__lowerCAmelCase	   =model.to(_lowercase)

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

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

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

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

						__lowerCAmelCase	   =version.parse(PIL.__version__) < version.parse("""9.0.0""")

						if is_pillow_less_than_a:
											__lowerCAmelCase	   =torch.tensor(
											    [
											        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
											        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
											        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
											    ]				,	device=_lowercase				,	)
						else:
											__lowerCAmelCase	   =torch.tensor(
											    [
											        [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
											        [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
											        [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
											    ]				,	device=_lowercase				,	)

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



	@slow
	def 		UpperCamelCase							(						self							:    int)->							Optional[int]:
						__lowerCAmelCase	   =BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""")
						__lowerCAmelCase	   =model.to(_lowercase)

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

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

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

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

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

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








def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list:
					__lowerCAmelCase	   =int(__lowerCamelCase	)
					if n_element < 1:
										__lowerCAmelCase	   =ValueError("""a should be a positive number"""	)
										raise my_error

					__lowerCAmelCase	   =[1]
					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =(0, 0, 0)
					__lowerCAmelCase	   =1
					while index < n_element:
										while hamming_list[i] * 2 <= hamming_list[-1]:
															i += 1
										while hamming_list[j] * 3 <= hamming_list[-1]:
															j += 1
										while hamming_list[k] * 5 <= hamming_list[-1]:
															k += 1
										hamming_list.append(
										    min(hamming_list[i] * 2				,   hamming_list[j] * 3				,   hamming_list[k] * 5	)	)
										index += 1
					return hamming_list


if __name__ == "__main__":
				lowercase_	       =			input('''Enter the last number (nth term) of the Hamming Number Series: ''')
				print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
				lowercase_	       =			hamming(int(n))
				print('''-----------------------------------------------------''')
				print(F"The list with nth numbers is: {hamming_numbers}")
				print('''-----------------------------------------------------''')




