"""simple docstring"""





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTMAEForPreTraining, ViTMAEModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_3		,			a=3_0		,			a=2		,			a=3		,			a=True		,			a=True		,			a=3_2		,			a=5		,			a=4		,			a=3_7		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=1_0		,			a=0.02		,			a=3		,			a=0.6		,			a=None		,			)       ->					List[Any]:
             lowercase__			:		Union[str, Any]       =	parent
             lowercase__			:		Union[str, Any]       =	batch_size
             lowercase__			:		Any       =	image_size
             lowercase__			:		Union[str, Any]       =	patch_size
             lowercase__			:		Any       =	num_channels
             lowercase__			:		Optional[int]       =	is_training
             lowercase__			:		List[Any]       =	use_labels
             lowercase__			:		List[str]       =	hidden_size
             lowercase__			:		Union[str, Any]       =	num_hidden_layers
             lowercase__			:		List[Any]       =	num_attention_heads
             lowercase__			:		str       =	intermediate_size
             lowercase__			:		Optional[int]       =	hidden_act
             lowercase__			:		int       =	hidden_dropout_prob
             lowercase__			:		List[Any]       =	attention_probs_dropout_prob
             lowercase__			:		Union[str, Any]       =	type_sequence_label_size
             lowercase__			:		Optional[Any]       =	initializer_range
             lowercase__			:		Any       =	mask_ratio
             lowercase__			:		Any       =	scope

             # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
             # (we add 1 for the [CLS] token)
             lowercase__			:		Union[str, Any]       =	(image_size // patch_size) ** 2
             lowercase__			:		List[Any]       =	int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		str       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__			:		int       =	None
             if self.use_labels:
                    lowercase__			:		int       =	ids_tensor([self.batch_size]		,			self.type_sequence_label_size							)

             lowercase__			:		Optional[int]       =	self.get_config()

             return config, pixel_values, labels


      def 		_UpperCAmelCase       (   self							)       ->					str:
             return ViTMAEConfig(
                 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=lowerCAmelCase__		,			initializer_range=self.initializer_range		,			mask_ratio=self.mask_ratio		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	ViTMAEModel(config=lowerCAmelCase__							)
             model.to(lowerCAmelCase__							)
             model.eval()
             lowercase__			:		List[Any]       =	model(lowerCAmelCase__							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[str]:
             lowercase__			:		int       =	ViTMAEForPreTraining(lowerCAmelCase__							)
             model.to(lowerCAmelCase__							)
             model.eval()
             lowercase__			:		List[Any]       =	model(lowerCAmelCase__							)
             lowercase__			:		Union[str, Any]       =	(self.image_size // self.patch_size) ** 2
             lowercase__			:		str       =	self.patch_size**2 * self.num_channels
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, num_patches, expected_num_channels)							)

             # test greyscale images
             lowercase__			:		Union[str, Any]       =	1
             lowercase__			:		List[str]       =	ViTMAEForPreTraining(lowerCAmelCase__							)
             model.to(lowerCAmelCase__							)
             model.eval()
             lowercase__			:		Optional[Any]       =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             lowercase__			:		Tuple       =	model(lowerCAmelCase__							)
             lowercase__			:		int       =	self.patch_size**2
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, num_patches, expected_num_channels)							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[Any]       =	self.prepare_config_and_inputs()
             lowercase__			:		Union[str, Any]       =	config_and_inputs
             lowercase__			:		Optional[int]       =	{"""pixel_values""": pixel_values}
             return config, inputs_dict





@require_torch
class      UpperCAmelCase_	(    lowerCamelCase__ ,							lowerCamelCase__ ,							unittest.TestCase):
      lowerCamelCase__		:    List[str]   =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      lowerCamelCase__		:    str   =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

      lowerCamelCase__		:    List[Any]   =  False
      lowerCamelCase__		:    Tuple   =  False
      lowerCamelCase__		:    Any   =  False
      lowerCamelCase__		:    Union[str, Any]   =  False


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		List[Any]       =	ViTMAEModelTester(self							)
             lowercase__			:		Optional[Any]       =	ConfigTester(self		,			config_class=lowerCAmelCase__		,			has_text_modality=lowerCAmelCase__		,			hidden_size=3_7							)


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


      @unittest.skip(reason='ViTMAE does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					str:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

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


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		List[Any]       =	model_class(lowerCAmelCase__							)
                    lowercase__			:		str       =	inspect.signature(model.forward							)
                    # signature.parameters is an OrderedDict => so arg_names order is deterministic
                    lowercase__			:		Tuple       =	[*signature.parameters.keys()]

                    lowercase__			:		Tuple       =	["""pixel_values"""]
                    self.assertListEqual(arg_names[:1]		,			lowerCAmelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowerCAmelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		str       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					List[Any]:
             np.random.seed(2							)

             lowercase__			:		str       =	int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
             lowercase__			:		List[Any]       =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
             lowercase__			:		Union[str, Any]       =	torch.from_numpy(lowerCAmelCase__							)

             # Add `noise` argument.
             # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
             lowercase__			:		Optional[int]       =	pt_noise

             super().check_pt_tf_models(lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__			:		Union[str, Any]       =	model_class(lowerCAmelCase__							)
                    model.to(lowerCAmelCase__							)
                    model.eval()
                    # make random mask reproducible
                    torch.manual_seed(2							)
                    with torch.no_grad():
                           lowercase__			:		List[str]       =	model(**self._prepare_for_class(lowerCAmelCase__		,			lowerCAmelCase__							)							)

                    lowercase__			:		Dict       =	outputs[0].cpu().numpy()
                    lowercase__			:		str       =	0

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(lowerCAmelCase__							)
                           lowercase__			:		int       =	model_class.from_pretrained(lowerCAmelCase__							)
                           model.to(lowerCAmelCase__							)
                           # make random mask reproducible
                           torch.manual_seed(2							)
                           with torch.no_grad():
                                  lowercase__			:		Dict       =	model(**self._prepare_for_class(lowerCAmelCase__		,			lowerCAmelCase__							)							)

                           # Make sure we don't have nans
                           lowercase__			:		int       =	after_outputs[0].cpu().numpy()
                           lowercase__			:		Optional[int]       =	0
                           lowercase__			:		List[Any]       =	np.amax(np.abs(out_a - out_a							)							)
                           self.assertLessEqual(lowerCAmelCase__		,			1e-5							)


      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'							)
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             pass


      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'							)
      def 		_UpperCAmelCase       (   self							)       ->					str:
             pass


      @unittest.skip(
          reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'							)
      def 		_UpperCAmelCase       (   self							)       ->					int:
             pass


      @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'							)
      def 		_UpperCAmelCase       (   self							)       ->					str:
             pass


      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             pass


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					str:






             for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__			:		List[Any]       =	ViTMAEModel.from_pretrained(lowerCAmelCase__							)
                    self.assertIsNotNone(lowerCAmelCase__							)





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		Optional[int]       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image






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


      @cached_property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'							) if is_vision_available() else None


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             np.random.seed(2							)

             lowercase__			:		str       =	ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'							).to(lowerCAmelCase__							)

             lowercase__			:		List[Any]       =	self.default_image_processor
             lowercase__			:		Tuple       =	prepare_img()
             lowercase__			:		int       =	image_processor(images=lowerCAmelCase__		,			return_tensors='pt'							).to(lowerCAmelCase__							)

             # prepare a noise vector that will be also used for testing the TF model
             # (this way we can ensure that the PT and TF models operate on the same inputs)
             lowercase__			:		Tuple       =	ViTMAEConfig()
             lowercase__			:		Optional[int]       =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
             lowercase__			:		List[Any]       =	np.random.uniform(size=(1, num_patches)							)

             # forward pass
             with torch.no_grad():
                    lowercase__			:		List[Any]       =	model(**lowerCAmelCase__		,			noise=torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)							)

             # verify the logits
             lowercase__			:		Dict       =	torch.Size((1, 1_9_6, 7_6_8)							)
             self.assertEqual(outputs.logits.shape		,			lowerCAmelCase__							)

             lowercase__			:		List[Any]       =	torch.tensor(
                 [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]							)

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






'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)
		if n_element < 1:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		ValueError("""a should be a positive number"""					)
				raise my_error

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		[1]
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(0, 0, 0)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		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__":
						UpperCamelCase__       :	Optional[Any]    = input('''Enter the last number (nth term) of the Hamming Number Series: ''')
						print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
						UpperCamelCase__       :	List[str]    = hamming(int(n))
						print('''-----------------------------------------------------''')
						print(f"The list with nth numbers is: {hamming_numbers}")
						print('''-----------------------------------------------------''')