



'''simple docstring'''




import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
						from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=64       ,      lowercase=2       ,      lowercase=3       ,      lowercase=True       ,      lowercase=True       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=10       ,      lowercase=0.02       ,      lowercase=[1, 16, 4, 4]       ,      lowercase=None       ,      ):
				A_	:						List[Any]	=     parent
				A_	:						List[Any]	=     batch_size
				A_	:						Union[str, Any]	=     image_size
				A_	:						Optional[Any]	=     patch_size
				A_	:						Any	=     num_channels
				A_	:						Optional[Any]	=     is_training
				A_	:						Optional[Any]	=     use_labels
				A_	:						Union[str, Any]	=     hidden_size
				A_	:						str	=     num_hidden_layers
				A_	:						Union[str, Any]	=     num_attention_heads
				A_	:						Optional[int]	=     intermediate_size
				A_	:						List[Any]	=     hidden_act
				A_	:						Optional[int]	=     hidden_dropout_prob
				A_	:						Union[str, Any]	=     attention_probs_dropout_prob
				A_	:						str	=     type_sequence_label_size
				A_	:						Any	=     initializer_range
				A_	:						List[str]	=     scope
				A_	:						Optional[int]	=     backbone_featmap_shape

				# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				# the number of patches is based on the feature map of the backbone, which by default uses an output stride
				# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
				A_	:						List[Any]	=     (self.image_size // 32) ** 2
				A_	:						List[str]	=     num_patches + 1


	def     _a	(self							):
				A_	:						str	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

				A_	:						Dict	=     self.get_config()

				return config, pixel_values, labels


	def     _a	(self							):
				A_	:						Any	=     {
				    """global_padding""": """same""",
				    """layer_type""": """bottleneck""",
				    """depths""": [3, 4, 9],
				    """out_features""": ["""stage1""", """stage2""", """stage3"""],
				    """embedding_dynamic_padding""": True,
				    """hidden_sizes""": [4, 8, 16, 32],
				    """num_groups""": 2,
				}

				return ViTHybridConfig(
				    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       ,      backbone_featmap_shape=self.backbone_featmap_shape       ,      backbone_config=lowercase       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						str	=     ViTHybridModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Union[str, Any]	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     self.type_sequence_label_size
				A_	:						List[Any]	=     ViTHybridForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						int	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.type_sequence_label_size)							)


	def     _a	(self							):
				A_	:						str	=     self.prepare_config_and_inputs()
				A_,    A_,    A_	:						Optional[int]	=     config_and_inputs
				A_	:						Dict	=     {"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Dict					=					(
	    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE   :		Tuple					=					False
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		Tuple					=					False


	def     _a	(self							):
				A_	:						List[str]	=     ViTHybridModelTester(self							)
				A_	:						Optional[Any]	=     ConfigTester(self       ,      config_class=lowercase       ,      has_text_modality=lowercase       ,      hidden_size=37							)


	def     _a	(self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""ViT does not use inputs_embeds"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				A_,    A_	:						Dict	=     self.model_tester.prepare_config_and_inputs_for_common()

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


	def     _a	(self							):
				A_,    A_	:						int	=     self.model_tester.prepare_config_and_inputs_for_common()

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

							A_	:						int	=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]       ,      lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						List[str]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()

				A_	:						List[Any]	=     _config_zero_init(lowercase							)
				for model_class in self.all_model_classes:
							A_	:						Any	=     model_class(config=lowercase							)
							# Skip the check for the backbone
							for name, module in model.named_modules():
										if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
													A_	:						Dict	=     [F'{name}.{key}' for key in module.state_dict().keys()]
													break

							for name, param in model.named_parameters():
										if param.requires_grad:
													if name in backbone_params:
																continue
													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     _a	(self							):
				for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						List[str]	=     ViTHybridModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)






def 	a			( ):
			'''simple docstring'''





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







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


	@cached_property
	def     _a	(self							):
				return (
				    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
				    if is_vision_available()
				    else None
				)


	@slow
	def     _a	(self							):
				A_	:						Dict	=     ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
				    lowercase							)

				A_	:						Optional[int]	=     self.default_image_processor
				A_	:						Any	=     prepare_img()
				A_	:						str	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_	:						List[Any]	=     model(**lowercase							)

				# verify the logits
				A_	:						Any	=     torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape       ,      lowercase							)

				A_	:						Union[str, Any]	=     torch.tensor([-1.90_90, -0.49_93, -0.23_89]							).to(lowercase							)

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


	@slow
	@require_accelerate
	def     _a	(self							):
				A_	:						int	=     ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""							)
				A_	:						Tuple	=     ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""       ,      device_map="""auto"""							)

				A_	:						Optional[int]	=     prepare_img()

				A_	:						List[str]	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							)
				A_	:						Optional[Any]	=     model(**lowercase							)
				A_	:						Optional[Any]	=     outputs.logits
				# model predicts one of the 1000 ImageNet classes
				A_	:						Dict	=     logits.argmax(-1							).item()

				self.assertTrue(model.config.idalabel[predicted_class_idx]       ,      """tabby, tabby cat"""							)




'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if num <= 0:
						A_	:						List[Any]	=     f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(lowerCamelCase__    )

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(lowerCamelCase__    )

									# Set multiples of start be False
									for i in range(start * start						,      num + 1						,      lowerCamelCase__    ):
												if sieve[i] is True:
															A_	:						List[Any]	=     False

						start += 1

			for j in range(end + 1						,      num + 1    ):
						if sieve[j] is True:
									prime.append(lowerCamelCase__    )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))