




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    LevitForImageClassification,
			    LevitForImageClassificationWithTeacher,
			    LevitModel,
			)
			from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor






class 			__magic_name__    (	_UpperCamelCase):
				'''simple docstring'''







				def      _A				(				self: Dict		):
											SCREAMING_SNAKE_CASE_ 						=     self.config_class(**self.inputs_dict		)
											self.parent.assertTrue(hasattr(_UpperCAmelCase		,       '''hidden_sizes'''		)		)
											self.parent.assertTrue(hasattr(_UpperCAmelCase		,       '''num_attention_heads'''		)		)






class 			__magic_name__    :
				'''simple docstring'''







				def __init__(				self: Dict		,       _lowerCamelCase: Optional[int]		,       _lowerCamelCase: Optional[int]=13		,       _lowerCamelCase: Dict=64		,       _lowerCamelCase: List[Any]=3		,       _lowerCamelCase: Optional[int]=3		,       _lowerCamelCase: Tuple=2		,       _lowerCamelCase: List[Any]=1		,       _lowerCamelCase: Tuple=16		,       _lowerCamelCase: List[str]=[1_28, 2_56, 3_84]		,       _lowerCamelCase: str=[4, 6, 8]		,       _lowerCamelCase: str=[2, 3, 4]		,       _lowerCamelCase: Any=[16, 16, 16]		,       _lowerCamelCase: Dict=0		,       _lowerCamelCase: Tuple=[2, 2, 2]		,       _lowerCamelCase: List[Any]=[2, 2, 2]		,       _lowerCamelCase: List[Any]=0.02		,       _lowerCamelCase: List[str]=True		,       _lowerCamelCase: List[str]=True		,       _lowerCamelCase: Dict=2		,       ):
											SCREAMING_SNAKE_CASE_ 						=     parent
											SCREAMING_SNAKE_CASE_ 						=     batch_size
											SCREAMING_SNAKE_CASE_ 						=     image_size
											SCREAMING_SNAKE_CASE_ 						=     num_channels
											SCREAMING_SNAKE_CASE_ 						=     kernel_size
											SCREAMING_SNAKE_CASE_ 						=     stride
											SCREAMING_SNAKE_CASE_ 						=     padding
											SCREAMING_SNAKE_CASE_ 						=     hidden_sizes
											SCREAMING_SNAKE_CASE_ 						=     num_attention_heads
											SCREAMING_SNAKE_CASE_ 						=     depths
											SCREAMING_SNAKE_CASE_ 						=     key_dim
											SCREAMING_SNAKE_CASE_ 						=     drop_path_rate
											SCREAMING_SNAKE_CASE_ 						=     patch_size
											SCREAMING_SNAKE_CASE_ 						=     attention_ratio
											SCREAMING_SNAKE_CASE_ 						=     mlp_ratio
											SCREAMING_SNAKE_CASE_ 						=     initializer_range
											SCREAMING_SNAKE_CASE_ 						=     [
											    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
											    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
											]
											SCREAMING_SNAKE_CASE_ 						=     is_training
											SCREAMING_SNAKE_CASE_ 						=     use_labels
											SCREAMING_SNAKE_CASE_ 						=     num_labels
											SCREAMING_SNAKE_CASE_ 						=     initializer_range


				def      _A				(				self: Dict		):
											SCREAMING_SNAKE_CASE_ 						=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

											SCREAMING_SNAKE_CASE_ 						=     None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE_ 						=     ids_tensor([self.batch_size]		,       self.num_labels		)

											SCREAMING_SNAKE_CASE_ 						=     self.get_config()
											return config, pixel_values, labels


				def      _A				(				self: List[Any]		):
											return LevitConfig(
											    image_size=self.image_size		,       num_channels=self.num_channels		,       kernel_size=self.kernel_size		,       stride=self.stride		,       padding=self.padding		,       patch_size=self.patch_size		,       hidden_sizes=self.hidden_sizes		,       num_attention_heads=self.num_attention_heads		,       depths=self.depths		,       key_dim=self.key_dim		,       drop_path_rate=self.drop_path_rate		,       mlp_ratio=self.mlp_ratio		,       attention_ratio=self.attention_ratio		,       initializer_range=self.initializer_range		,       down_ops=self.down_ops		,       )


				def      _A				(				self: List[Any]		,       _lowerCamelCase: Tuple		,       _lowerCamelCase: Union[str, Any]		,       _lowerCamelCase: Optional[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     LevitModel(config=_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_ 						=     model(_UpperCAmelCase		)
											SCREAMING_SNAKE_CASE_ 						=     (self.image_size, self.image_size)
											SCREAMING_SNAKE_CASE_ 						=     image_size[0], image_size[1]
											for _ in range(4		):
																		SCREAMING_SNAKE_CASE_ 						=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
																		SCREAMING_SNAKE_CASE_ 						=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
											self.parent.assertEqual(
											    result.last_hidden_state.shape		,       (self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1])		,       )


				def      _A				(				self: Optional[Any]		,       _lowerCamelCase: str		,       _lowerCamelCase: Optional[Any]		,       _lowerCamelCase: List[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     self.num_labels
											SCREAMING_SNAKE_CASE_ 						=     LevitForImageClassification(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.eval()
											SCREAMING_SNAKE_CASE_ 						=     model(_UpperCAmelCase		,       labels=_UpperCAmelCase		)
											self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)		)





				def      _A				(				self: List[str]		):
											SCREAMING_SNAKE_CASE_ 						=     self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE_ 						=     config_and_inputs
											SCREAMING_SNAKE_CASE_ 						=     {'pixel_values': pixel_values}
											return config, inputs_dict






@require_torch
class 			__magic_name__    (	_UpperCamelCase     ,	_UpperCamelCase     ,	unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				str						   =       (
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__       :				Tuple						   =       (
				    {
				        'feature-extraction': LevitModel,
				        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE__       :				Dict						   =       False
				SCREAMING_SNAKE_CASE__       :				str						   =       False
				SCREAMING_SNAKE_CASE__       :				Optional[Any]						   =       False
				SCREAMING_SNAKE_CASE__       :				Union[str, Any]						   =       False
				SCREAMING_SNAKE_CASE__       :				List[str]						   =       False


				def      _A				(				self: Any		):
											SCREAMING_SNAKE_CASE_ 						=     LevitModelTester(self		)
											SCREAMING_SNAKE_CASE_ 						=     ConfigTester(self		,       config_class=_UpperCAmelCase		,       has_text_modality=_UpperCAmelCase		,       hidden_size=37		)


				def      _A				(				self: List[Any]		):
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()


				def      _A				(				self: Any		):
											return


				@unittest.skip(reason='''Levit does not use inputs_embeds'''		)
				def      _A				(				self: Optional[int]		):
											pass


				@unittest.skip(reason='''Levit does not support input and output embeddings'''		)
				def      _A				(				self: List[Any]		):
											pass


				@unittest.skip(reason='''Levit does not output attentions'''		)
				def      _A				(				self: str		):
											pass


				def      _A				(				self: Dict		):
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE_ 						=     ['pixel_values']
																		self.assertListEqual(arg_names[:1]		,       _UpperCAmelCase		)


				def      _A				(				self: Optional[Any]		):
											def check_hidden_states_output(_lowerCamelCase: Optional[Any]		,       _lowerCamelCase: str		,       _lowerCamelCase: Union[str, Any]		):
																		SCREAMING_SNAKE_CASE_ 						=     model_class(_UpperCAmelCase		)
																		model.to(_UpperCAmelCase		)
																		model.eval()

																		with torch.no_grad():
																									SCREAMING_SNAKE_CASE_ 						=     model(**self._prepare_for_class(_UpperCAmelCase		,       _UpperCAmelCase		)		)

																		SCREAMING_SNAKE_CASE_ 						=     outputs.hidden_states

																		SCREAMING_SNAKE_CASE_ 						=     len(self.model_tester.depths		) + 1
																		self.assertEqual(len(_UpperCAmelCase		)		,       _UpperCAmelCase		)

																		SCREAMING_SNAKE_CASE_ 						=     (self.model_tester.image_size, self.model_tester.image_size)
																		SCREAMING_SNAKE_CASE_ 						=     image_size[0], image_size[1]
																		for _ in range(4		):
																									SCREAMING_SNAKE_CASE_ 						=     floor(
																									    (
																									        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																									        / self.model_tester.stride
																									    )
																									    + 1		)
																									SCREAMING_SNAKE_CASE_ 						=     floor(
																									    (
																									        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																									        / self.model_tester.stride
																									    )
																									    + 1		)
																		# verify the first hidden states (first block)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]		)		,       [
																		        height * width,
																		        self.model_tester.hidden_sizes[0],
																		    ]		,       )

											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE_ 						=     True
																		check_hidden_states_output(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		SCREAMING_SNAKE_CASE_ 						=     True

																		check_hidden_states_output(_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)


				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
				def      _A				(				self: Optional[int]		):
											pass


				def      _A				(				self: Dict		,       _lowerCamelCase: List[str]		,       _lowerCamelCase: Union[str, Any]		,       _lowerCamelCase: Optional[int]=False		):
											SCREAMING_SNAKE_CASE_ 						=     super()._prepare_for_class(_UpperCAmelCase		,       _UpperCAmelCase		,       return_labels=_UpperCAmelCase		)

											if return_labels:
																		if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																									del inputs_dict["labels"]

											return inputs_dict


				def      _A				(				self: str		):
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_UpperCAmelCase		)


				def      _A				(				self: Dict		):
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)


				def      _A				(				self: Any		):
											if not self.model_tester.is_training:
																		return

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

											for model_class in self.all_model_classes:
																		# LevitForImageClassificationWithTeacher supports inference-only
																		if (
																		    model_class in get_values(_UpperCAmelCase		)
																		    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																		):
																									continue
																		SCREAMING_SNAKE_CASE_ 						=     model_class(_UpperCAmelCase		)
																		model.to(_UpperCAmelCase		)
																		model.train()
																		SCREAMING_SNAKE_CASE_ 						=     self._prepare_for_class(_UpperCAmelCase		,       _UpperCAmelCase		,       return_labels=_UpperCAmelCase		)
																		SCREAMING_SNAKE_CASE_ 						=     model(**_UpperCAmelCase		).loss
																		loss.backward()


				def      _A				(				self: Union[str, Any]		):
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																		return

											SCREAMING_SNAKE_CASE_ 						=     False
											SCREAMING_SNAKE_CASE_ 						=     True

											for model_class in self.all_model_classes:
																		if model_class in get_values(_UpperCAmelCase		) or not model_class.supports_gradient_checkpointing:
																									continue
																		# LevitForImageClassificationWithTeacher supports inference-only
																		if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																									continue
																		SCREAMING_SNAKE_CASE_ 						=     model_class(_UpperCAmelCase		)
																		model.gradient_checkpointing_enable()
																		model.to(_UpperCAmelCase		)
																		model.train()
																		SCREAMING_SNAKE_CASE_ 						=     self._prepare_for_class(_UpperCAmelCase		,       _UpperCAmelCase		,       return_labels=_UpperCAmelCase		)
																		SCREAMING_SNAKE_CASE_ 						=     model(**_UpperCAmelCase		).loss
																		loss.backward()


				def      _A				(				self: Optional[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     self.model_tester.prepare_config_and_inputs_for_common()

											SCREAMING_SNAKE_CASE_ 						=     [
											    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
											    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
											    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
											]

											for model_class in self.all_model_classes:
																		if (
																		    model_class
																		    not in [
																		        *get_values(_UpperCAmelCase		),
																		    ]
																		    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																		):
																									continue

																		for problem_type in problem_types:
																									with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"		):
																																SCREAMING_SNAKE_CASE_ 						=     problem_type['title']
																																SCREAMING_SNAKE_CASE_ 						=     problem_type['num_labels']

																																SCREAMING_SNAKE_CASE_ 						=     model_class(_UpperCAmelCase		)
																																model.to(_UpperCAmelCase		)
																																model.train()

																																SCREAMING_SNAKE_CASE_ 						=     self._prepare_for_class(_UpperCAmelCase		,       _UpperCAmelCase		,       return_labels=_UpperCAmelCase		)

																																if problem_type["num_labels"] > 1:
																																							SCREAMING_SNAKE_CASE_ 						=     inputs['labels'].unsqueeze(1		).repeat(1		,       problem_type['''num_labels''']		)

																																SCREAMING_SNAKE_CASE_ 						=     inputs['labels'].to(problem_type['''dtype''']		)

																																# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																																# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																																# they have the same size." which is a symptom something in wrong for the regression problem.
																																# See https://github.com/huggingface/transformers/issues/11780
																																with warnings.catch_warnings(record=_UpperCAmelCase		) as warning_list:
																																							SCREAMING_SNAKE_CASE_ 						=     model(**_UpperCAmelCase		).loss
																																for w in warning_list:
																																							if "Using a target size that is different to the input size" in str(w.message		):
																																														raise ValueError(
																																														    f"Something is going wrong in the regression problem: intercepted {w.message}"		)

																																loss.backward()





				@slow
				def      _A				(				self: Optional[int]		):
											for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE_ 						=     LevitModel.from_pretrained(_UpperCAmelCase		)
																		self.assertIsNotNone(_UpperCAmelCase		)






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






@require_torch
@require_vision
class 			__magic_name__    (	unittest.TestCase):
				'''simple docstring'''







				@cached_property
				def      _A				(				self: List[str]		):
											return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)





				@slow
				def      _A				(				self: List[Any]		):
											SCREAMING_SNAKE_CASE_ 						=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
											    _UpperCAmelCase		)

											SCREAMING_SNAKE_CASE_ 						=     self.default_image_processor
											SCREAMING_SNAKE_CASE_ 						=     prepare_img()
											SCREAMING_SNAKE_CASE_ 						=     image_processor(images=_UpperCAmelCase		,       return_tensors='''pt'''		).to(_UpperCAmelCase		)

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_ 						=     model(**_UpperCAmelCase		)

											# verify the logits
											SCREAMING_SNAKE_CASE_ 						=     torch.Size((1, 10_00)		)
											self.assertEqual(outputs.logits.shape		,       _UpperCAmelCase		)

											SCREAMING_SNAKE_CASE_ 						=     torch.tensor([1.04_48, -0.37_45, -1.83_17]		).to(_UpperCAmelCase		)

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







'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


if __name__ == "__main__":
							print(f"""{solution() = }""")




