



"""simple docstring"""
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 							lowerCamelCase_(   A__    ):





				'''simple docstring'''



				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.config_class(**self.inputs_dict   )
								self.parent.assertTrue(hasattr(A__				,       '''hidden_sizes'''   )   )
								self.parent.assertTrue(hasattr(A__				,       '''num_attention_heads'''   )   )


class 							lowerCamelCase_:





				'''simple docstring'''



				def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=1_3				,       lowerCamelCase__=6_4				,       lowerCamelCase__=3				,       lowerCamelCase__=3				,       lowerCamelCase__=2				,       lowerCamelCase__=1				,       lowerCamelCase__=1_6				,       lowerCamelCase__=[1_2_8, 2_5_6, 3_8_4]				,       lowerCamelCase__=[4, 6, 8]				,       lowerCamelCase__=[2, 3, 4]				,       lowerCamelCase__=[1_6, 1_6, 1_6]				,       lowerCamelCase__=0				,       lowerCamelCase__=[2, 2, 2]				,       lowerCamelCase__=[2, 2, 2]				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=2				,       ):
								_lowerCamelCase        =				parent
								_lowerCamelCase        =				batch_size
								_lowerCamelCase        =				image_size
								_lowerCamelCase        =				num_channels
								_lowerCamelCase        =				kernel_size
								_lowerCamelCase        =				stride
								_lowerCamelCase        =				padding
								_lowerCamelCase        =				hidden_sizes
								_lowerCamelCase        =				num_attention_heads
								_lowerCamelCase        =				depths
								_lowerCamelCase        =				key_dim
								_lowerCamelCase        =				drop_path_rate
								_lowerCamelCase        =				patch_size
								_lowerCamelCase        =				attention_ratio
								_lowerCamelCase        =				mlp_ratio
								_lowerCamelCase        =				initializer_range
								_lowerCamelCase        =				[
								    ["""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],
								]
								_lowerCamelCase        =				is_training
								_lowerCamelCase        =				use_labels
								_lowerCamelCase        =				num_labels
								_lowerCamelCase        =				initializer_range

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

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

				def 			snake_case__      (    self   ):
								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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
								_lowerCamelCase        =				LevitModel(config=A__   )
								model.to(A__   )
								model.eval()
								_lowerCamelCase        =				model(A__   )
								_lowerCamelCase        =				(self.image_size, self.image_size)
								_lowerCamelCase        =				image_size[0], image_size[1]
								for _ in range(4   ):
												_lowerCamelCase        =				floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1   )
												_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
								_lowerCamelCase        =				self.num_labels
								_lowerCamelCase        =				LevitForImageClassification(A__   )
								model.to(A__   )
								model.eval()
								_lowerCamelCase        =				model(A__				,       labels=A__   )
								self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)   )





				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.prepare_config_and_inputs()
								_lowerCamelCase        =				config_and_inputs
								_lowerCamelCase        =				{"""pixel_values""": pixel_values}
								return config, inputs_dict


@require_torch
class 							lowerCamelCase_(   A__,				A__,				unittest.TestCase    ):





				'''simple docstring'''



				lowercase__   :       Union[str, Any]										= (
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				lowercase__   :       Union[str, Any]										= (
				    {
				        '''feature-extraction''': LevitModel,
				        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				lowercase__   :       List[str]										= False
				lowercase__   :       List[Any]										= False
				lowercase__   :       int										= False
				lowercase__   :       Optional[int]										= False
				lowercase__   :       Dict										= False

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				LevitModelTester(self   )
								_lowerCamelCase        =				ConfigTester(self				,       config_class=A__				,       has_text_modality=A__				,       hidden_size=3_7   )

				def 			snake_case__      (    self   ):
								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 			snake_case__      (    self   ):
								return

				@unittest.skip(reason='''Levit does not use inputs_embeds'''   )
				def 			snake_case__      (    self   ):
								pass

				@unittest.skip(reason='''Levit does not support input and output embeddings'''   )
				def 			snake_case__      (    self   ):
								pass

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

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCamelCase        =				model_class(A__   )
												_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]				,       A__   )

				def 			snake_case__      (    self   ):
								def check_hidden_states_output(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
												_lowerCamelCase        =				model_class(A__   )
												model.to(A__   )
												model.eval()

												with torch.no_grad():
																_lowerCamelCase        =				model(**self._prepare_for_class(A__				,       A__   )   )

												_lowerCamelCase        =				outputs.hidden_states

												_lowerCamelCase        =				len(self.model_tester.depths   ) + 1
												self.assertEqual(len(A__   )				,       A__   )

												_lowerCamelCase        =				(self.model_tester.image_size, self.model_tester.image_size)
												_lowerCamelCase        =				image_size[0], image_size[1]
												for _ in range(4   ):
																_lowerCamelCase        =				floor(
																    (
																        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1   )
																_lowerCamelCase        =				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],
												    ]				,       )

								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCamelCase        =				True
												check_hidden_states_output(A__				,       A__				,       A__   )

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

												check_hidden_states_output(A__				,       A__				,       A__   )

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

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=False   ):
								_lowerCamelCase        =				super()._prepare_for_class(A__				,       A__				,       return_labels=A__   )

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

								return inputs_dict

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A__   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A__   )

				def 			snake_case__      (    self   ):
								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:
												# LevitForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(A__   )
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
																continue
												_lowerCamelCase        =				model_class(A__   )
												model.to(A__   )
												model.train()
												_lowerCamelCase        =				self._prepare_for_class(A__				,       A__				,       return_labels=A__   )
												_lowerCamelCase        =				model(**A__   ).loss
												loss.backward()

				def 			snake_case__      (    self   ):
								_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:
												if model_class in get_values(A__   ) or not model_class.supports_gradient_checkpointing:
																continue
												# LevitForImageClassificationWithTeacher supports inference-only
												if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																continue
												_lowerCamelCase        =				model_class(A__   )
												model.gradient_checkpointing_enable()
												model.to(A__   )
												model.train()
												_lowerCamelCase        =				self._prepare_for_class(A__				,       A__				,       return_labels=A__   )
												_lowerCamelCase        =				model(**A__   ).loss
												loss.backward()

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs_for_common()

								_lowerCamelCase        =				[
								    {"""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(A__   ),
												    ]
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
																continue

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

																				_lowerCamelCase        =				model_class(A__   )
																				model.to(A__   )
																				model.train()

																				_lowerCamelCase        =				self._prepare_for_class(A__				,       A__				,       return_labels=A__   )

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

																				_lowerCamelCase        =				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=A__   ) as warning_list:
																								_lowerCamelCase        =				model(**A__   ).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 			snake_case__      (    self   ):
								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCamelCase        =				LevitModel.from_pretrained(A__   )
												self.assertIsNotNone(A__   )



def        lowerCAmelCase_(   )	->       Dict:
				_lowerCamelCase        =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




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





				'''simple docstring'''



				@cached_property
				def 			snake_case__      (    self   ):
								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )





				@slow
				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
								    A__   )

								_lowerCamelCase        =				self.default_image_processor
								_lowerCamelCase        =				prepare_img()
								_lowerCamelCase        =				image_processor(images=A__				,       return_tensors='''pt'''   ).to(A__   )

								# forward pass
								with torch.no_grad():
												_lowerCamelCase        =				model(**A__   )

								# verify the logits
								_lowerCamelCase        =				torch.Size((1, 1_0_0_0)   )
								self.assertEqual(outputs.logits.shape				,       A__   )

								_lowerCamelCase        =				torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]   ).to(A__   )

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





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   input_matrix.conj().T							)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

				while not convergence:
								# Multiple matrix by the vector.
								_lowerCamelCase        =				np.dot(lowercase_						,   lowercase_							)
								# Normalize the resulting output vector.
								_lowerCamelCase        =				w / np.linalg.norm(lowercase_							)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								_lowerCamelCase        =				vector.conj().T if is_complex else vector.T
								_lowerCamelCase        =				np.dot(lowercase_						,   np.dot(lowercase_						,   lowercase_							)							)

								# Check convergence.
								_lowerCamelCase        =				np.abs(lambda_ - lambda_previous							) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												_lowerCamelCase        =				True

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												_lowerCamelCase        =				real_input_matrix
												_lowerCamelCase        =				real_vector
								elif problem_type == "complex":
												_lowerCamelCase        =				complex_input_matrix
												_lowerCamelCase        =				complex_vector

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# Last eigenvalue is the maximum one.
								_lowerCamelCase        =				eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								_lowerCamelCase        =				eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max							) <= 1e-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
