

import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
					from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=1_3				,      lowerCamelCase__=3				,      lowerCamelCase__=True				,      lowerCamelCase__=True				,      lowerCamelCase__=0.1				,      lowerCamelCase__=0.1				,      lowerCamelCase__=2_2_4				,      lowerCamelCase__=1_0_0_0				,      lowerCamelCase__=[3, 3, 6, 4]				,      lowerCamelCase__=[4_8, 5_6, 1_1_2, 2_2_0]				,      ):
						UpperCAmelCase__:	Optional[int]									= parent
						UpperCAmelCase__:	Union[str, Any]									= batch_size
						UpperCAmelCase__:	Tuple									= num_channels
						UpperCAmelCase__:	Dict									= is_training
						UpperCAmelCase__:	Dict									= use_labels
						UpperCAmelCase__:	Dict									= hidden_dropout_prob
						UpperCAmelCase__:	Optional[int]									= attention_probs_dropout_prob
						UpperCAmelCase__:	Tuple									= num_labels
						UpperCAmelCase__:	List[Any]									= image_size
						UpperCAmelCase__:	Optional[Any]									= layer_depths
						UpperCAmelCase__:	List[str]									= embed_dims





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

						UpperCAmelCase__:	Union[str, Any]									= None
						if self.use_labels:
							UpperCAmelCase__:	List[str]									= ids_tensor([self.batch_size]				,      self.num_labels						)

						UpperCAmelCase__:	Tuple									= self.get_config()

						return config, pixel_values, labels





					def    _UpperCAmelCase    (				self						):
						return SwiftFormerConfig(
						    depths=self.layer_depths				,      embed_dims=self.embed_dims				,      mlp_ratio=4				,      downsamples=[True, True, True, True]				,      hidden_act="gelu"				,      num_labels=self.num_labels				,      down_patch_size=3				,      down_stride=2				,      down_pad=1				,      drop_rate=0.0				,      drop_path_rate=0.0				,      use_layer_scale=lowerCamelCase__				,      layer_scale_init_value=1e-5				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= SwiftFormerModel(config=lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()
						UpperCAmelCase__:	Union[str, Any]									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.embed_dims[-1], 7, 7)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= self.num_labels
						UpperCAmelCase__:	Dict									= SwiftFormerForImageClassification(lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()
						UpperCAmelCase__:	str									= model(lowerCamelCase__				,      labels=lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)						)

						UpperCAmelCase__:	Union[str, Any]									= SwiftFormerForImageClassification(lowerCamelCase__						)
						model.to(lowerCamelCase__						)
						model.eval()

						UpperCAmelCase__:	Union[str, Any]									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
						UpperCAmelCase__:	Tuple									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)						)





					def    _UpperCAmelCase    (				self						):
						((UpperCAmelCase__)							,						(UpperCAmelCase__)							,						(UpperCAmelCase__)):	List[str]									= self.prepare_config_and_inputs()
						UpperCAmelCase__:	Tuple									= {"pixel_values": pixel_values}
						return config, inputs_dict

@require_torch
class __UpperCamelCase     (				_a					,_a					,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__        =   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					__magic_name__        =   (
					    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					__magic_name__        =   False
					__magic_name__        =   False
					__magic_name__        =   False
					__magic_name__        =   False
					__magic_name__        =   False





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= SwiftFormerModelTester(self						)
						UpperCAmelCase__:	Dict									= ConfigTester(
						    self				,      config_class=lowerCamelCase__				,      has_text_modality=lowerCamelCase__				,      hidden_size=3_7				,      num_attention_heads=1_2				,      num_hidden_layers=1_2				,      )





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





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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase__:	str									= model_class(lowerCamelCase__						)
							UpperCAmelCase__:	List[Any]									= model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowerCamelCase__				,      nn.Linear						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase__:	List[str]									= ["pixel_values"]
							self.assertListEqual(arg_names[:1]				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__						)





					@slow
					def    _UpperCAmelCase    (				self						):
						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase__:	List[Any]									= SwiftFormerModel.from_pretrained(lowerCamelCase__						)
							self.assertIsNotNone(lowerCamelCase__						)





					@unittest.skip(reason="SwiftFormer does not output attentions"						)
					def    _UpperCAmelCase    (				self						):
						pass





					def    _UpperCAmelCase    (				self						):
						def check_hidden_states_output(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	Optional[int]									= model_class(lowerCamelCase__						)
							model.to(lowerCamelCase__						)
							model.eval()

							with torch.no_grad():
								UpperCAmelCase__:	Any									= model(**self._prepare_for_class(lowerCamelCase__				,      lowerCamelCase__						)						)

							UpperCAmelCase__:	Optional[Any]									= outputs.hidden_states

							UpperCAmelCase__:	Dict									= 8
							self.assertEqual(len(lowerCamelCase__						)				,      lowerCamelCase__						)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(lowerCamelCase__						)						):
								self.assertEqual(
								    hidden_states[i].shape				,      torch.Size(
								        [
								            self.model_tester.batch_size,
								            self.model_tester.embed_dims[i // 2],
								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
								        ]						)				,      )

						UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase__:	int									= True
							check_hidden_states_output(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase__:	List[Any]									= True

							check_hidden_states_output(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						def _config_zero_init(lowerCamelCase__						):
							UpperCAmelCase__:	str									= copy.deepcopy(lowerCamelCase__						)
							for key in configs_no_init.__dict__.keys():
								if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
									setattr(lowerCamelCase__				,      lowerCamelCase__				,      1e-10						)
								if isinstance(getattr(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)				,      lowerCamelCase__						):
									UpperCAmelCase__:	Optional[Any]									= _config_zero_init(getattr(lowerCamelCase__				,      lowerCamelCase__						)						)
									setattr(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)
							return configs_no_init

						UpperCAmelCase__							,						UpperCAmelCase__:	Any									= self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase__:	str									= _config_zero_init(lowerCamelCase__						)
						for model_class in self.all_model_classes:
							UpperCAmelCase__:	List[Any]									= model_class(config=lowerCamelCase__						)
							for name, param in model.named_parameters():
								if param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9) / 1e9).round().item()				,      [0.0, 1.0]				,      msg=F"Parameter {name} of model {model_class} seems not properly initialized"				,      )





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



def 			_A	(      ):
	UpperCAmelCase__:	List[str]									= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
	return image

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






					'''simple docstring'''



					@cached_property
					def    _UpperCAmelCase    (				self						):
						return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"						) if is_vision_available() else None





					@slow
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[int]									= SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"						).to(lowerCamelCase__						)

						UpperCAmelCase__:	List[str]									= self.default_image_processor
						UpperCAmelCase__:	Optional[Any]									= prepare_img()
						UpperCAmelCase__:	Union[str, Any]									= image_processor(images=lowerCamelCase__				,      return_tensors="pt"						).to(lowerCamelCase__						)

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

						# verify the logits
						UpperCAmelCase__:	Dict									= torch.Size((1, 1_0_0_0)						)
						self.assertEqual(outputs.logits.shape				,      lowerCamelCase__						)

						UpperCAmelCase__:	Optional[int]									= torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]						).to(lowerCamelCase__						)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]				,      lowerCamelCase__				,      atol=1e-4						)						)


import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	return input_array.reshape((input_array.size, 1)      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= np.nan
	for i in range(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	Tuple									= features[:, labels == i]
		UpperCAmelCase__:	int									= data.mean(1      )
		# Centralize the data of class i
		UpperCAmelCase__:	Optional[Any]									= data - column_reshape(SCREAMING_SNAKE_CASE      )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      )
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCAmelCase__:	Dict									= np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      )

	return covariance_sum / features.shape[1]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= features.mean(1      )
	UpperCAmelCase__:	Optional[Any]									= np.nan
	for i in range(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[Any]									= features[:, labels == i]
		UpperCAmelCase__:	Optional[Any]									= data.shape[1]
		UpperCAmelCase__:	List[Any]									= data.mean(1      )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += device_data * np.dot(
			    column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )	,(column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )).T	,)
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCAmelCase__:	Tuple									= device_data * np.dot(
			    column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )	,(column_reshape(SCREAMING_SNAKE_CASE      ) - column_reshape(SCREAMING_SNAKE_CASE      )).T	,)

	return covariance_sum / features.shape[1]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):

	# Check if the features have been loaded
	if features.any():
		UpperCAmelCase__:	Tuple									= features.mean(1      )
		# Center the dataset
		UpperCAmelCase__:	List[Any]									= features - np.reshape(SCREAMING_SNAKE_CASE	,(data_mean.size, 1)      )
		UpperCAmelCase__:	Optional[int]									= np.dot(SCREAMING_SNAKE_CASE	,centered_data.T      ) / features.shape[1]
		UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= np.linalg.eigh(SCREAMING_SNAKE_CASE      )
		# Take all the columns in the reverse order (-1), and then takes only the first
		UpperCAmelCase__:	Optional[Any]									= eigenvectors[:, ::-1][:, 0:dimensions]
		# Project the database on the new space
		UpperCAmelCase__:	Tuple									= np.dot(filtered_eigenvectors.T	,SCREAMING_SNAKE_CASE      )
		logging.info("Principal Component Analysis computed"      )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR	,format="%(message)s"	,force=SCREAMING_SNAKE_CASE      )
		logging.error("Dataset empty"      )
		raise AssertionError



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	assert classes > dimensions

	# Check if features have been already loaded
	if features.any:
		UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= eigh(
		    covariance_between_classes(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )	,covariance_within_classes(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )	,)
		UpperCAmelCase__:	int									= eigenvectors[:, ::-1][:, :dimensions]
		UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Optional[Any]									= np.linalg.svd(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Optional[Any]									= svd_matrix[:, 0:dimensions]
		UpperCAmelCase__:	Optional[int]									= np.dot(filtered_svd_matrix.T	,SCREAMING_SNAKE_CASE      )
		logging.info("Linear Discriminant Analysis computed"      )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR	,format="%(message)s"	,force=SCREAMING_SNAKE_CASE      )
		logging.error("Dataset empty"      )
		raise AssertionError



def 			_A	(      ):
	# Create dummy dataset with 2 classes and 3 features
	UpperCAmelCase__:	List[str]									= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]      )
	UpperCAmelCase__:	Optional[int]									= np.array([0, 0, 0, 1, 1]      )
	UpperCAmelCase__:	List[Any]									= 2
	UpperCAmelCase__:	Union[str, Any]									= 2

	# Assert that the function raises an AssertionError if dimensions > classes
	with pytest.raises(SCREAMING_SNAKE_CASE      ) as error_info:
		UpperCAmelCase__:	List[Any]									= linear_discriminant_analysis(
		    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		if isinstance(SCREAMING_SNAKE_CASE	,np.ndarray      ):
			raise AssertionError(
			    "Did not raise AssertionError for dimensions > classes"      )
		assert error_info.type is AssertionError



def 			_A	(      ):
	UpperCAmelCase__:	Optional[Any]									= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]      )
	UpperCAmelCase__:	Optional[int]									= 2
	UpperCAmelCase__:	Union[str, Any]									= np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]      )

	with pytest.raises(SCREAMING_SNAKE_CASE      ) as error_info:
		UpperCAmelCase__:	Any									= principal_component_analysis(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		if not np.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
			raise AssertionError
		assert error_info.type is AssertionError


if __name__ == "__main__":
					import doctest

					doctest.testmod()