

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 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__					, A__=13					, A__=3					, A__=True					, A__=True					, A__=0.1					, A__=0.1					, A__=224					, A__=1000					, A__=[3, 3, 6, 4]					, A__=[48, 56, 112, 220]					, ):
						A__							:					Tuple       =	parent
						A__							:					str       =	batch_size
						A__							:					int       =	num_channels
						A__							:					Union[str, Any]       =	is_training
						A__							:					Tuple       =	use_labels
						A__							:					List[str]       =	hidden_dropout_prob
						A__							:					Any       =	attention_probs_dropout_prob
						A__							:					Dict       =	num_labels
						A__							:					List[Any]       =	image_size
						A__							:					List[Any]       =	layer_depths
						A__							:					str       =	embed_dims






					def 						__A    (				self    ):
						A__							:					Tuple       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

						A__							:					Tuple       =	self.get_config()

						return config, pixel_values, labels






					def 						__A    (				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=A__					, layer_scale_init_value=1e-5					, )






					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					List[Any]       =	SwiftFormerModel(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					List[Any]       =	model(A__    )
						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.embed_dims[-1], 7, 7)    )






					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					Tuple       =	self.num_labels
						A__							:					Tuple       =	SwiftFormerForImageClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					int       =	model(A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)    )

						A__							:					List[str]       =	SwiftFormerForImageClassification(A__    )
						model.to(A__    )
						model.eval()

						A__							:					Any       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
						A__							:					Optional[int]       =	model(A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.num_labels)    )






					def 						__A    (				self    ):
						((A__)     ,     (A__)     ,     (A__))							:					Tuple       =	self.prepare_config_and_inputs()
						A__							:					Optional[int]       =	{"""pixel_values""": pixel_values}
						return config, inputs_dict



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


					'''simple docstring'''






					UpperCAmelCase__:     Dict													= (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					UpperCAmelCase__:     List[Any]													= (
					    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__:     Dict													= False
					UpperCAmelCase__:     Union[str, Any]													= False
					UpperCAmelCase__:     Any													= False
					UpperCAmelCase__:     int													= False
					UpperCAmelCase__:     Any													= False






					def 						__A    (				self    ):
						A__							:					List[str]       =	SwiftFormerModelTester(self    )
						A__							:					int       =	ConfigTester(
						    self					, config_class=A__					, has_text_modality=A__					, hidden_size=37					, num_attention_heads=12					, num_hidden_layers=12					, )






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






					@unittest.skip(reason="""SwiftFormer 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__							:					List[str]       =	model_class(A__    )
							A__							:					Dict       =	model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(A__					, nn.Linear    )    )






					def 						__A    (				self    ):
						A__     ,     A__							:					Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

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

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






					def 						__A    (				self    ):
						A__							:					Dict       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )






					def 						__A    (				self    ):
						A__							:					Any       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__    )






					@slow
					def 						__A    (				self    ):
						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:					Tuple       =	SwiftFormerModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )






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






					def 						__A    (				self    ):
						def check_hidden_states_output(A__					, A__					, A__    ):
							A__							:					Any       =	model_class(A__    )
							model.to(A__    )
							model.eval()

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

							A__							:					str       =	outputs.hidden_states

							A__							:					Tuple       =	8
							self.assertEqual(len(A__    )					, A__    )  # 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(A__    )    ):
								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),
								        ]    )					, )

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

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

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

							check_hidden_states_output(A__					, A__					, A__    )






					def 						__A    (				self    ):
						def _config_zero_init(A__    ):
							A__							:					str       =	copy.deepcopy(A__    )
							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(A__					, A__					, 1e-10    )
								if isinstance(getattr(A__					, A__					, A__    )					, A__    ):
									A__							:					Optional[Any]       =	_config_zero_init(getattr(A__					, A__    )    )
									setattr(A__					, A__					, A__    )
							return configs_no_init

						A__     ,     A__							:					str       =	self.model_tester.prepare_config_and_inputs_for_common()

						A__							:					List[Any]       =	_config_zero_init(A__    )
						for model_class in self.all_model_classes:
							A__							:					str       =	model_class(config=A__    )
							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 						__A    (				self    ):
						pass



def 		UpperCamelCase      ()  ->   Optional[Any]:
	A__							:					Union[str, Any]       =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
	return image



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


					'''simple docstring'''






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






					@slow
					def 						__A    (				self    ):
						A__							:					List[Any]       =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""    ).to(A__    )

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

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

						# verify the logits
						A__							:					Union[str, Any]       =	torch.Size((1, 1000)    )
						self.assertEqual(outputs.logits.shape					, A__    )

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






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				int					,   lowercase_:				int      )  ->   np.ndarray:
	A__							:					Any       =	cva.getAffineTransform(lowercase_					,   lowercase_      )
	return cva.warpAffine(lowercase_					,   lowercase_					,   (rows, cols)      )


if __name__ == "__main__":
	# read original image
	A_ :				List[Any]						 =    cva.imread(
	    str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
	)
	# turn image in gray scale value
	A_ :				List[Any]						 =    cva.cvtColor(image, cva.COLOR_BGR2GRAY)
	# get image shape
	A_ ,    A_ :				Optional[Any]						 =    gray_img.shape

	# set different points to rotate image
	A_ :				str						 =    np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A_ :				Dict						 =    np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

	# add all rotated images in a list
	A_ :				Dict						 =    [
	    gray_img,
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	]

	# plot different image rotations
	A_ :				Union[str, Any]						 =    plt.figure(1)
	A_ :				Union[str, Any]						 =    ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
	for i, image in enumerate(images):
		plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
		plt.title(titles[i])
		plt.axis('off')
		plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
	plt.show()



