

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 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__					, A__=13					, A__=64					, A__=2					, A__=3					, A__=True					, A__=True					, A__=32					, A__=5					, A__=4					, A__=37					, A__="gelu"					, A__=0.1					, A__=0.1					, A__=10					, A__=0.0_2					, A__=[1, 16, 4, 4]					, A__=None					, ):
						A__							:					int       =	parent
						A__							:					str       =	batch_size
						A__							:					Tuple       =	image_size
						A__							:					List[Any]       =	patch_size
						A__							:					Optional[Any]       =	num_channels
						A__							:					List[str]       =	is_training
						A__							:					List[Any]       =	use_labels
						A__							:					Optional[Any]       =	hidden_size
						A__							:					Any       =	num_hidden_layers
						A__							:					List[Any]       =	num_attention_heads
						A__							:					Any       =	intermediate_size
						A__							:					Optional[int]       =	hidden_act
						A__							:					List[str]       =	hidden_dropout_prob
						A__							:					str       =	attention_probs_dropout_prob
						A__							:					int       =	type_sequence_label_size
						A__							:					Dict       =	initializer_range
						A__							:					Tuple       =	scope
						A__							:					Dict       =	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__							:					Optional[int]       =	(self.image_size // 32) ** 2
						A__							:					List[str]       =	num_patches + 1






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

						A__							:					str       =	None
						if self.use_labels:
							A__							:					Union[str, Any]       =	ids_tensor([self.batch_size]					, self.type_sequence_label_size    )

						A__							:					Dict       =	self.get_config()

						return config, pixel_values, labels






					def 						__A    (				self    ):
						A__							:					Union[str, 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=A__					, initializer_range=self.initializer_range					, backbone_featmap_shape=self.backbone_featmap_shape					, backbone_config=A__					, )






					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					Optional[int]       =	ViTHybridModel(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Tuple       =	model(A__    )
						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, self.seq_length, self.hidden_size)    )






					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					Dict       =	self.type_sequence_label_size
						A__							:					Dict       =	ViTHybridForImageClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					Any       =	model(A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.prepare_config_and_inputs()
						A__     ,     A__     ,     A__							:					Union[str, Any]       =	config_and_inputs
						A__							:					str       =	{"""pixel_values""": pixel_values}
						return config, inputs_dict



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


					'''simple docstring'''






					UpperCAmelCase__:     str													= (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					UpperCAmelCase__:     Tuple													= (
					    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					UpperCAmelCase__:     Dict													= False
					UpperCAmelCase__:     List[Any]													= False
					UpperCAmelCase__:     Tuple													= False






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	ViTHybridModelTester(self    )
						A__							:					List[Any]       =	ConfigTester(self					, config_class=A__					, has_text_modality=A__					, 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__							:					List[str]       =	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:					int       =	model_class(A__    )
							self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)    )
							A__							:					int       =	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__							:					List[str]       =	model_class(A__    )
							A__							:					str       =	inspect.signature(model.forward    )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A__							:					Optional[int]       =	[*signature.parameters.keys()]

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






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__    )






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

						A__							:					Any       =	_config_zero_init(A__    )
						for model_class in self.all_model_classes:
							A__							:					List[str]       =	model_class(config=A__    )
							# Skip the check for the backbone
							for name, module in model.named_modules():
								if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
									A__							:					Tuple       =	[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__							:					Union[str, Any]       =	ViTHybridModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )



def 		UpperCamelCase      ()  ->   Any:
	A__							:					Tuple       =	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 (
						    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
						    if is_vision_available()
						    else None
						)






					@slow
					def 						__A    (				self    ):
						A__							:					str       =	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(
						    A__    )

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

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

						# verify the logits
						A__							:					Tuple       =	torch.Size((1, 1000)    )
						self.assertEqual(outputs.logits.shape					, A__    )

						A__							:					List[Any]       =	torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]    ).to(A__    )

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






					@slow
					@require_accelerate
					def 						__A    (				self    ):
						A__							:					str       =	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[Any]       =	prepare_img()

						A__							:					Union[str, Any]       =	image_processor(images=A__					, return_tensors="""pt"""    )
						A__							:					Union[str, Any]       =	model(**A__    )
						A__							:					Tuple       =	outputs.logits
						# model predicts one of the 1000 ImageNet classes
						A__							:					Union[str, Any]       =	logits.argmax(-1    ).item()

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






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()



