


import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 DinatBackbone, DinatForImageClassification, DinatModel
	from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor




class 			lowercase_				:





				def __init__(							self					,     lowercase_					,     lowercase_=13					,     lowercase_=64					,     lowercase_=4					,     lowercase_=3					,     lowercase_=16					,     lowercase_=[1, 2, 1]					,     lowercase_=[2, 4, 8]					,     lowercase_=3					,     lowercase_=[[3], [1, 2], [1]]					,     lowercase_=2.0					,     lowercase_=True					,     lowercase_=0.0					,     lowercase_=0.0					,     lowercase_=0.1					,     lowercase_="gelu"					,     lowercase_=True					,     lowercase_=0.02					,     lowercase_=1e-5					,     lowercase_=True					,     lowercase_=None					,     lowercase_=True					,     lowercase_=10					,     lowercase_=["stage1", "stage2"]					,     lowercase_=[1, 2]					,     )     ->   Any:
											a__     						=parent
											a__     						=batch_size
											a__     						=image_size
											a__     						=patch_size
											a__     						=num_channels
											a__     						=embed_dim
											a__     						=depths
											a__     						=num_heads
											a__     						=kernel_size
											a__     						=dilations
											a__     						=mlp_ratio
											a__     						=qkv_bias
											a__     						=hidden_dropout_prob
											a__     						=attention_probs_dropout_prob
											a__     						=drop_path_rate
											a__     						=hidden_act
											a__     						=patch_norm
											a__     						=layer_norm_eps
											a__     						=initializer_range
											a__     						=is_training
											a__     						=scope
											a__     						=use_labels
											a__     						=num_labels
											a__     						=out_features
											a__     						=out_indices





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__     						=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

											a__     						=self.get_config()

											return config, pixel_values, labels





				def 	__UpperCamelCase						(							self)     ->   Any:
											return DinatConfig(
											    num_labels=self.num_labels					,     image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     embed_dim=self.embed_dim					,     depths=self.depths					,     num_heads=self.num_heads					,     kernel_size=self.kernel_size					,     dilations=self.dilations					,     mlp_ratio=self.mlp_ratio					,     qkv_bias=self.qkv_bias					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     drop_path_rate=self.drop_path_rate					,     hidden_act=self.hidden_act					,     patch_norm=self.patch_norm					,     layer_norm_eps=self.layer_norm_eps					,     initializer_range=self.initializer_range					,     out_features=self.out_features					,     out_indices=self.out_indices					,     )





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   int:
											a__     						=DinatModel(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											a__     						=model(lowercase_)

											a__     						=a__     						=(config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
											a__     						=int(config.embed_dim * 2 ** (len(config.depths) - 1))

											self.parent.assertEqual(
											    result.last_hidden_state.shape					,     (self.batch_size, expected_height, expected_width, expected_dim))





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Optional[Any]:
											a__     						=DinatForImageClassification(lowercase_)
											model.to(lowercase_)
											model.eval()
											a__     						=model(lowercase_					,     labels=lowercase_)
											self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels))

											# test greyscale images
											a__     						=1
											a__     						=DinatForImageClassification(lowercase_)
											model.to(lowercase_)
											model.eval()

											a__     						=floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											a__     						=model(lowercase_)
											self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels))





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Union[str, Any]:
											a__     						=DinatBackbone(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											a__     						=model(lowercase_)

											# verify hidden states
											self.parent.assertEqual(len(result.feature_maps)					,     len(config.out_features))
											self.parent.assertListEqual(list(result.feature_maps[0].shape)					,     [self.batch_size, model.channels[0], 16, 16])

											# verify channels
											self.parent.assertEqual(len(model.channels)					,     len(config.out_features))

											# verify backbone works with out_features=None
											a__     						=None
											a__     						=DinatBackbone(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											a__     						=model(lowercase_)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps)					,     1)
											self.parent.assertListEqual(list(result.feature_maps[0].shape)					,     [self.batch_size, model.channels[-1], 4, 4])

											# verify channels
											self.parent.assertEqual(len(model.channels)					,     1)





				def 	__UpperCamelCase						(							self)     ->   List[str]:
											a__     						=self.prepare_config_and_inputs()
											a__						,    a__						,    a__     						=config_and_inputs
											a__     						={'pixel_values': pixel_values}
											return config, inputs_dict







@require_natten
@require_torch
class 			lowercase_				(lowercase__			,				lowercase__			,				unittest.TestCase      ):
				snake_case  						=(
				    (
				        DinatModel,
				        DinatForImageClassification,
				        DinatBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				snake_case  						=(
				    {'feature-extraction': DinatModel, 'image-classification': DinatForImageClassification}
				    if is_torch_available()
				    else {}
				)
				snake_case  						=False

				snake_case  						=False
				snake_case  						=False
				snake_case  						=False
				snake_case  						=False





				def 	__UpperCamelCase						(							self)     ->   Dict:
											a__     						=DinatModelTester(self)
											a__     						=ConfigTester(self					,     config_class=lowercase_					,     embed_dim=37)





				def 	__UpperCamelCase						(							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 	__UpperCamelCase						(							self)     ->   str:
											return





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__     						=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_)





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*lowercase_)





				def 	__UpperCamelCase						(							self)     ->   List[str]:
											a__     						=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*lowercase_)





				@unittest.skip(reason='Dinat does not use inputs_embeds')
				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											pass





				@unittest.skip(reason='Dinat does not use feedforward chunking')
				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											pass





				def 	__UpperCamelCase						(							self)     ->   Tuple:
											a__						,    a__     						=self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__     						=model_class(lowercase_)
																		self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module))
																		a__     						=model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(lowercase_					,     nn.Linear))





				def 	__UpperCamelCase						(							self)     ->   Any:
											a__						,    a__     						=self.model_tester.prepare_config_and_inputs_for_common()

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

																		a__     						=['pixel_values']
																		self.assertListEqual(arg_names[:1]					,     lowercase_)





				def 	__UpperCamelCase						(							self)     ->   Union[str, Any]:
											self.skipTest('Dinat\'s attention operation is handled entirely by NATTEN.')





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Any:
											a__     						=model_class(lowercase_)
											model.to(lowercase_)
											model.eval()

											with torch.no_grad():
																		a__     						=model(**self._prepare_for_class(lowercase_					,     lowercase_))

											a__     						=outputs.hidden_states

											a__     						=getattr(
											    self.model_tester					,     'expected_num_hidden_layers'					,     len(self.model_tester.depths) + 1)
											self.assertEqual(len(lowercase_)					,     lowercase_)

											# Dinat has a different seq_length
											a__     						=(
											    config.patch_size
											    if isinstance(config.patch_size					,     collections.abc.Iterable)
											    else (config.patch_size, config.patch_size)
											)

											a__     						=image_size[0] // patch_size[0]
											a__     						=image_size[1] // patch_size[1]

											self.assertListEqual(
											    list(hidden_states[0].shape[-3:])					,     [height, width, self.model_tester.embed_dim]					,     )

											if model_class.__name__ != "DinatBackbone":
																		a__     						=outputs.reshaped_hidden_states
																		self.assertEqual(len(lowercase_)					,     lowercase_)

																		a__						,    a__						,    a__						,    a__     						=reshaped_hidden_states[0].shape
																		a__     						=(
																		    reshaped_hidden_states[0].view(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_).permute(0					,     2					,     3					,     1)
																		)
																		self.assertListEqual(
																		    list(reshaped_hidden_states.shape[-3:])					,     [height, width, self.model_tester.embed_dim]					,     )





				def 	__UpperCamelCase						(							self)     ->   Any:
											a__						,    a__     						=self.model_tester.prepare_config_and_inputs_for_common()

											a__     						=(
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size					,     collections.abc.Iterable)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)

											for model_class in self.all_model_classes:
																		a__     						=True
																		self.check_hidden_states_output(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)

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

																		self.check_hidden_states_output(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)





				@slow
				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		a__     						=DinatModel.from_pretrained(lowercase_)
																		self.assertIsNotNone(lowercase_)





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__						,    a__     						=self.model_tester.prepare_config_and_inputs_for_common()

											a__     						=_config_zero_init(lowercase_)
											for model_class in self.all_model_classes:
																		a__     						=model_class(config=lowercase_)
																		for name, param in model.named_parameters():
																									if "embeddings" not in name and param.requires_grad:
																																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"""					,     )







@require_natten
@require_vision
@require_torch
class 			lowercase_				(unittest.TestCase      ):





				@cached_property
				def 	__UpperCamelCase						(							self)     ->   Optional[int]:
											return AutoImageProcessor.from_pretrained('shi-labs/dinat-mini-in1k-224') if is_vision_available() else None





				@slow
				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						=DinatForImageClassification.from_pretrained('shi-labs/dinat-mini-in1k-224').to(lowercase_)
											a__     						=self.default_image_processor

											a__     						=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
											a__     						=image_processor(images=lowercase_					,     return_tensors='pt').to(lowercase_)

											# forward pass
											with torch.no_grad():
																		a__     						=model(**lowercase_)

											# verify the logits
											a__     						=torch.Size((1, 1000))
											self.assertEqual(outputs.logits.shape					,     lowercase_)
											a__     						=torch.tensor([-0.15_45, -0.76_67, 0.46_42]).to(lowercase_)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     lowercase_					,     atol=1e-4))







@require_torch
@require_natten
class 			lowercase_				(unittest.TestCase			,				lowercase__      ):
				snake_case  						=(DinatBackbone,) if is_torch_available() else ()
				snake_case  						=DinatConfig





				def 	__UpperCamelCase						(							self)     ->   Tuple:
											a__     						=DinatModelTester(self)








import numpy as np
from PIL import Image






def      _lowercase(				__a							:	np.array    ):
							a__						,    a__						,    a__     						=rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def      _lowercase(				__a							:	np.array    ):
							return (gray > 127) & (gray <= 255)






def      _lowercase(				__a							:	np.array						,    __a							:	np.array    ):
							a__     						=np.zeros_like(__a    )
							a__     						=np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

							# Copy image to padded image
							a__     						=image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]    ):
														for y in range(image.shape[0]    ):
																					a__     						=(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					a__     						=int(summation == 5    )
							return output


# kernel to be applied
_lowerCAmelCase:	Dict      							=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
	# read original image
	_lowerCAmelCase:	Optional[int]      							=					np.array(Image.open(R'..\image_data\lena.jpg'))
	# Apply erosion operation to a binary image
	_lowerCAmelCase:	Any      							=					erosion(grayabinary(rgbagray(image)), structuring_element)
	# Save the output image
	_lowerCAmelCase:	Dict      							=					Image.fromarray(output).convert('RGB')
	pil_img.save('result_erosion.png')

