


import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
	    MODEL_MAPPING,
	    DataaVecVisionForImageClassification,
	    DataaVecVisionForSemanticSegmentation,
	    DataaVecVisionModel,
	)
	from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import BeitImageProcessor




class 			lowercase_				:





				def __init__(							self					,     lowercase_					,     lowercase_=100					,     lowercase_=13					,     lowercase_=30					,     lowercase_=2					,     lowercase_=3					,     lowercase_=True					,     lowercase_=True					,     lowercase_=32					,     lowercase_=4					,     lowercase_=4					,     lowercase_=37					,     lowercase_="gelu"					,     lowercase_=0.1					,     lowercase_=0.1					,     lowercase_=10					,     lowercase_=0.02					,     lowercase_=3					,     lowercase_=None					,     lowercase_=[0, 1, 2, 3]					,     )     ->   Optional[Any]:
											a__     						=parent
											a__     						=100
											a__     						=batch_size
											a__     						=image_size
											a__     						=patch_size
											a__     						=num_channels
											a__     						=is_training
											a__     						=use_labels
											a__     						=hidden_size
											a__     						=num_hidden_layers
											a__     						=num_attention_heads
											a__     						=intermediate_size
											a__     						=hidden_act
											a__     						=hidden_dropout_prob
											a__     						=attention_probs_dropout_prob
											a__     						=type_sequence_label_size
											a__     						=initializer_range
											a__     						=scope
											a__     						=out_indices
											a__     						=num_labels

											# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											a__     						=(image_size // patch_size) ** 2
											a__     						=num_patches + 1





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

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

											a__     						=self.get_config()

											return config, pixel_values, labels, pixel_labels





				def 	__UpperCamelCase						(							self)     ->   Any:
											return DataaVecVisionConfig(
											    vocab_size=self.vocab_size					,     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=lowercase_					,     initializer_range=self.initializer_range					,     out_indices=self.out_indices					,     )





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)     ->   int:
											a__     						=DataaVecVisionModel(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											a__     						=model(lowercase_)
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											a__     						=(self.image_size // self.patch_size) ** 2
											self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, num_patches + 1, self.hidden_size))





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





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)     ->   str:
											a__     						=self.num_labels
											a__     						=DataaVecVisionForSemanticSegmentation(lowercase_)
											model.to(lowercase_)
											model.eval()
											a__     						=model(lowercase_)
											self.parent.assertEqual(
											    result.logits.shape					,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
											a__     						=model(lowercase_					,     labels=lowercase_)
											self.parent.assertEqual(
											    result.logits.shape					,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))





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







@require_torch
class 			lowercase_				(lowercase__			,				lowercase__			,				unittest.TestCase      ):
				snake_case  						=(
				    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				snake_case  						=(
				    {
				        'feature-extraction': DataaVecVisionModel,
				        'image-classification': DataaVecVisionForImageClassification,
				        'image-segmentation': DataaVecVisionForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				snake_case  						=False
				snake_case  						=False
				snake_case  						=False





				def 	__UpperCamelCase						(							self)     ->   List[str]:
											a__     						=DataaVecVisionModelTester(self)
											a__     						=ConfigTester(
											    self					,     config_class=lowercase_					,     has_text_modality=lowercase_					,     hidden_size=37)





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											self.config_tester.run_common_tests()





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											# Data2VecVision does not use inputs_embeds
											pass





				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
				def 	__UpperCamelCase						(							self)     ->   Union[str, Any]:
											pass





				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_)
																		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)     ->   int:
											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)     ->   List[str]:
											a__     						=self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_)





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





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											if not self.model_tester.is_training:
																		return

											a__						,    a__     						=self.model_tester.prepare_config_and_inputs_for_common()
											a__     						=True

											for model_class in self.all_model_classes:
																		if model_class in [*get_values(lowercase_)]:
																									continue

																		a__     						=model_class(lowercase_)
																		model.to(lowercase_)
																		model.train()
																		a__     						=self._prepare_for_class(lowercase_					,     lowercase_					,     return_labels=lowercase_)
																		a__     						=model(**lowercase_).loss
																		loss.backward()





				def 	__UpperCamelCase						(							self)     ->   Optional[int]:
											a__						,    a__     						=self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																		return

											a__     						=False
											a__     						=True

											for model_class in self.all_model_classes:
																		if model_class in [*get_values(lowercase_)] or not model_class.supports_gradient_checkpointing:
																									continue
																		# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
																		# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																		elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																									a__						,    a__						,    a__						,    a__     						=inputs_dict['pixel_values'].shape
																									a__     						=torch.zeros(
																									    [self.model_tester.batch_size, height, width]					,     device=lowercase_).long()
																		a__     						=model_class(lowercase_)
																		model.gradient_checkpointing_enable()
																		model.to(lowercase_)
																		model.train()
																		a__     						=self._prepare_for_class(lowercase_					,     lowercase_					,     return_labels=lowercase_)
																		a__     						=model(**lowercase_).loss
																		loss.backward()





				def 	__UpperCamelCase						(							self)     ->   Dict:
											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():
																									# we skip lambda parameters as these require special initial values
																									# determined by config.layer_scale_init_value
																									if "lambda" in name:
																																continue
																									if 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"""					,     )





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=2e-4					,     lowercase_="outputs"					,     lowercase_=None)     ->   List[str]:
											# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
											super().check_pt_tf_outputs(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)





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





				@slow
				def 	__UpperCamelCase						(							self)     ->   int:
											for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		a__     						=DataaVecVisionModel.from_pretrained(lowercase_)
																		self.assertIsNotNone(lowercase_)







def      _lowercase(				):
							a__     						=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
							return image







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





				@cached_property
				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											return (
											    BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k') if is_vision_available() else None
											)





				@slow
				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__     						=DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k').to(
											    lowercase_)

											a__     						=self.default_image_processor
											a__     						=prepare_img()
											a__     						=image_processor(images=lowercase_					,     return_tensors='pt').to(lowercase_)

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

											# verify the logits
											a__     						=torch.Size((1, 1000))
											self.assertEqual(logits.shape					,     lowercase_)

											a__     						=torch.tensor([0.32_77, -0.13_95, 0.09_11]).to(lowercase_)

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

											a__     						=[model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
											self.assertEqual(logits[0].topk(2).indices.cpu().tolist()					,     lowercase_)





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')

