import collections
import inspect
import unittest

from transformers import NatConfig
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 NatBackbone, NatForImageClassification, NatModel
				from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=13       ,			__UpperCamelCase=64       ,			__UpperCamelCase=4       ,			__UpperCamelCase=3       ,			__UpperCamelCase=16       ,			__UpperCamelCase=[1, 2, 1]       ,			__UpperCamelCase=[2, 4, 8]       ,			__UpperCamelCase=3       ,			__UpperCamelCase=2.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase="gelu"       ,			__UpperCamelCase=True       ,			__UpperCamelCase=0.0_2       ,			__UpperCamelCase=1E-5       ,			__UpperCamelCase=True       ,			__UpperCamelCase=None       ,			__UpperCamelCase=True       ,			__UpperCamelCase=10       ,			__UpperCamelCase=["stage1", "stage2"]       ,			__UpperCamelCase=[1, 2]       ,			)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Any   				= parent
									snake_case__   :      Optional[Any]   				= batch_size
									snake_case__   :      Tuple   				= image_size
									snake_case__   :      List[str]   				= patch_size
									snake_case__   :      Optional[Any]   				= num_channels
									snake_case__   :      str   				= embed_dim
									snake_case__   :      List[Any]   				= depths
									snake_case__   :      Any   				= num_heads
									snake_case__   :      str   				= kernel_size
									snake_case__   :      Any   				= mlp_ratio
									snake_case__   :      str   				= qkv_bias
									snake_case__   :      List[Any]   				= hidden_dropout_prob
									snake_case__   :      List[str]   				= attention_probs_dropout_prob
									snake_case__   :      Tuple   				= drop_path_rate
									snake_case__   :      Dict   				= hidden_act
									snake_case__   :      Union[str, Any]   				= patch_norm
									snake_case__   :      int   				= layer_norm_eps
									snake_case__   :      Any   				= initializer_range
									snake_case__   :      Tuple   				= is_training
									snake_case__   :      int   				= scope
									snake_case__   :      Any   				= use_labels
									snake_case__   :      Any   				= num_labels
									snake_case__   :      List[Any]   				= out_features
									snake_case__   :      List[Any]   				= out_indices




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									snake_case__   :      Any   				= None
									if self.use_labels:
													snake_case__   :      Tuple   				= ids_tensor([self.batch_size]       ,			self.num_labels						)

									snake_case__   :      List[str]   				= self.get_config()

									return config, pixel_values, labels




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									return NatConfig(
									    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       ,			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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Any   				= NatModel(config=__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      int   				= model(__UpperCamelCase						)

									snake_case__   :      List[str]   				= (config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
									snake_case__   :      Optional[int]   				= 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      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= NatForImageClassification(__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      Dict   				= model(__UpperCamelCase       ,			labels=__UpperCamelCase						)
									self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)						)

									# test greyscale images
									snake_case__   :      Dict   				= 1
									snake_case__   :      Optional[Any]   				= NatForImageClassification(__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()

									snake_case__   :      Tuple   				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									snake_case__   :      Dict   				= model(__UpperCamelCase						)
									self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      int   				= NatBackbone(config=__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      List[Any]   				= model(__UpperCamelCase						)

									# 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
									snake_case__   :      List[Any]   				= None
									snake_case__   :      int   				= NatBackbone(config=__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()
									snake_case__   :      Optional[int]   				= model(__UpperCamelCase						)

									# 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      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      int   				= self.prepare_config_and_inputs()
									snake_case__   :      Optional[int]   				= config_and_inputs
									snake_case__   :      List[Any]   				= {'''pixel_values''': pixel_values}
									return config, inputs_dict





@require_natten
@require_torch
class 					__snake_case    (     _lowerCamelCase       ,_lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  (
					    (
					        NatModel,
					        NatForImageClassification,
					        NatBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					__lowerCamelCase			   =  (
					    {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
					    if is_torch_available()
					    else {}
					)
					__lowerCamelCase			   =  False

					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= NatModelTester(self						)
									snake_case__   :      Union[str, Any]   				= ConfigTester(self       ,			config_class=__UpperCamelCase       ,			embed_dim=37						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									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      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									return




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase						)




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Tuple   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__UpperCamelCase						)




					@unittest.skip(reason='Nat does not use inputs_embeds'						)
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									pass




					@unittest.skip(reason='Nat does not use feedforward chunking'						)
					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									pass




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.model_tester.prepare_config_and_inputs_for_common()

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




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= self.model_tester.prepare_config_and_inputs_for_common()

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

													snake_case__   :      Tuple   				= ['''pixel_values''']
													self.assertListEqual(arg_names[:1]       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.'						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Dict   				= model_class(__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()

									with torch.no_grad():
													snake_case__   :      Tuple   				= model(**self._prepare_for_class(__UpperCamelCase       ,			__UpperCamelCase						)						)

									snake_case__   :      List[Any]   				= outputs.hidden_states

									snake_case__   :      Tuple   				= getattr(
									    self.model_tester       ,			'expected_num_hidden_layers'       ,			len(self.model_tester.depths						) + 1						)
									self.assertEqual(len(__UpperCamelCase						)       ,			__UpperCamelCase						)

									# Nat has a different seq_length
									snake_case__   :      Optional[Any]   				= (
									    config.patch_size
									    if isinstance(config.patch_size       ,			collections.abc.Iterable						)
									    else (config.patch_size, config.patch_size)
									)

									snake_case__   :      Union[str, Any]   				= image_size[0] // patch_size[0]
									snake_case__   :      List[str]   				= 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__ != "NatBackbone":
													snake_case__   :      str   				= outputs.reshaped_hidden_states
													self.assertEqual(len(__UpperCamelCase						)       ,			__UpperCamelCase						)

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




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= self.model_tester.prepare_config_and_inputs_for_common()

									snake_case__   :      Any   				= (
									    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:
													snake_case__   :      Union[str, Any]   				= True
													self.check_hidden_states_output(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													snake_case__   :      Union[str, Any]   				= True

													self.check_hidden_states_output(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)




					@slow
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case__   :      Any   				= NatModel.from_pretrained(__UpperCamelCase						)
													self.assertIsNotNone(__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= self.model_tester.prepare_config_and_inputs_for_common()

									snake_case__   :      Dict   				= _config_zero_init(__UpperCamelCase						)
									for model_class in self.all_model_classes:
													snake_case__   :      int   				= model_class(config=__UpperCamelCase						)
													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 					__snake_case    (     unittest.TestCase      ):




					@cached_property
					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224'						) if is_vision_available() else None




					@slow
					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      int   				= NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224'						).to(__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= self.default_image_processor

									snake_case__   :      Optional[int]   				= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									snake_case__   :      str   				= image_processor(images=__UpperCamelCase       ,			return_tensors='pt'						).to(__UpperCamelCase						)

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

									# verify the logits
									snake_case__   :      List[Any]   				= torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape       ,			__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]						).to(__UpperCamelCase						)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,			__UpperCamelCase       ,			atol=1E-4						)						)





@require_torch
@require_natten
class 					__snake_case    (     unittest.TestCase       ,_lowerCamelCase      ):
					__lowerCamelCase			   =  (NatBackbone,) if is_torch_available() else ()
					__lowerCamelCase			   =  NatConfig




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= NatModelTester(self						)



'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

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


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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