
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 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				str							,A      :				Any							,A      :				str=13							,A      :				Tuple=64							,A      :				int=4							,A      :				Optional[Any]=3							,A      :				Any=16							,A      :				List[Any]=[1, 2, 1]							,A      :				List[Any]=[2, 4, 8]							,A      :				Optional[int]=3							,A      :				Tuple=2.0							,A      :				List[Any]=True							,A      :				Union[str, Any]=0.0							,A      :				Any=0.0							,A      :				Union[str, Any]=0.1							,A      :				Optional[Any]="gelu"							,A      :				str=True							,A      :				Any=0.02							,A      :				Any=1E-5							,A      :				Union[str, Any]=True							,A      :				Optional[Any]=None							,A      :				Dict=True							,A      :				int=10							,A      :				List[str]=["stage1", "stage2"]							,A      :				Optional[Any]=[1, 2]							,):
					__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         =				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      :				List[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      :				Union[str, Any]				):
					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        UpperCamelCase_						( self      :				List[str]							,A      :				Any							,A      :				Dict							,A      :				Tuple				):
					__A         =				NatModel(config=A				)
					model.to(A				)
					model.eval()
					__A         =				model(A				)

					__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      :				List[str]							,A      :				List[Any]							,A      :				Optional[Any]							,A      :				Dict				):
					__A         =				NatForImageClassification(A				)
					model.to(A				)
					model.eval()
					__A         =				model(A							,labels=A				)
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)				)

					# test greyscale images
					__A         =				1
					__A         =				NatForImageClassification(A				)
					model.to(A				)
					model.eval()

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






	def        UpperCamelCase_						( self      :				Dict							,A      :				Any							,A      :				Union[str, Any]							,A      :				Tuple				):
					__A         =				NatBackbone(config=A				)
					model.to(A				)
					model.eval()
					__A         =				model(A				)

					# 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         =				NatBackbone(config=A				)
					model.to(A				)
					model.eval()
					__A         =				model(A				)

					# 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      :				Tuple				):
					__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 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  (
	    (
	        NatModel,
	        NatForImageClassification,
	        NatBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case_      					=  (
	    {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
	    if is_torch_available()
	    else {}
	)
	snake_case_      					=  False

	snake_case_      					=  False
	snake_case_      					=  False
	snake_case_      					=  False
	snake_case_      					=  False






	def        UpperCamelCase_						( self      :				int				):
					__A         =				NatModelTester(self				)
					__A         =				ConfigTester(self							,config_class=A							,embed_dim=37				)






	def        UpperCamelCase_						( self      :				Optional[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      :				int				):
					return






	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A				)






	def        UpperCamelCase_						( self      :				Optional[int]				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A				)






	def        UpperCamelCase_						( self      :				int				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*A				)






	@unittest.skip(reason="Nat does not use inputs_embeds"				)
	def        UpperCamelCase_						( self      :				List[str]				):
					pass






	@unittest.skip(reason="Nat does not use feedforward chunking"				)
	def        UpperCamelCase_						( self      :				Tuple				):
					pass






	def        UpperCamelCase_						( self      :				List[Any]				):
					__A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

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






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A         =				model_class(A				)
									__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]							,A				)






	def        UpperCamelCase_						( self      :				Optional[int]				):
					self.skipTest("Nat's attention operation is handled entirely by NATTEN."				)






	def        UpperCamelCase_						( self      :				Tuple							,A      :				Tuple							,A      :				Optional[Any]							,A      :				Optional[Any]							,A      :				List[str]				):
					__A         =				model_class(A				)
					model.to(A				)
					model.eval()

					with torch.no_grad():
									__A         =				model(**self._prepare_for_class(A							,A				)				)

					__A         =				outputs.hidden_states

					__A         =				getattr(
					    self.model_tester							,"expected_num_hidden_layers"							,len(self.model_tester.depths				) + 1				)
					self.assertEqual(len(A				)							,A				)

					# Nat 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__ != "NatBackbone":
									__A         =				outputs.reshaped_hidden_states
									self.assertEqual(len(A				)							,A				)

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






	def        UpperCamelCase_						( self      :				Dict				):
					__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(A							,A							,A							,A				)

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

									self.check_hidden_states_output(A							,A							,A							,A				)






	@slow
	def        UpperCamelCase_						( self      :				Optional[Any]				):
					for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A         =				NatModel.from_pretrained(A				)
									self.assertIsNotNone(A				)







	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					__A    ,							__A         =				self.model_tester.prepare_config_and_inputs_for_common()

					__A         =				_config_zero_init(A				)
					for model_class in self.all_model_classes:
									__A         =				model_class(config=A				)
									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 							UpperCAmelCase   (						unittest.TestCase						):




	'''simple docstring'''





	@cached_property
	def        UpperCamelCase_						( self      :				Dict				):
					return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"				) if is_vision_available() else None







	@slow
	def        UpperCamelCase_						( self      :				Any				):
					__A         =				NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"				).to(A				)
					__A         =				self.default_image_processor

					__A         =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					__A         =				image_processor(images=A							,return_tensors="pt"				).to(A				)

					# forward pass
					with torch.no_grad():
									__A         =				model(**A				)

					# verify the logits
					__A         =				torch.Size((1, 10_00)				)
					self.assertEqual(outputs.logits.shape							,A				)
					__A         =				torch.tensor([0.38_05, -0.86_76, -0.39_12]				).to(A				)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,A							,atol=1E-4				)				)





@require_torch
@require_natten
class 							UpperCAmelCase   (						unittest.TestCase    ,    __SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  (NatBackbone,) if is_torch_available() else ()
	snake_case_      					=  NatConfig






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				NatModelTester(self				)


from typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
