




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__				:
				"""simple docstring"""



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

							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]   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Any				)   ->	Union[str, Any]:
							a       =     NatForImageClassification(__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase   , labels=__lowerCamelCase				)
							self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)				)

							# test greyscale images
							a       =     1
							a       =     NatForImageClassification(__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()

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





				def 				__UpperCAmelCase				( self							:	Dict   , __lowerCamelCase							:	Optional[int]   , __lowerCamelCase							:	int   , __lowerCamelCase							:	Union[str, Any]				)   ->	List[Any]:
							a       =     NatBackbone(config=__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase				)

							# 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=__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()
							a       =     model(__lowerCamelCase				)

							# 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[Any]				)   ->	Optional[Any]:
							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        snake_case__				(_UpperCamelCase       ,			_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   (
				    (
				        NatModel,
				        NatForImageClassification,
				        NatBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_			:				List[Any]									=   (
				    {"""feature-extraction""": NatModel, """image-classification""": NatForImageClassification}
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_			:				List[Any]									=   False

				SCREAMING_SNAKE_CASE_			:				str									=   False
				SCREAMING_SNAKE_CASE_			:				str									=   False
				SCREAMING_SNAKE_CASE_			:				Tuple									=   False
				SCREAMING_SNAKE_CASE_			:				int									=   False





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Any:
							a       =     NatModelTester(self				)
							a       =     ConfigTester(self   , config_class=__lowerCamelCase   , embed_dim=37				)





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	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							:	Any				)   ->	Optional[Any]:
							return





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Tuple:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	str				)   ->	List[str]:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							a       =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__lowerCamelCase				)





				@unittest.skip(reason="Nat does not use inputs_embeds"				)
				def 				__UpperCAmelCase				( self							:	str				)   ->	Optional[Any]:
							pass





				@unittest.skip(reason="Nat does not use feedforward chunking"				)
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							pass





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

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





				def 				__UpperCAmelCase				( self							:	str				)   ->	Optional[Any]:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										a       =     model_class(__lowerCamelCase				)
										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]   , __lowerCamelCase				)





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





				def 				__UpperCAmelCase				( self							:	str   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	List[Any]   , __lowerCamelCase							:	List[Any]				)   ->	Optional[int]:
							a       =     model_class(__lowerCamelCase				)
							model.to(__lowerCamelCase				)
							model.eval()

							with torch.no_grad():
										a       =     model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase				)				)

							a       =     outputs.hidden_states

							a       =     getattr(
							    self.model_tester   , "expected_num_hidden_layers"   , len(self.model_tester.depths				) + 1				)
							self.assertEqual(len(__lowerCamelCase				)   , __lowerCamelCase				)

							# 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(__lowerCamelCase				)   , __lowerCamelCase				)

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





				def 				__UpperCAmelCase				( self							:	Any				)   ->	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(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)

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

										self.check_hidden_states_output(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)





				@slow
				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	str:
							for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a       =     NatModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	List[str]:
							a       ,						a       =     self.model_tester.prepare_config_and_inputs_for_common()

							a       =     _config_zero_init(__lowerCamelCase				)
							for model_class in self.all_model_classes:
										a       =     model_class(config=__lowerCamelCase				)
										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  ):
				"""simple docstring"""



				@cached_property
				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	List[str]:
							return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"				) if is_vision_available() else None





				@slow
				def 				__UpperCAmelCase				( self							:	int				)   ->	List[str]:
							a       =     NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"				).to(__lowerCamelCase				)
							a       =     self.default_image_processor

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

							# forward pass
							with torch.no_grad():
										a       =     model(**__lowerCamelCase				)

							# verify the logits
							a       =     torch.Size((1, 10_00)				)
							self.assertEqual(outputs.logits.shape   , __lowerCamelCase				)
							a       =     torch.tensor([0.3_805, -0.8_676, -0.3_912]				).to(__lowerCamelCase				)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]   , __lowerCamelCase   , atol=1e-4				)				)




@require_torch
@require_natten
class        snake_case__				(unittest.TestCase       ,			_UpperCamelCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   (NatBackbone,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   NatConfig





				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	List[str]:
							a       =     NatModelTester(self				)












import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   """hf-internal-testing/tiny-stable-diffusion-pipe"""
				SCREAMING_SNAKE_CASE_			:				str									=   1
				SCREAMING_SNAKE_CASE_			:				Dict									=   77
				SCREAMING_SNAKE_CASE_			:				List[str]									=   32
				SCREAMING_SNAKE_CASE_			:				List[str]									=   4
				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   64
				SCREAMING_SNAKE_CASE_			:				int									=   torch.manual_seed(0  )





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	List[Any]=0.9_999				)   ->	Tuple:
							a       =     UNetaDConditionModel.from_pretrained(self.model_id   , subfolder="unet"				)
							a       =     unet.to(__lowerCamelCase				)
							a       =     EMAModel(unet.parameters()   , decay=__lowerCamelCase   , model_cls=__lowerCamelCase   , model_config=unet.config				)
							return unet, ema_unet





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Any:
							a       =     torch.randn(
							    self.batch_size   , self.num_in_channels   , self.latent_height   , self.latent_width   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randint(0   , 10_00   , size=(self.batch_size,)   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randn(
							    self.batch_size   , self.prompt_length   , self.text_encoder_hidden_dim   , generator=self.generator				).to(__lowerCamelCase				)
							return noisy_latents, timesteps, encoder_hidden_states





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Any				)   ->	Optional[int]:
							a       =     {}
							for k, param in unet.state_dict().items():
										a       =     torch.randn_like(__lowerCamelCase				) + (param * torch.randn_like(__lowerCamelCase				))
										updated_state_dict.update({k: updated_param}				)
							unet.load_state_dict(__lowerCamelCase				)
							return unet





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Union[str, Any]:
							a       ,						a       =     self.get_models()
							# Take the first (hypothetical) EMA step.
							ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 1

							# Take two more.
							for _ in range(2				):
										ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 3





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[int]:
							a       ,						a       =     self.get_models()
							# Since the `unet` is not being updated (i.e., backprop'd)
							# there won't be any difference between the `params` of `unet`
							# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
							ema_unet.step(unet.parameters()				)
							a       =     list(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# The above holds true even if we call `ema.step()` multiple times since
							# `unet` params are still not being updated.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							a       ,						a       =     self.get_models()
							# Here we simulate the parameter updates for `unet`. Since there might
							# be some parameters which are initialized to zero we take extra care to
							# initialize their values to something non-zero before the multiplication.
							a       =     self.simulate_backprop(__lowerCamelCase				)

							# Take the EMA step.
							ema_unet.step(unet_pseudo_updated_step_one.parameters()				)

							# Now the EMA'd parameters won't be equal to the original model parameters.
							a       =     list(unet_pseudo_updated_step_one.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# Ensure this is the case when we take multiple EMA steps.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Any:
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							a       ,						a       =     self.get_models()

							# First backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							# Second backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							a       ,						a       =     self.get_models(decay=0.0				)
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Dict:
							a       ,						a       =     self.get_models()
							a       ,						a       ,						a       =     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(__lowerCamelCase				)
										a       =     UNetaDConditionModel.from_pretrained(__lowerCamelCase   , model_cls=__lowerCamelCase				)
										a       =     loaded_unet.to(unet.device				)

							# Since no EMA step has been performed the outputs should match.
							a       =     unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample
							a       =     loaded_unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample

							assert torch.allclose(__lowerCamelCase   , __lowerCamelCase   , atol=1e-4				)






