code: 
  """simple docstring"""
  
  
  
  
  
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  
  @dataclass
  class     __a			(					a__			):
         _lowerCamelCase							:							int       =			{
             "sld_warmup_steps": 15,
             "sld_guidance_scale": 20,
             "sld_threshold": 0.0,
             "sld_momentum_scale": 0.0,
             "sld_mom_beta": 0.0,
         }
         _lowerCamelCase							:							List[Any]       =			{
             "sld_warmup_steps": 10,
             "sld_guidance_scale": 1_000,
             "sld_threshold": 0.01,
             "sld_momentum_scale": 0.3,
             "sld_mom_beta": 0.4,
         }
         _lowerCamelCase							:							Optional[Any]       =			{
             "sld_warmup_steps": 7,
             "sld_guidance_scale": 2_000,
             "sld_threshold": 0.025,
             "sld_momentum_scale": 0.5,
             "sld_mom_beta": 0.7,
         }
         _lowerCamelCase							:							Optional[int]       =			{
             "sld_warmup_steps": 0,
             "sld_guidance_scale": 5_000,
             "sld_threshold": 1.0,
             "sld_momentum_scale": 0.5,
             "sld_mom_beta": 0.7,
         }
  
  
  
  
  
  
  @dataclass
  class     __a			(					a__			):
         _lowerCamelCase							:							Union[List[PIL.Image.Image], np.ndarray]
         _lowerCamelCase							:							Optional[List[bool]]
         _lowerCamelCase							:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
         _lowerCamelCase							:							Optional[str]
  
  
  if is_transformers_available() and is_torch_available():
    from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
    from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  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     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=13							,lowerCamelCase_=64							,lowerCamelCase_=4							,lowerCamelCase_=3							,lowerCamelCase_=16							,lowerCamelCase_=[1, 2, 1]							,lowerCamelCase_=[2, 4, 8]							,lowerCamelCase_=3							,lowerCamelCase_=2.0							,lowerCamelCase_=True							,lowerCamelCase_=0.0							,lowerCamelCase_=0.0							,lowerCamelCase_=0.1							,lowerCamelCase_="gelu"							,lowerCamelCase_=True							,lowerCamelCase_=0.0_2							,lowerCamelCase_=1e-5							,lowerCamelCase_=True							,lowerCamelCase_=None							,lowerCamelCase_=True							,lowerCamelCase_=10							,lowerCamelCase_=["stage1", "stage2"]							,lowerCamelCase_=[1, 2]							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					parent
            _UpperCAmelCase   	=					batch_size
            _UpperCAmelCase   	=					image_size
            _UpperCAmelCase   	=					patch_size
            _UpperCAmelCase   	=					num_channels
            _UpperCAmelCase   	=					embed_dim
            _UpperCAmelCase   	=					depths
            _UpperCAmelCase   	=					num_heads
            _UpperCAmelCase   	=					kernel_size
            _UpperCAmelCase   	=					mlp_ratio
            _UpperCAmelCase   	=					qkv_bias
            _UpperCAmelCase   	=					hidden_dropout_prob
            _UpperCAmelCase   	=					attention_probs_dropout_prob
            _UpperCAmelCase   	=					drop_path_rate
            _UpperCAmelCase   	=					hidden_act
            _UpperCAmelCase   	=					patch_norm
            _UpperCAmelCase   	=					layer_norm_eps
            _UpperCAmelCase   	=					initializer_range
            _UpperCAmelCase   	=					is_training
            _UpperCAmelCase   	=					scope
            _UpperCAmelCase   	=					use_labels
            _UpperCAmelCase   	=					num_labels
            _UpperCAmelCase   	=					out_features
            _UpperCAmelCase   	=					out_indices
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
            _UpperCAmelCase   	=					None
            if self.use_labels:
               _UpperCAmelCase   	=					ids_tensor([self.batch_size]							,self.num_labels		)
  
            _UpperCAmelCase   	=					self.get_config()
  
            return config, pixel_values, labels
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatModel(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
  
            _UpperCAmelCase   	=					_UpperCAmelCase   	=					(config.image_size // config.patch_size) // (2 ** (len(config.depths		) - 1))
            _UpperCAmelCase   	=					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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatForImageClassification(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					model(lowerCamelCase_							,labels=lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)
  
            # test greyscale images
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					NatForImageClassification(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
  
            _UpperCAmelCase   	=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
            _UpperCAmelCase   	=					model(lowerCamelCase_		)
            self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatBackbone(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					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
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					NatBackbone(config=lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
            _UpperCAmelCase   	=					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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.prepare_config_and_inputs()
            _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					config_and_inputs
            _UpperCAmelCase   	=					{"pixel_values": pixel_values}
            return config, inputs_dict
  
  
  
  
  
  
  @require_natten
  @require_torch
  class     __a			(					lowerCAmelCase_				,	lowerCAmelCase_				,	unittest.TestCase			):
         _lowerCamelCase							:							str       =			(
             (
                 NatModel,
                 NatForImageClassification,
                 NatBackbone,
             )
             if is_torch_available()
             else ()
         )
         _lowerCamelCase							:							int       =			(
             {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
             if is_torch_available()
             else {}
         )
         _lowerCamelCase							:							Optional[Any]       =			False
  
         _lowerCamelCase							:							Optional[Any]       =			False
         _lowerCamelCase							:							Optional[int]       =			False
         _lowerCamelCase							:							List[str]       =			False
         _lowerCamelCase							:							str       =			False
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatModelTester(self		)
            _UpperCAmelCase   	=					ConfigTester(self							,config_class=lowerCamelCase_							,embed_dim=37		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            pass
  
  
  
  
  
  
  
         @unittest.skip(reason="Nat does not use feedforward chunking"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            pass
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               self.assertIsInstance(model.get_input_embeddings()							,(nn.Module)		)
               _UpperCAmelCase   	=					model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase_							,nn.Linear		)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
               _UpperCAmelCase   	=					inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _UpperCAmelCase   	=					[*signature.parameters.keys()]
  
               _UpperCAmelCase   	=					["pixel_values"]
               self.assertListEqual(arg_names[:1]							,lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            self.skipTest("Nat's attention operation is handled entirely by NATTEN."		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					model_class(lowerCamelCase_		)
            model.to(lowerCamelCase_		)
            model.eval()
  
            with torch.no_grad():
               _UpperCAmelCase   	=					model(**self._prepare_for_class(lowerCamelCase_							,lowerCamelCase_		)		)
  
            _UpperCAmelCase   	=					outputs.hidden_states
  
            _UpperCAmelCase   	=					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
            _UpperCAmelCase   	=					(
                config.patch_size
                if isinstance(config.patch_size							,collections.abc.Iterable		)
                else (config.patch_size, config.patch_size)
            )
  
            _UpperCAmelCase   	=					image_size[0] // patch_size[0]
            _UpperCAmelCase   	=					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":
               _UpperCAmelCase   	=					outputs.reshaped_hidden_states
               self.assertEqual(len(lowerCamelCase_		)							,lowerCamelCase_		)
  
               _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					reshaped_hidden_states[0].shape
               _UpperCAmelCase   	=					(
                   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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            _UpperCAmelCase   	=					(
                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:
               _UpperCAmelCase   	=					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"]
               _UpperCAmelCase   	=					True
  
               self.check_hidden_states_output(lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		)
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _UpperCAmelCase   	=					NatModel.from_pretrained(lowerCamelCase_		)
               self.assertIsNotNone(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.model_tester.prepare_config_and_inputs_for_common()
  
            _UpperCAmelCase   	=					_config_zero_init(lowerCamelCase_		)
            for model_class in self.all_model_classes:
               _UpperCAmelCase   	=					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     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         @cached_property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"		) if is_vision_available() else None
  
  
  
  
  
  
  
         @slow
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"		).to(lowerCamelCase_		)
            _UpperCAmelCase   	=					self.default_image_processor
  
            _UpperCAmelCase   	=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
            _UpperCAmelCase   	=					image_processor(images=lowerCamelCase_							,return_tensors="pt"		).to(lowerCamelCase_		)
  
            # forward pass
            with torch.no_grad():
               _UpperCAmelCase   	=					model(**lowerCamelCase_		)
  
            # verify the logits
            _UpperCAmelCase   	=					torch.Size((1, 1000)		)
            self.assertEqual(outputs.logits.shape							,lowerCamelCase_		)
            _UpperCAmelCase   	=					torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]		).to(lowerCamelCase_		)
            self.assertTrue(torch.allclose(outputs.logits[0, :3]							,lowerCamelCase_							,atol=1e-4		)		)
  
  
  
  
  
  
  @require_torch
  @require_natten
  class     __a			(					unittest.TestCase				,	lowerCAmelCase_			):
         _lowerCamelCase							:							Any       =			(NatBackbone,) if is_torch_available() else ()
         _lowerCamelCase							:							Dict       =			NatConfig
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					NatModelTester(self		)
  
  