
'''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 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:Union[str, Any]						,					_A	:Dict=13						,					_A	:Union[str, Any]=64						,					_A	:List[Any]=4						,					_A	:Union[str, Any]=3						,					_A	:Tuple=16						,					_A	:List[str]=[1, 2, 1]						,					_A	:Union[str, Any]=[2, 4, 8]						,					_A	:Tuple=3						,					_A	:Any=2.0						,					_A	:int=True						,					_A	:int=0.0						,					_A	:int=0.0						,					_A	:int=0.1						,					_A	:List[str]="gelu"						,					_A	:Optional[Any]=True						,					_A	:Any=0.02						,					_A	:List[str]=1E-5						,					_A	:List[Any]=True						,					_A	:Optional[int]=None						,					_A	:Optional[Any]=True						,					_A	:Dict=10						,					_A	:List[str]=["stage1", "stage2"]						,					_A	:Union[str, Any]=[1, 2]						,					)					->      Optional[Any]:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       __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 						lowercase_    (      self	:List[Any]      )					->      str:







       '''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 						lowercase_    (      self	:Tuple						,					_A	:Dict						,					_A	:Dict						,					_A	:Dict      )					->      str:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Optional[int]						,					_A	:str						,					_A	:Optional[int]						,					_A	:Optional[int]      )					->      Union[str, Any]:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Optional[Any]						,					_A	:int						,					_A	:str						,					_A	:List[Any]      )					->      int:







       '''simple docstring'''



       __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 						lowercase_    (      self	:str      )					->      Tuple:







       '''simple docstring'''



       __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):
 UpperCAmelCase__     :     List[str]    					=       (
     (
         NatModel,
         NatForImageClassification,
         NatBackbone,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase__     :     int    					=       (
     {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
     if is_torch_available()
     else {}
 )
 UpperCAmelCase__     :     Dict    					=       False

 UpperCAmelCase__     :     int    					=       False
 UpperCAmelCase__     :     str    					=       False
 UpperCAmelCase__     :     List[str]    					=       False
 UpperCAmelCase__     :     List[str]    					=       False






 def 						lowercase_    (      self	:Optional[Any]      )					->      Tuple:







       '''simple docstring'''



       __A													=      NatModelTester(self      )
       __A													=      ConfigTester(self						,					config_class=_A						,					embed_dim=37      )






 def 						lowercase_    (      self	:Any      )					->      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 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return






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







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A      )






 def 						lowercase_    (      self	:str      )					->      List[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_A      )






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







       '''simple docstring'''



       __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 						lowercase_    (      self	:int      )					->      Union[str, Any]:







       '''simple docstring'''



       pass






 @unittest.skip(reason='Nat does not use feedforward chunking'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      str:







       '''simple docstring'''



       pass






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







       '''simple docstring'''



       __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 						lowercase_    (      self	:List[Any]      )					->      Optional[Any]:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Optional[int]      )					->      str:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Tuple						,					_A	:Tuple						,					_A	:Dict						,					_A	:Any						,					_A	:List[Any]      )					->      Dict:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Tuple      )					->      Dict:







       '''simple docstring'''



       __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 						lowercase_    (      self	:Tuple      )					->      List[str]:







       '''simple docstring'''



       for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A													=      NatModel.from_pretrained(_A      )
             self.assertIsNotNone(_A      )






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







       '''simple docstring'''



       __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):






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







       '''simple docstring'''



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






 @slow
 def 						lowercase_    (      self	:str      )					->      Tuple:







       '''simple docstring'''



       __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, 1_000)      )
       self.assertEqual(outputs.logits.shape						,					_A      )
       __A													=      torch.tensor([0.3_805, -0.8_676, -0.3_912]      ).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):
 UpperCAmelCase__     :     Any    					=       (NatBackbone,) if is_torch_available() else ()
 UpperCAmelCase__     :     Dict    					=       NatConfig






 def 						lowercase_    (      self	:Tuple      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      NatModelTester(self      )







"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



