




'''simple docstring'''





import collections
import inspect
import unittest

from transformers import DinatConfig
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 DinatBackbone, DinatForImageClassification, DinatModel
 from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       Tuple						,				A :       Optional[Any]						,				A :       int=13						,				A :       Optional[Any]=64						,				A :       List[Any]=4						,				A :       Optional[Any]=3						,				A :       Optional[int]=16						,				A :       Any=[1, 2, 1]						,				A :       Optional[int]=[2, 4, 8]						,				A :       Union[str, Any]=3						,				A :       Dict=[[3], [1, 2], [1]]						,				A :       str=2.0						,				A :       List[str]=True						,				A :       Optional[int]=0.0						,				A :       Dict=0.0						,				A :       List[Any]=0.1						,				A :       Tuple="gelu"						,				A :       Any=True						,				A :       str=0.0_2						,				A :       List[str]=1E-5						,				A :       Any=True						,				A :       Tuple=None						,				A :       List[Any]=True						,				A :       Any=10						,				A :       Optional[int]=["stage1", "stage2"]						,				A :       Dict=[1, 2]						,				)			->			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__      =       dilations
         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 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Optional[int]:






         """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 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Optional[int]:






         """simple docstring"""
         return DinatConfig(
             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						,				dilations=self.dilations						,				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 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       Any						,				A :       int						,				A :       int    )			->			Optional[int]:






         """simple docstring"""
         a__      =       DinatModel(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 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       Any						,				A :       str						,				A :       Optional[int]    )			->			int:






         """simple docstring"""
         a__      =       DinatForImageClassification(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__      =       DinatForImageClassification(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 				__SCREAMING_SNAKE_CASE		(      self :       Tuple						,				A :       List[str]						,				A :       Union[str, Any]						,				A :       Tuple    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       DinatBackbone(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__      =       DinatBackbone(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 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Optional[Any]:






         """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(		lowercase__       ,	lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    (
         (
             DinatModel,
             DinatForImageClassification,
             DinatBackbone,
         )
         if is_torch_available()
         else ()
     )
     __UpperCAmelCase	: Optional[int]						=    (
         {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
         if is_torch_available()
         else {}
     )
     __UpperCAmelCase	: List[str]						=    False

     __UpperCAmelCase	: Dict						=    False
     __UpperCAmelCase	: Optional[int]						=    False
     __UpperCAmelCase	: Optional[int]						=    False
     __UpperCAmelCase	: int						=    False

     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Dict:






         """simple docstring"""
         a__      =       DinatModelTester(self    )
         a__      =       ConfigTester(self						,				config_class=A						,				embed_dim=37    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			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 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			int:






         """simple docstring"""
         return

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






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A    )

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






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*A    )

     @unittest.skip(reason="""Dinat does not use inputs_embeds"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			List[Any]:






         """simple docstring"""
         pass

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






         """simple docstring"""
         pass

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			List[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    )
             self.assertIsInstance(model.get_input_embeddings()						,				(nn.Module)    )
             a__      =       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A						,				nn.Linear    )    )

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






         """simple docstring"""
         self.skipTest("""Dinat's attention operation is handled entirely by NATTEN."""    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       List[str]						,				A :       List[str]						,				A :       int						,				A :       List[Any]    )			->			List[str]:






         """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    )

         # Dinat 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__ != "DinatBackbone":
             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 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """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 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			Union[str, Any]:






         """simple docstring"""
         for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a__      =       DinatModel.from_pretrained(A    )
             self.assertIsNotNone(A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			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 ):





     """simple docstring"""



     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Optional[int]:






         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224"""    ) if is_vision_available() else None






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






         """simple docstring"""
         a__      =       DinatForImageClassification.from_pretrained("""shi-labs/dinat-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([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]    ).to(A    )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]						,				A						,				atol=1E-4    )    )







@require_torch
@require_natten
class 		__UpperCAmelCase(		unittest.TestCase       ,	lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: Union[str, Any]						=    (DinatBackbone,) if is_torch_available() else ()
     __UpperCAmelCase	: Dict						=    DinatConfig

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			int:






         """simple docstring"""
         a__      =       DinatModelTester(self    )












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import CamembertModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				dtype=torch.float						,				)
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(torch.allclose(output[:, :3, :3]						,				A						,				atol=1E-4    )    )






