


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   A__							:




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=parent
										_SCREAMING_SNAKE_CASE     					=batch_size
										_SCREAMING_SNAKE_CASE     					=image_size
										_SCREAMING_SNAKE_CASE     					=patch_size
										_SCREAMING_SNAKE_CASE     					=num_channels
										_SCREAMING_SNAKE_CASE     					=embed_dim
										_SCREAMING_SNAKE_CASE     					=depths
										_SCREAMING_SNAKE_CASE     					=num_heads
										_SCREAMING_SNAKE_CASE     					=kernel_size
										_SCREAMING_SNAKE_CASE     					=dilations
										_SCREAMING_SNAKE_CASE     					=mlp_ratio
										_SCREAMING_SNAKE_CASE     					=qkv_bias
										_SCREAMING_SNAKE_CASE     					=hidden_dropout_prob
										_SCREAMING_SNAKE_CASE     					=attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE     					=drop_path_rate
										_SCREAMING_SNAKE_CASE     					=hidden_act
										_SCREAMING_SNAKE_CASE     					=patch_norm
										_SCREAMING_SNAKE_CASE     					=layer_norm_eps
										_SCREAMING_SNAKE_CASE     					=initializer_range
										_SCREAMING_SNAKE_CASE     					=is_training
										_SCREAMING_SNAKE_CASE     					=scope
										_SCREAMING_SNAKE_CASE     					=use_labels
										_SCREAMING_SNAKE_CASE     					=num_labels
										_SCREAMING_SNAKE_CASE     					=out_features
										_SCREAMING_SNAKE_CASE     					=out_indices




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										_SCREAMING_SNAKE_CASE     					=None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE     					=ids_tensor([self.batch_size]			,      self.num_labels					)

										_SCREAMING_SNAKE_CASE     					=self.get_config()

										return config, pixel_values, labels




						def 							__UpperCamelCase			(    self   : List[str]					)							->       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 							__UpperCamelCase			(    self   : List[str]			,      _a   : Any			,      _a   : List[str]			,      _a   : Any					)							->       Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=DinatModel(config=_a					)
										model.to(_a					)
										model.eval()
										_SCREAMING_SNAKE_CASE     					=model(_a					)

										_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=(config.image_size // config.patch_size) // (2 ** (len(config.depths					) - 1))
										_SCREAMING_SNAKE_CASE     					=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   : Any			,      _a   : int			,      _a   : List[str]			,      _a   : Optional[int]					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=DinatForImageClassification(_a					)
										model.to(_a					)
										model.eval()
										_SCREAMING_SNAKE_CASE     					=model(_a			,      labels=_a					)
										self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_labels)					)

										# test greyscale images
										_SCREAMING_SNAKE_CASE     					=1
										_SCREAMING_SNAKE_CASE     					=DinatForImageClassification(_a					)
										model.to(_a					)
										model.eval()

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




						def 							__UpperCamelCase			(    self   : Any			,      _a   : Union[str, Any]			,      _a   : Optional[int]			,      _a   : Optional[Any]					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=DinatBackbone(config=_a					)
										model.to(_a					)
										model.eval()
										_SCREAMING_SNAKE_CASE     					=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
										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=DinatBackbone(config=_a					)
										model.to(_a					)
										model.eval()
										_SCREAMING_SNAKE_CASE     					=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   : Optional[int]					)							->       List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=config_and_inputs
										_SCREAMING_SNAKE_CASE     					={'''pixel_values''': pixel_values}
										return config, inputs_dict


@require_natten
@require_torch
class   A__							(						UpperCamelCase__				,       UpperCamelCase__				,       unittest.TestCase					):
						UpperCAmelCase     =  (
						    (
						        DinatModel,
						        DinatForImageClassification,
						        DinatBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase     =  (
						    {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase     =  False

						UpperCAmelCase     =  False
						UpperCAmelCase     =  False
						UpperCAmelCase     =  False
						UpperCAmelCase     =  False




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=DinatModelTester(self					)
										_SCREAMING_SNAKE_CASE     					=ConfigTester(self			,      config_class=_a			,      embed_dim=37					)




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


										"""simple docstring"""





										return




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a					)




						def 							__UpperCamelCase			(    self   : Any					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_a					)




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=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 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       List[str]:


										"""simple docstring"""





										pass




						@unittest.skip(reason='''Dinat does not use feedforward chunking'''					)
						def 							__UpperCamelCase			(    self   : Any					)							->       Dict:


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE     					=model_class(_a					)
														_SCREAMING_SNAKE_CASE     					=inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_SCREAMING_SNAKE_CASE     					=[*signature.parameters.keys()]

														_SCREAMING_SNAKE_CASE     					=['''pixel_values''']
														self.assertListEqual(arg_names[:1]			,      _a					)




						def 							__UpperCamelCase			(    self   : str					)							->       Dict:


										"""simple docstring"""





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




						def 							__UpperCamelCase			(    self   : Tuple			,      _a   : List[Any]			,      _a   : List[str]			,      _a   : Optional[int]			,      _a   : Tuple					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=model_class(_a					)
										model.to(_a					)
										model.eval()

										with torch.no_grad():
														_SCREAMING_SNAKE_CASE     					=model(**self._prepare_for_class(_a			,      _a					)					)

										_SCREAMING_SNAKE_CASE     					=outputs.hidden_states

										_SCREAMING_SNAKE_CASE     					=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
										_SCREAMING_SNAKE_CASE     					=(
										    config.patch_size
										    if isinstance(config.patch_size			,      collections.abc.Iterable					)
										    else (config.patch_size, config.patch_size)
										)

										_SCREAMING_SNAKE_CASE     					=image_size[0] // patch_size[0]
										_SCREAMING_SNAKE_CASE     					=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":
														_SCREAMING_SNAKE_CASE     					=outputs.reshaped_hidden_states
														self.assertEqual(len(_a					)			,      _a					)

														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=reshaped_hidden_states[0].shape
														_SCREAMING_SNAKE_CASE     					=(
														    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   : Union[str, Any]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE     					=(
										    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:
														_SCREAMING_SNAKE_CASE     					=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"]
														_SCREAMING_SNAKE_CASE     					=True

														self.check_hidden_states_output(_a			,      _a			,      _a			,      _a					)




						@slow
						def 							__UpperCamelCase			(    self   : Dict					)							->       Any:


										"""simple docstring"""





										for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE     					=DinatModel.from_pretrained(_a					)
														self.assertIsNotNone(_a					)




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.model_tester.prepare_config_and_inputs_for_common()

										_SCREAMING_SNAKE_CASE     					=_config_zero_init(_a					)
										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE     					=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   A__							(						unittest.TestCase					):




						@cached_property
						def 							__UpperCamelCase			(    self   : str					)							->       int:


										"""simple docstring"""





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




						@slow
						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''					).to(_a					)
										_SCREAMING_SNAKE_CASE     					=self.default_image_processor

										_SCREAMING_SNAKE_CASE     					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
										_SCREAMING_SNAKE_CASE     					=image_processor(images=_a			,      return_tensors='''pt'''					).to(_a					)

										# forward pass
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE     					=model(**_a					)

										# verify the logits
										_SCREAMING_SNAKE_CASE     					=torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape			,      _a					)
										_SCREAMING_SNAKE_CASE     					=torch.tensor([-0.15_45, -0.76_67, 0.46_42]					).to(_a					)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			,      _a			,      atol=1E-4					)					)


@require_torch
@require_natten
class   A__							(						unittest.TestCase				,       UpperCamelCase__					):
						UpperCAmelCase     =  (DinatBackbone,) if is_torch_available() else ()
						UpperCAmelCase     =  DinatConfig




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=DinatModelTester(self					)



import math

class   A__							:




						def __init__(    self   : Optional[int]			,      _a   : int			,      _a   : List[Any]			,      _a   : int					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=x
										_SCREAMING_SNAKE_CASE     					=y
										_SCREAMING_SNAKE_CASE     					=z




						def __repr__(    self   : List[str]					)							->       str:


										"""simple docstring"""





										return f"Point({self.x}, {self.y}, {self.z})"







def   lowerCamelCase(				a__		,a__):
				return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))


if __name__ == "__main__":
							import doctest

							doctest.testmod()