




'''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 						lowerCAmelCase_   :







		'''simple docstring'''







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






			"""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 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






			"""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    :	int						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	Tuple ):






			"""simple docstring"""






			UpperCAmelCase__										=    DinatModel(config=_UpperCAmelCase )
			model.to(_UpperCAmelCase )
			model.eval()
			UpperCAmelCase__										=    model(_UpperCAmelCase )

			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 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	str ):






			"""simple docstring"""






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

			# test greyscale images
			UpperCAmelCase__										=    1
			UpperCAmelCase__										=    DinatForImageClassification(_UpperCAmelCase )
			model.to(_UpperCAmelCase )
			model.eval()

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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	Any ):






			"""simple docstring"""






			UpperCAmelCase__										=    DinatBackbone(config=_UpperCAmelCase )
			model.to(_UpperCAmelCase )
			model.eval()
			UpperCAmelCase__										=    model(_UpperCAmelCase )

			# 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__										=    DinatBackbone(config=_UpperCAmelCase )
			model.to(_UpperCAmelCase )
			model.eval()
			UpperCAmelCase__										=    model(_UpperCAmelCase )

			# 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    :	Tuple ):






			"""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 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







		'''simple docstring'''







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

		lowerCAmelCase_				:					Union[str, Any]				     =						False
		lowerCAmelCase_				:					List[str]				     =						False
		lowerCAmelCase_				:					Any				     =						False
		lowerCAmelCase_				:					Any				     =						False


		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






			"""simple docstring"""






			UpperCAmelCase__										=    DinatModelTester(self )
			UpperCAmelCase__										=    ConfigTester(self						,		config_class=_UpperCAmelCase						,		embed_dim=37 )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[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    :	int ):






			"""simple docstring"""






			return


		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_UpperCAmelCase )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






			"""simple docstring"""






			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*_UpperCAmelCase )


		@unittest.skip(reason="""Dinat does not use inputs_embeds""" )
		def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






			"""simple docstring"""






			pass


		@unittest.skip(reason="""Dinat does not use feedforward chunking""" )
		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






			"""simple docstring"""






			pass


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






			"""simple docstring"""






			UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






			"""simple docstring"""






			UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				UpperCAmelCase__										=    model_class(_UpperCAmelCase )
				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]						,		_UpperCAmelCase )


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






			"""simple docstring"""






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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	Dict ):






			"""simple docstring"""






			UpperCAmelCase__										=    model_class(_UpperCAmelCase )
			model.to(_UpperCAmelCase )
			model.eval()

			with torch.no_grad():
				UpperCAmelCase__										=    model(**self._prepare_for_class(_UpperCAmelCase						,		_UpperCAmelCase ) )

			UpperCAmelCase__										=    outputs.hidden_states

			UpperCAmelCase__										=    getattr(
			    self.model_tester						,		"""expected_num_hidden_layers"""						,		len(self.model_tester.depths ) + 1 )
			self.assertEqual(len(_UpperCAmelCase )						,		_UpperCAmelCase )

			# Dinat 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__ != "DinatBackbone":
				UpperCAmelCase__										=    outputs.reshaped_hidden_states
				self.assertEqual(len(_UpperCAmelCase )						,		_UpperCAmelCase )

				UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    reshaped_hidden_states[0].shape
				UpperCAmelCase__										=    (
				    reshaped_hidden_states[0].view(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase ).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    :	Optional[int] ):






			"""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(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )

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

				self.check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )


		@slow
		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






			"""simple docstring"""






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



		def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






			"""simple docstring"""






			UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

			UpperCAmelCase__										=    _config_zero_init(_UpperCAmelCase )
			for model_class in self.all_model_classes:
				UpperCAmelCase__										=    model_class(config=_UpperCAmelCase )
				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 						lowerCAmelCase_   ( unittest.TestCase   ):







		'''simple docstring'''







		@cached_property
		def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






			"""simple docstring"""






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



		@slow
		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






			"""simple docstring"""






			UpperCAmelCase__										=    DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224""" ).to(_UpperCAmelCase )
			UpperCAmelCase__										=    self.default_image_processor

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

			# forward pass
			with torch.no_grad():
				UpperCAmelCase__										=    model(**_UpperCAmelCase )

			# verify the logits
			UpperCAmelCase__										=    torch.Size((1, 10_00) )
			self.assertEqual(outputs.logits.shape						,		_UpperCAmelCase )
			UpperCAmelCase__										=    torch.tensor([-0.1545, -0.7667, 0.4642] ).to(_UpperCAmelCase )
			self.assertTrue(torch.allclose(outputs.logits[0, :3]						,		_UpperCAmelCase						,		atol=1E-4 ) )

@require_torch
@require_natten
class 						lowerCAmelCase_   ( unittest.TestCase			,  lowerCamelCase_   ):







		'''simple docstring'''







		lowerCAmelCase_				:					Any				     =						(DinatBackbone,) if is_torch_available() else ()
		lowerCAmelCase_				:					Optional[Any]				     =						DinatConfig


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






			"""simple docstring"""






			UpperCAmelCase__										=    DinatModelTester(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 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Any				     =						IFImgaImgPipeline
  lowerCAmelCase_				:					Dict				     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
  lowerCAmelCase_				:					Union[str, Any]				     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCAmelCase_				:					Dict				     =						PipelineTesterMixin.required_optional_params - {"""latents"""}


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._get_dummy_components()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Any=0 ):






   """simple docstring"""






   if str(_UpperCAmelCase ).startswith("""mps""" ):
    UpperCAmelCase__										=    torch.manual_seed(_UpperCAmelCase )
   else:
    UpperCAmelCase__										=    torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )

   UpperCAmelCase__										=    floats_tensor((1, 3, 32, 32)						,		rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )

   UpperCAmelCase__										=    {
       """prompt""": """A painting of a squirrel eating a burger""",
       """image""": image,
       """generator""": generator,
       """num_inference_steps""": 2,
       """output_type""": """numpy""",
   }

   return inputs


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any] ):






   """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 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   super().test_save_load_floataa(expected_max_diff=1E-1 )


  @unittest.skipIf(torch_device != """cuda"""						,		reason="""float16 requires CUDA""" )
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   super().test_floataa_inference(expected_max_diff=1E-1 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   self._test_save_load_local()



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






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