

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__  :






		def __init__(	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							int=13							,    SCREAMING_SNAKE_CASE__	:							Tuple=64							,    SCREAMING_SNAKE_CASE__	:							Tuple=4							,    SCREAMING_SNAKE_CASE__	:							str=3							,    SCREAMING_SNAKE_CASE__	:							List[Any]=16							,    SCREAMING_SNAKE_CASE__	:							int=[1, 2, 1]							,    SCREAMING_SNAKE_CASE__	:							Dict=[2, 4, 8]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=3							,    SCREAMING_SNAKE_CASE__	:							Any=[[3], [1, 2], [1]]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=2.0							,    SCREAMING_SNAKE_CASE__	:							Any=True							,    SCREAMING_SNAKE_CASE__	:							Tuple=0.0							,    SCREAMING_SNAKE_CASE__	:							List[str]=0.0							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=0.1							,    SCREAMING_SNAKE_CASE__	:							List[str]="gelu"							,    SCREAMING_SNAKE_CASE__	:							int=True							,    SCREAMING_SNAKE_CASE__	:							str=0.02							,    SCREAMING_SNAKE_CASE__	:							List[Any]=1e-5							,    SCREAMING_SNAKE_CASE__	:							List[str]=True							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=None							,    SCREAMING_SNAKE_CASE__	:							List[str]=True							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=10							,    SCREAMING_SNAKE_CASE__	:							List[str]=["stage1", "stage2"]							,    SCREAMING_SNAKE_CASE__	:							str=[1, 2]							,    )  ->							str:
						__lowerCamelCase					      =			parent
						__lowerCamelCase					      =			batch_size
						__lowerCamelCase					      =			image_size
						__lowerCamelCase					      =			patch_size
						__lowerCamelCase					      =			num_channels
						__lowerCamelCase					      =			embed_dim
						__lowerCamelCase					      =			depths
						__lowerCamelCase					      =			num_heads
						__lowerCamelCase					      =			kernel_size
						__lowerCamelCase					      =			dilations
						__lowerCamelCase					      =			mlp_ratio
						__lowerCamelCase					      =			qkv_bias
						__lowerCamelCase					      =			hidden_dropout_prob
						__lowerCamelCase					      =			attention_probs_dropout_prob
						__lowerCamelCase					      =			drop_path_rate
						__lowerCamelCase					      =			hidden_act
						__lowerCamelCase					      =			patch_norm
						__lowerCamelCase					      =			layer_norm_eps
						__lowerCamelCase					      =			initializer_range
						__lowerCamelCase					      =			is_training
						__lowerCamelCase					      =			scope
						__lowerCamelCase					      =			use_labels
						__lowerCamelCase					      =			num_labels
						__lowerCamelCase					      =			out_features
						__lowerCamelCase					      =			out_indices






		def 				__A (	self	:							List[str]						)  ->							Dict:
						__lowerCamelCase					      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

						__lowerCamelCase					      =			self.get_config()

						return config, pixel_values, labels






		def 				__A (	self	:							Union[str, Any]						)  ->							List[str]:
						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 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							str							,    SCREAMING_SNAKE_CASE__	:							Any						)  ->							Optional[int]:
						__lowerCamelCase					      =			DinatModel(config=SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

						__lowerCamelCase					      =			__lowerCamelCase					      =			(config.image_size // config.patch_size) // (2 ** (len(config.depths						) - 1))
						__lowerCamelCase					      =			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 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Any							,    SCREAMING_SNAKE_CASE__	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							List[str]:
						__lowerCamelCase					      =			DinatForImageClassification(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__							,    labels=SCREAMING_SNAKE_CASE__						)
						self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)						)

						# test greyscale images
						__lowerCamelCase					      =			1
						__lowerCamelCase					      =			DinatForImageClassification(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()

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






		def 				__A (	self	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							Optional[int]:
						__lowerCamelCase					      =			DinatBackbone(config=SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

						# 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
						__lowerCamelCase					      =			None
						__lowerCamelCase					      =			DinatBackbone(config=SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()
						__lowerCamelCase					      =			model(SCREAMING_SNAKE_CASE__						)

						# 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 				__A (	self	:							Tuple						)  ->							int:
						__lowerCamelCase					      =			self.prepare_config_and_inputs()
						__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			config_and_inputs
						__lowerCamelCase					      =			{'''pixel_values''': pixel_values}
						return config, inputs_dict





@require_natten
@require_torch
class      lowerCAmelCase__  (			__lowerCamelCase						,			__lowerCamelCase						,			unittest.TestCase    ):
		a__ :		Tuple   						=	(
		    (
		        DinatModel,
		        DinatForImageClassification,
		        DinatBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		a__ :		List[Any]   						=	(
		    {'feature-extraction': DinatModel, 'image-classification': DinatForImageClassification}
		    if is_torch_available()
		    else {}
		)
		a__ :		Dict   						=	False

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






		def 				__A (	self	:							List[Any]						)  ->							Tuple:
						__lowerCamelCase					      =			DinatModelTester(self						)
						__lowerCamelCase					      =			ConfigTester(self							,    config_class=SCREAMING_SNAKE_CASE__							,    embed_dim=37						)






		def 				__A (	self	:							Tuple						)  ->							Union[str, Any]:
						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 				__A (	self	:							Tuple						)  ->							Optional[Any]:
						return






		def 				__A (	self	:							Optional[Any]						)  ->							int:
						__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							Any						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							str						)  ->							List[str]:
						__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE__						)






		@unittest.skip(reason='''Dinat does not use inputs_embeds'''						)
		def 				__A (	self	:							int						)  ->							Dict:
						pass






		@unittest.skip(reason='''Dinat does not use feedforward chunking'''						)
		def 				__A (	self	:							Dict						)  ->							List[Any]:
						pass






		def 				__A (	self	:							List[Any]						)  ->							int:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

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






		def 				__A (	self	:							int						)  ->							Any:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

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

										__lowerCamelCase					      =			['''pixel_values''']
										self.assertListEqual(arg_names[:1]							,    SCREAMING_SNAKE_CASE__						)






		def 				__A (	self	:							int						)  ->							int:
						self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''						)






		def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							int							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Dict						)  ->							str:
						__lowerCamelCase					      =			model_class(SCREAMING_SNAKE_CASE__						)
						model.to(SCREAMING_SNAKE_CASE__						)
						model.eval()

						with torch.no_grad():
										__lowerCamelCase					      =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)						)

						__lowerCamelCase					      =			outputs.hidden_states

						__lowerCamelCase					      =			getattr(
						    self.model_tester							,    '''expected_num_hidden_layers'''							,    len(self.model_tester.depths						) + 1						)
						self.assertEqual(len(SCREAMING_SNAKE_CASE__						)							,    SCREAMING_SNAKE_CASE__						)

						# Dinat has a different seq_length
						__lowerCamelCase					      =			(
						    config.patch_size
						    if isinstance(config.patch_size							,    collections.abc.Iterable						)
						    else (config.patch_size, config.patch_size)
						)

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

										__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			reshaped_hidden_states[0].shape
										__lowerCamelCase					      =			(
										    reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						).permute(0							,    2							,    3							,    1						)
										)
										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-3:]						)							,    [height, width, self.model_tester.embed_dim]							,    )






		def 				__A (	self	:							int						)  ->							Dict:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCamelCase					      =			(
						    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:
										__lowerCamelCase					      =			True
										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

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

										self.check_hidden_states_output(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)






		@slow
		def 				__A (	self	:							Dict						)  ->							Tuple:






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







		def 				__A (	self	:							List[Any]						)  ->							Optional[Any]:
						__lowerCamelCase      ,					__lowerCamelCase					      =			self.model_tester.prepare_config_and_inputs_for_common()

						__lowerCamelCase					      =			_config_zero_init(SCREAMING_SNAKE_CASE__						)
						for model_class in self.all_model_classes:
										__lowerCamelCase					      =			model_class(config=SCREAMING_SNAKE_CASE__						)
										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    ):






		@cached_property
		def 				__A (	self	:							Optional[Any]						)  ->							Optional[int]:
						return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						) if is_vision_available() else None







		@slow
		def 				__A (	self	:							Any						)  ->							Any:
						__lowerCamelCase					      =			DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''						).to(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			self.default_image_processor

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

						# forward pass
						with torch.no_grad():
										__lowerCamelCase					      =			model(**SCREAMING_SNAKE_CASE__						)

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





@require_torch
@require_natten
class      lowerCAmelCase__  (			unittest.TestCase						,			__lowerCamelCase    ):
		a__ :		List[Any]   						=	(DinatBackbone,) if is_torch_available() else ()
		a__ :		str   						=	DinatConfig






		def 				__A (	self	:							List[str]						)  ->							Tuple:
						__lowerCamelCase					      =			DinatModelTester(self						)





from ..utils import requires_backends


SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					None

class      lowerCAmelCase__  :






		def __init__(	self	:							int							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
						requires_backends(self							,    ['''detectron2''']						)







		@classmethod
		def 				__A (	cls	:							Dict							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Tuple:
						requires_backends(cls							,    ['''detectron2''']						)



