
'''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			:




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase=13       ,    UpperCamelCase=64       ,    UpperCamelCase=4       ,    UpperCamelCase=3       ,    UpperCamelCase=16       ,    UpperCamelCase=[1, 2, 1]       ,    UpperCamelCase=[2, 4, 8]       ,    UpperCamelCase=3       ,    UpperCamelCase=[[3], [1, 2], [1]]       ,    UpperCamelCase=2.0       ,    UpperCamelCase=True       ,    UpperCamelCase=0.0       ,    UpperCamelCase=0.0       ,    UpperCamelCase=0.1       ,    UpperCamelCase="gelu"       ,    UpperCamelCase=True       ,    UpperCamelCase=0.02       ,    UpperCamelCase=1e-5       ,    UpperCamelCase=True       ,    UpperCamelCase=None       ,    UpperCamelCase=True       ,    UpperCamelCase=10       ,    UpperCamelCase=["stage1", "stage2"]       ,    UpperCamelCase=[1, 2]       ,    ):
								_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      lowercase							(					self   ):
								_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      lowercase							(					self   ):
								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      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			DinatModel(config=UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase   )

								_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      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			DinatForImageClassification(UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase       ,    labels=UpperCamelCase   )
								self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)   )

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

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




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			DinatBackbone(config=UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			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
								_SCREAMING_SNAKE_CASE					=			None
								_SCREAMING_SNAKE_CASE					=			DinatBackbone(config=UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.eval()
								_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								_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       lowerCAmelCase			(					__UpperCAmelCase		,  __UpperCAmelCase		,  unittest.TestCase      ):
		a				:       List[Any]							=       (
		    (
		        DinatModel,
		        DinatForImageClassification,
		        DinatBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		a				:       Tuple							=       (
		    {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
		    if is_torch_available()
		    else {}
		)
		a				:       str							=       False

		a				:       int							=       False
		a				:       Optional[Any]							=       False
		a				:       str							=       False
		a				:       List[str]							=       False




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			DinatModelTester(self   )
								_SCREAMING_SNAKE_CASE					=			ConfigTester(self       ,    config_class=UpperCamelCase       ,    embed_dim=37   )




		def      lowercase							(					self   ):
								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   ):
								return




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			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      lowercase							(					self   ):
								pass




		@unittest.skip(reason="Dinat does not use feedforward chunking"   )
		def      lowercase							(					self   ):
								pass




		def      lowercase							(					self   ):
								_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(UpperCamelCase   )
														self.assertIsInstance(model.get_input_embeddings()       ,    (nn.Module)   )
														_SCREAMING_SNAKE_CASE					=			model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(UpperCamelCase       ,    nn.Linear   )   )




		def      lowercase							(					self   ):
								_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(UpperCamelCase   )
														_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]       ,    UpperCamelCase   )




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




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			model_class(UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.eval()

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

								_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(UpperCamelCase   )       ,    UpperCamelCase   )

								# 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(UpperCamelCase   )       ,    UpperCamelCase   )

														_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(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      lowercase							(					self   ):
								_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(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

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

														self.check_hidden_states_output(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )




		@slow
		def      lowercase							(					self   ):
								for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE					=			DinatModel.from_pretrained(UpperCamelCase   )
														self.assertIsNotNone(UpperCamelCase   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.model_tester.prepare_config_and_inputs_for_common()

								_SCREAMING_SNAKE_CASE					=			_config_zero_init(UpperCamelCase   )
								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE					=			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      ):




		@cached_property
		def      lowercase							(					self   ):
								return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224"   ) if is_vision_available() else None




		@slow
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224"   ).to(UpperCamelCase   )
								_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=UpperCamelCase       ,    return_tensors="pt"   ).to(UpperCamelCase   )

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

								# verify the logits
								_SCREAMING_SNAKE_CASE					=			torch.Size((1, 1_000)   )
								self.assertEqual(outputs.logits.shape       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			torch.tensor([-0.15_45, -0.76_67, 0.46_42]   ).to(UpperCamelCase   )
								self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,    UpperCamelCase       ,    atol=1e-4   )   )


@require_torch
@require_natten
class       lowerCAmelCase			(					unittest.TestCase		,  __UpperCAmelCase      ):
		a				:       Any							=       (DinatBackbone,) if is_torch_available() else ()
		a				:       int							=       DinatConfig




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			DinatModelTester(self   )

'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: int	):
						return (input_a, input_a).count(0	) % 2



def       _a		(						):
						assert xor_gate(0						,      0	) == 0
						assert xor_gate(0						,      1	) == 1
						assert xor_gate(1						,      0	) == 1
						assert xor_gate(1						,      1	) == 0


if __name__ == "__main__":
						print(xor_gate(0, 0))
						print(xor_gate(0, 1))