



def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							int:
  UpperCAmelCase_						=				len(_snake_case )
  UpperCAmelCase_						=				[[0] * n for i in range(_snake_case )]
  for i in range(_snake_case ):
    UpperCAmelCase_						=				y_points[i]

  for i in range(2			,     _snake_case ):
    for j in range(_snake_case			,     _snake_case ):
      UpperCAmelCase_						=				(
          (xa - x_points[j - i + 1]) * q[j][i - 1]
          - (xa - x_points[j]) * q[j - 1][i - 1]
      ) / (x_points[j] - x_points[j - i + 1])

  return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=13    ,	lowerCAmelCase=3    ,	lowerCAmelCase=True    ,	lowerCAmelCase=True    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=224    ,	lowerCAmelCase=1000    ,	lowerCAmelCase=[3, 3, 6, 4]    ,	lowerCAmelCase=[48, 56, 112, 220]    ,	):
							UpperCAmelCase_						=				parent
							UpperCAmelCase_						=				batch_size
							UpperCAmelCase_						=				num_channels
							UpperCAmelCase_						=				is_training
							UpperCAmelCase_						=				use_labels
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				num_labels
							UpperCAmelCase_						=				image_size
							UpperCAmelCase_						=				layer_depths
							UpperCAmelCase_						=				embed_dims



					def    A__		(			self     ):
							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    A__		(			self     ):
							return SwiftFormerConfig(
							    depths=self.layer_depths    ,	embed_dims=self.embed_dims    ,	mlp_ratio=4    ,	downsamples=[True, True, True, True]    ,	hidden_act="gelu"    ,	num_labels=self.num_labels    ,	down_patch_size=3    ,	down_stride=2    ,	down_pad=1    ,	drop_rate=0.0    ,	drop_path_rate=0.0    ,	use_layer_scale=lowerCAmelCase    ,	layer_scale_init_value=1e-5    ,	)



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				SwiftFormerModel(config=lowerCAmelCase     )
							model.to(lowerCAmelCase     )
							model.eval()
							UpperCAmelCase_						=				model(lowerCAmelCase     )
							self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.num_labels
							UpperCAmelCase_						=				SwiftFormerForImageClassification(lowerCAmelCase     )
							model.to(lowerCAmelCase     )
							model.eval()
							UpperCAmelCase_						=				model(lowerCAmelCase    ,	labels=lowerCAmelCase     )
							self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)     )

							UpperCAmelCase_						=				SwiftFormerForImageClassification(lowerCAmelCase     )
							model.to(lowerCAmelCase     )
							model.eval()

							UpperCAmelCase_						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
							UpperCAmelCase_						=				model(lowerCAmelCase     )
							self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)     )



					def    A__		(			self     ):
							((UpperCAmelCase_)					,						(UpperCAmelCase_)					,						(UpperCAmelCase_))						=				self.prepare_config_and_inputs()
							UpperCAmelCase_						=				{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class 	lowerCamelCase      (						lowercase__,     lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[Any]									=  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					lowerCAmelCase_				:    int									=  (
					    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase_				:    List[Any]									=  False
					lowerCAmelCase_				:    Dict									=  False
					lowerCAmelCase_				:    int									=  False
					lowerCAmelCase_				:    str									=  False
					lowerCAmelCase_				:    Optional[Any]									=  False



					def    A__		(			self     ):
							UpperCAmelCase_						=				SwiftFormerModelTester(self     )
							UpperCAmelCase_						=				ConfigTester(
							    self    ,	config_class=lowerCAmelCase    ,	has_text_modality=lowerCAmelCase    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)



					def    A__		(			self     ):
							self.config_tester.run_common_tests()



					@unittest.skip(reason="SwiftFormer does not use inputs_embeds"     )
					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									UpperCAmelCase_						=				model_class(lowerCAmelCase     )
									UpperCAmelCase_						=				model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(lowerCAmelCase    ,	nn.Linear     )     )



					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									UpperCAmelCase_						=				model_class(lowerCAmelCase     )
									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]    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase     )



					@slow
					def    A__		(			self     ):
							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_						=				SwiftFormerModel.from_pretrained(lowerCAmelCase     )
									self.assertIsNotNone(lowerCAmelCase     )



					@unittest.skip(reason="SwiftFormer does not output attentions"     )
					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							def check_hidden_states_output(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
									UpperCAmelCase_						=				model_class(lowerCAmelCase     )
									model.to(lowerCAmelCase     )
									model.eval()

									with torch.no_grad():
											UpperCAmelCase_						=				model(**self._prepare_for_class(lowerCAmelCase    ,	lowerCAmelCase     )     )

									UpperCAmelCase_						=				outputs.hidden_states

									UpperCAmelCase_						=				8
									self.assertEqual(len(lowerCAmelCase     )    ,	lowerCAmelCase     )  # TODO

									# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
									# with the width and height being successively divided by 2, after every 2 blocks
									for i in range(len(lowerCAmelCase     )     ):
											self.assertEqual(
											    hidden_states[i].shape    ,	torch.Size(
											        [
											            self.model_tester.batch_size,
											            self.model_tester.embed_dims[i // 2],
											            (self.model_tester.image_size // 4) // 2 ** (i // 2),
											            (self.model_tester.image_size // 4) // 2 ** (i // 2),
											        ]     )    ,	)

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									UpperCAmelCase_						=				True
									check_hidden_states_output(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )

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

									check_hidden_states_output(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							def _config_zero_init(lowerCAmelCase     ):
									UpperCAmelCase_						=				copy.deepcopy(lowerCAmelCase     )
									for key in configs_no_init.__dict__.keys():
											if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowerCAmelCase    ,	lowerCAmelCase    ,	1e-1_0     )
											if isinstance(getattr(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )    ,	lowerCAmelCase     ):
													UpperCAmelCase_						=				_config_zero_init(getattr(lowerCAmelCase    ,	lowerCAmelCase     )     )
													setattr(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )
									return configs_no_init

							UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_						=				_config_zero_init(lowerCAmelCase     )
							for model_class in self.all_model_classes:
									UpperCAmelCase_						=				model_class(config=lowerCAmelCase     )
									for name, param in model.named_parameters():
											if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1e9) / 1e9).round().item()    ,	[0.0, 1.0]    ,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,	)



					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
					def    A__		(			self     ):
							pass







def snake_case__  (			)		->							str:
		UpperCAmelCase_						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
		return image



@require_torch
@require_vision
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@cached_property
					def    A__		(			self     ):
							return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"     ) if is_vision_available() else None



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"     ).to(lowerCAmelCase     )

							UpperCAmelCase_						=				self.default_image_processor
							UpperCAmelCase_						=				prepare_img()
							UpperCAmelCase_						=				image_processor(images=lowerCAmelCase    ,	return_tensors="pt"     ).to(lowerCAmelCase     )

							# forward pass
							with torch.no_grad():
									UpperCAmelCase_						=				model(**lowerCAmelCase     )

							# verify the logits
							UpperCAmelCase_						=				torch.Size((1, 1000)     )
							self.assertEqual(outputs.logits.shape    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]     ).to(lowerCAmelCase     )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	lowerCAmelCase    ,	atol=1e-4     )     )

