

'''simple docstring'''







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        A__   :



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=13		,					UpperCamelCase__=3		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=224		,					UpperCamelCase__=1000		,					UpperCamelCase__=[3, 3, 6, 4]		,					UpperCamelCase__=[48, 56, 112, 220]		,					)    ->   List[str]:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   num_channels
           A_						       =   is_training
           A_						       =   use_labels
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   num_labels
           A_						       =   image_size
           A_						       =   layer_depths
           A_						       =   embed_dims



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

           A_						       =   self.get_config()

           return config, pixel_values, labels



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





           '''simple docstring'''
           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=UpperCamelCase_		,					layer_scale_init_value=1e-5		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   SwiftFormerModel(config=UpperCamelCase_							)
           model.to(UpperCamelCase_							)
           model.eval()
           A_						       =   model(UpperCamelCase_							)
           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.embed_dims[-1], 7, 7)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.num_labels
           A_						       =   SwiftFormerForImageClassification(UpperCamelCase_							)
           model.to(UpperCamelCase_							)
           model.eval()
           A_						       =   model(UpperCamelCase_		,					labels=UpperCamelCase_							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)							)

           A_						       =   SwiftFormerForImageClassification(UpperCamelCase_							)
           model.to(UpperCamelCase_							)
           model.eval()

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







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           (A_)						       =   self.prepare_config_and_inputs()
           A_						       =   {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
class        A__   (   A__    ,    A__    ,    unittest.TestCase      ):
    lowercase						       =		(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase						       =		(
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False



    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   SwiftFormerModelTester(self							)
           A_						       =   ConfigTester(
               self		,					config_class=UpperCamelCase_		,					has_text_modality=UpperCamelCase_		,					hidden_size=37		,					num_attention_heads=12		,					num_hidden_layers=12		,					)



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





           '''simple docstring'''
           self.config_tester.run_common_tests()



    @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

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



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





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

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

                  A_						       =   ['''pixel_values''']
                  self.assertListEqual(arg_names[:1]		,					UpperCamelCase_							)



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_							)



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





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_							)



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





           '''simple docstring'''


           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   SwiftFormerModel.from_pretrained(UpperCamelCase_							)
                  self.assertIsNotNone(UpperCamelCase_							)



    @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           pass



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





           '''simple docstring'''


           def check_hidden_states_output(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							):
                  A_						       =   model_class(UpperCamelCase_							)
                  model.to(UpperCamelCase_							)
                  model.eval()

                  with torch.no_grad():
                         A_						       =   model(**self._prepare_for_class(UpperCamelCase_		,					UpperCamelCase_							)							)

                  A_						       =   outputs.hidden_states

                  A_						       =   8
                  self.assertEqual(len(UpperCamelCase_							)		,					UpperCamelCase_							)  # 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(UpperCamelCase_							)							):
                         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),
                                 ]							)		,					)

           A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A_						       =   True
                  check_hidden_states_output(UpperCamelCase_		,					UpperCamelCase_		,					UpperCamelCase_							)

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

                  check_hidden_states_output(UpperCamelCase_		,					UpperCamelCase_		,					UpperCamelCase_							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''


           def _config_zero_init(UpperCamelCase__							):
                  A_						       =   copy.deepcopy(UpperCamelCase_							)
                  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(UpperCamelCase_		,					UpperCamelCase_		,					1e-1_0							)
                         if isinstance(getattr(UpperCamelCase_		,					UpperCamelCase_		,					UpperCamelCase_							)		,					UpperCamelCase_							):
                                A_						       =   _config_zero_init(getattr(UpperCamelCase_		,					UpperCamelCase_							)							)
                                setattr(UpperCamelCase_		,					UpperCamelCase_		,					UpperCamelCase_							)
                  return configs_no_init

           A_						       =   self.model_tester.prepare_config_and_inputs_for_common()

           A_						       =   _config_zero_init(UpperCamelCase_							)
           for model_class in self.all_model_classes:
                  A_						       =   model_class(config=UpperCamelCase_							)
                  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        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           pass

def    UpperCAmelCase__     (   )      ->      Any:
       A_						       =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
       return image







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



    @cached_property
    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None







    @slow
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(UpperCamelCase_							)

           A_						       =   self.default_image_processor
           A_						       =   prepare_img()
           A_						       =   image_processor(images=UpperCamelCase_		,					return_tensors="""pt"""							).to(UpperCamelCase_							)

           # forward pass
           with torch.no_grad():
                  A_						       =   model(**UpperCamelCase_							)

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

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




'''simple docstring'''
from __future__ import annotations

from math import pi, sqrt


def      a (     __a					,   __a  )    ->      tuple:



					'''simple docstring'''






					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''  )

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''  )

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance  )))  ),
										)


if __name__ == "__main__":
		import doctest

		doctest.testmod()