

'''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__       :







  """simple docstring"""




  def __init__(			self    : Any				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : int=1_3				,				UpperCAmelCase__    : Dict=3				,				UpperCAmelCase__    : Any=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Union[str, Any]=0.1				,				UpperCAmelCase__    : int=0.1				,				UpperCAmelCase__    : int=2_2_4				,				UpperCAmelCase__    : str=1_0_0_0				,				UpperCAmelCase__    : Any=[3, 3, 6, 4]				,				UpperCAmelCase__    : Tuple=[4_8, 5_6, 1_1_2, 2_2_0]				,				)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   parent
        SCREAMING_SNAKE_CASE :      Tuple       =   batch_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_channels
        SCREAMING_SNAKE_CASE :      List[str]       =   is_training
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   use_labels
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      Any       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   num_labels
        SCREAMING_SNAKE_CASE :      Tuple       =   image_size
        SCREAMING_SNAKE_CASE :      str       =   layer_depths
        SCREAMING_SNAKE_CASE :      str       =   embed_dims


  def  _lowercase      (			self    : Tuple		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        SCREAMING_SNAKE_CASE :      str       =   None
        if self.use_labels:
              SCREAMING_SNAKE_CASE :      Tuple       =   ids_tensor([self.batch_size]				,				self.num_labels		)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.get_config()

        return config, pixel_values, labels


  def  _lowercase      (			self    : List[Any]		)					->Any:
        """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  _lowercase      (			self    : int				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Union[str, Any]		)					->Dict:
        """simple docstring"""







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


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.num_labels
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   SwiftFormerForImageClassification(UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()
        SCREAMING_SNAKE_CASE :      str       =   model(UpperCAmelCase_				,				labels=UpperCAmelCase_		)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   SwiftFormerForImageClassification(UpperCAmelCase_		)
        model.to(UpperCAmelCase_		)
        model.eval()

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




  def  _lowercase      (			self    : List[str]		)					->Optional[int]:
        """simple docstring"""







        (SCREAMING_SNAKE_CASE) :      Tuple       =   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE :      str       =   {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        a__       (   __lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : str			       =(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  UpperCAmelCase__      : Tuple			       =(
      {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

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


  def  _lowercase      (			self    : Any		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   SwiftFormerModelTester(self		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   ConfigTester(
            self				,				config_class=UpperCAmelCase_				,				has_text_modality=UpperCAmelCase_				,				hidden_size=3_7				,				num_attention_heads=1_2				,				num_hidden_layers=1_2				,				)


  def  _lowercase      (			self    : Optional[int]		)					->Union[str, Any]:
        """simple docstring"""







        self.config_tester.run_common_tests()


  @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
  def  _lowercase      (			self    : int		)					->Dict:
        """simple docstring"""







        pass


  def  _lowercase      (			self    : Optional[Any]		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE :      Dict       =   model_class(UpperCAmelCase_		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(UpperCAmelCase_				,				nn.Linear		)		)


  def  _lowercase      (			self    : Tuple		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   model_class(UpperCAmelCase_		)
              SCREAMING_SNAKE_CASE :      List[str]       =   inspect.signature(model.forward		)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE :      Optional[int]       =   [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE :      int       =   ['pixel_values']
              self.assertListEqual(arg_names[:1]				,				UpperCAmelCase_		)


  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase_		)


  def  _lowercase      (			self    : Tuple		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_		)


  @slow
  def  _lowercase      (			self    : List[Any]		)					->str:
        """simple docstring"""







        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE :      Dict       =   SwiftFormerModel.from_pretrained(UpperCAmelCase_		)
              self.assertIsNotNone(UpperCAmelCase_		)


  @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
  def  _lowercase      (			self    : Optional[Any]		)					->Dict:
        """simple docstring"""







        pass


  def  _lowercase      (			self    : Union[str, Any]		)					->int:
        """simple docstring"""







        def check_hidden_states_output(UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Dict		):
              SCREAMING_SNAKE_CASE :      Optional[int]       =   model_class(UpperCAmelCase_		)
              model.to(UpperCAmelCase_		)
              model.eval()

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

              SCREAMING_SNAKE_CASE :      List[Any]       =   outputs.hidden_states

              SCREAMING_SNAKE_CASE :      Any       =   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),
                            ]		)				,				)

        SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   True

              check_hidden_states_output(UpperCAmelCase_				,				UpperCAmelCase_				,				UpperCAmelCase_		)


  def  _lowercase      (			self    : Union[str, Any]		)					->Tuple:
        """simple docstring"""







        def _config_zero_init(UpperCAmelCase__    : Any		):
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   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-10		)
                    if isinstance(getattr(UpperCAmelCase_				,				UpperCAmelCase_				,				UpperCAmelCase_		)				,				UpperCAmelCase_		):
                          SCREAMING_SNAKE_CASE :      Optional[int]       =   _config_zero_init(getattr(UpperCAmelCase_				,				UpperCAmelCase_		)		)
                          setattr(UpperCAmelCase_				,				UpperCAmelCase_				,				UpperCAmelCase_		)
              return configs_no_init

        SCREAMING_SNAKE_CASE :      List[str]       =   self.model_tester.prepare_config_and_inputs_for_common()

        SCREAMING_SNAKE_CASE :      Dict       =   _config_zero_init(UpperCAmelCase_		)
        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE :      List[str]       =   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  _lowercase      (			self    : List[str]		)					->List[Any]:
        """simple docstring"""







        pass






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







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







  """simple docstring"""




  @cached_property
  def  _lowercase      (			self    : Optional[Any]		)					->Optional[Any]:
        """simple docstring"""







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




  @slow
  def  _lowercase      (			self    : List[str]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(UpperCAmelCase_		)

        SCREAMING_SNAKE_CASE :      Any       =   self.default_image_processor
        SCREAMING_SNAKE_CASE :      Tuple       =   prepare_img()
        SCREAMING_SNAKE_CASE :      Dict       =   image_processor(images=UpperCAmelCase_				,				return_tensors="""pt"""		).to(UpperCAmelCase_		)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   model(**UpperCAmelCase_		)

        # verify the logits
        SCREAMING_SNAKE_CASE :      Dict       =   torch.Size((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape				,				UpperCAmelCase_		)

        SCREAMING_SNAKE_CASE :      Tuple       =   torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]		).to(UpperCAmelCase_		)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]				,				UpperCAmelCase_				,				atol=1e-4		)		)







from __future__ import annotations






def 					__lowercase      (     _A						,    _A			)     ->						list[str]:
      if nth_term == "":
            return [""]
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      list[str]       =   []
      for temp in range(int(_A			)			):
            series.append(F"1 / {pow(temp + 1						,    int(_A			)			)}" if series else """1"""			)
      return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase__ :     Dict				    =    int(input("""Enter the last number (nth term) of the P-Series"""))
  UpperCAmelCase__ :     Optional[int]				    =    int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))




