





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 							_UpperCAmelCase       :

  """simple docstring"""


  def __init__(							self					:						Any   ,    lowerCAmelCase_					:						Union[str, Any]   ,    lowerCAmelCase_					:						Any=1_3   ,    lowerCAmelCase_					:						Tuple=3   ,    lowerCAmelCase_					:						int=True   ,    lowerCAmelCase_					:						Tuple=True   ,    lowerCAmelCase_					:						Optional[Any]=0.1   ,    lowerCAmelCase_					:						Any=0.1   ,    lowerCAmelCase_					:						Union[str, Any]=2_2_4   ,    lowerCAmelCase_					:						List[str]=1_0_0_0   ,    lowerCAmelCase_					:						Union[str, Any]=[3, 3, 6, 4]   ,    lowerCAmelCase_					:						List[str]=[4_8, 5_6, 1_1_2, 2_2_0]   ,    )				->		Any:
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      is_training
     __lowerCAmelCase				      =      use_labels
     __lowerCAmelCase				      =      hidden_dropout_prob
     __lowerCAmelCase				      =      attention_probs_dropout_prob
     __lowerCAmelCase				      =      num_labels
     __lowerCAmelCase				      =      image_size
     __lowerCAmelCase				      =      layer_depths
     __lowerCAmelCase				      =      embed_dims






  def 							lowercase						(							self					:						Tuple	)				->		Optional[int]:
     __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 							lowercase						(							self					:						Optional[Any]	)				->		Optional[int]:
     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 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						Tuple	)				->		int:
     __lowerCAmelCase				      =      SwiftFormerModel(config=lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_	)
     self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.embed_dims[-1], 7, 7)	)






  def 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						Tuple   ,    lowerCAmelCase_					:						List[str]   ,    lowerCAmelCase_					:						Optional[int]	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.num_labels
     __lowerCAmelCase				      =      SwiftFormerForImageClassification(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()
     __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    labels=lowerCAmelCase_	)
     self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)	)

     __lowerCAmelCase				      =      SwiftFormerForImageClassification(lowerCAmelCase_	)
     model.to(lowerCAmelCase_	)
     model.eval()

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






  def 							lowercase						(							self					:						str	)				->		Any:
     ((__lowerCAmelCase)      ,      (__lowerCAmelCase)      ,      (__lowerCAmelCase))				      =      self.prepare_config_and_inputs()
     __lowerCAmelCase				      =      {'pixel_values': pixel_values}
     return config, inputs_dict





@require_torch
class 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


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

  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False
  a_              =					False






  def 							lowercase						(							self					:						Optional[int]	)				->		List[Any]:
     __lowerCAmelCase				      =      SwiftFormerModelTester(self	)
     __lowerCAmelCase				      =      ConfigTester(
         self   ,    config_class=lowerCAmelCase_   ,    has_text_modality=lowerCAmelCase_   ,    hidden_size=3_7   ,    num_attention_heads=1_2   ,    num_hidden_layers=1_2   ,    )






  def 							lowercase						(							self					:						Optional[Any]	)				->		Optional[int]:
     self.config_tester.run_common_tests()






  @unittest.skip(reason='SwiftFormer does not use inputs_embeds'	)
  def 							lowercase						(							self					:						List[str]	)				->		Tuple:
     pass






  def 							lowercase						(							self					:						int	)				->		Dict:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

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






  def 							lowercase						(							self					:						str	)				->		Optional[Any]:
     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
        __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]   ,    lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[Any]	)				->		Dict:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Tuple:
     __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_	)






  @slow
  def 							lowercase						(							self					:						Union[str, Any]	)				->		int:
     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCAmelCase				      =      SwiftFormerModel.from_pretrained(lowerCAmelCase_	)
        self.assertIsNotNone(lowerCAmelCase_	)






  @unittest.skip(reason='SwiftFormer does not output attentions'	)
  def 							lowercase						(							self					:						List[Any]	)				->		Union[str, Any]:
     pass






  def 							lowercase						(							self					:						Any	)				->		int:
     def check_hidden_states_output(lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						List[Any]	):
        __lowerCAmelCase				      =      model_class(lowerCAmelCase_	)
        model.to(lowerCAmelCase_	)
        model.eval()

        with torch.no_grad():
           __lowerCAmelCase				      =      model(**self._prepare_for_class(lowerCAmelCase_   ,    lowerCAmelCase_	)	)

        __lowerCAmelCase				      =      outputs.hidden_states

        __lowerCAmelCase				      =      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),
                   ]	)   ,    )

     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      True
        check_hidden_states_output(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)

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

        check_hidden_states_output(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[str]	)				->		List[str]:
     def _config_zero_init(lowerCAmelCase_					:						List[str]	):
        __lowerCAmelCase				      =      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-10	)
           if isinstance(getattr(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)   ,    lowerCAmelCase_	):
              __lowerCAmelCase				      =      _config_zero_init(getattr(lowerCAmelCase_   ,    lowerCAmelCase_	)	)
              setattr(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)
        return configs_no_init

     __lowerCAmelCase      ,      __lowerCAmelCase				      =      self.model_tester.prepare_config_and_inputs_for_common()

     __lowerCAmelCase				      =      _config_zero_init(lowerCAmelCase_	)
     for model_class in self.all_model_classes:
        __lowerCAmelCase				      =      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 							lowercase						(							self					:						Any	)				->		List[str]:
     pass





def     a_       (					):
   __lowerCAmelCase				      =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image




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

  """simple docstring"""


  @cached_property
  def 							lowercase						(							self					:						Dict	)				->		Tuple:
     return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'	) if is_vision_available() else None






  @slow
  def 							lowercase						(							self					:						str	)				->		List[str]:
     __lowerCAmelCase				      =      SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'	).to(lowerCAmelCase_	)

     __lowerCAmelCase				      =      self.default_image_processor
     __lowerCAmelCase				      =      prepare_img()
     __lowerCAmelCase				      =      image_processor(images=lowerCAmelCase_   ,    return_tensors='pt'	).to(lowerCAmelCase_	)

     # forward pass
     with torch.no_grad():
        __lowerCAmelCase				      =      model(**lowerCAmelCase_	)

     # verify the logits
     __lowerCAmelCase				      =      torch.Size((1, 1_0_0_0)	)
     self.assertEqual(outputs.logits.shape   ,    lowerCAmelCase_	)

     __lowerCAmelCase				      =      torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]	).to(lowerCAmelCase_	)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,    lowerCAmelCase_   ,    atol=1e-4	)	)






'''simple docstring'''





class __A     :



 '''simple docstring'''







 def __init__(self					,  A			)       ->	None:


      """simple docstring"""



      _a           =    len(A			)
      _a           =    [0] * len_array

      if len_array > 0:
           _a           =    array[0]

      for i in range(1					,  A			):
           _a           =    self.prefix_sum[i - 1] + array[i]



 def    a__    (self					,  A					,  A			)       ->	int:


      """simple docstring"""



      if start == 0:
           return self.prefix_sum[end]

      return self.prefix_sum[end] - self.prefix_sum[start - 1]



 def    a__    (self					,  A			)       ->	bool:


      """simple docstring"""



      _a           =    {0}
      for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
                return True

           sums.add(A			)

      return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()



