
'''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 		snake_case_       :



   """simple docstring"""





   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=0.0_2			,   UpperCamelCase=1E-5			,   UpperCamelCase=True			,   UpperCamelCase=None			,   UpperCamelCase=True			,   UpperCamelCase=10			,   UpperCamelCase=["stage1", "stage2"]			,   UpperCamelCase=[1, 2]			,   ):
          lowerCamelCase__				   =      parent
          lowerCamelCase__				   =      batch_size
          lowerCamelCase__				   =      image_size
          lowerCamelCase__				   =      patch_size
          lowerCamelCase__				   =      num_channels
          lowerCamelCase__				   =      embed_dim
          lowerCamelCase__				   =      depths
          lowerCamelCase__				   =      num_heads
          lowerCamelCase__				   =      kernel_size
          lowerCamelCase__				   =      dilations
          lowerCamelCase__				   =      mlp_ratio
          lowerCamelCase__				   =      qkv_bias
          lowerCamelCase__				   =      hidden_dropout_prob
          lowerCamelCase__				   =      attention_probs_dropout_prob
          lowerCamelCase__				   =      drop_path_rate
          lowerCamelCase__				   =      hidden_act
          lowerCamelCase__				   =      patch_norm
          lowerCamelCase__				   =      layer_norm_eps
          lowerCamelCase__				   =      initializer_range
          lowerCamelCase__				   =      is_training
          lowerCamelCase__				   =      scope
          lowerCamelCase__				   =      use_labels
          lowerCamelCase__				   =      num_labels
          lowerCamelCase__				   =      out_features
          lowerCamelCase__				   =      out_indices







   def     __UpperCAmelCase      ( self):
          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     __UpperCAmelCase      ( 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     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      DinatModel(config=UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()
          lowerCamelCase__				   =      model(UpperCamelCase)

          lowerCamelCase__				   =      lowerCamelCase__				   =      (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
          lowerCamelCase__				   =      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     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      DinatForImageClassification(UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()
          lowerCamelCase__				   =      model(UpperCamelCase			,   labels=UpperCamelCase)
          self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.num_labels))

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

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







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      DinatBackbone(config=UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()
          lowerCamelCase__				   =      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
          lowerCamelCase__				   =      None
          lowerCamelCase__				   =      DinatBackbone(config=UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()
          lowerCamelCase__				   =      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     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.prepare_config_and_inputs()
          lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      config_and_inputs
          lowerCamelCase__				   =      {"pixel_values": pixel_values}
          return config, inputs_dict







@require_natten
@require_torch
class 		snake_case_       (						A__		,	A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[int]								=(
       (
           DinatModel,
           DinatForImageClassification,
           DinatBackbone,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase	:							str								=(
       {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase	:							str								=False

   __lowerCAmelCase	:							Tuple								=False
   __lowerCAmelCase	:							Tuple								=False
   __lowerCAmelCase	:							str								=False
   __lowerCAmelCase	:							List[str]								=False







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      DinatModelTester(self)
          lowerCamelCase__				   =      ConfigTester(self			,   config_class=UpperCamelCase			,   embed_dim=37)







   def     __UpperCAmelCase      ( 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     __UpperCAmelCase      ( self):
          return







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      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     __UpperCAmelCase      ( self):
          pass







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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowerCamelCase__				   =      model_class(UpperCamelCase)
                 self.assertIsInstance(model.get_input_embeddings()			,   (nn.Module))
                 lowerCamelCase__				   =      model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(UpperCamelCase			,   nn.Linear))







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowerCamelCase__				   =      model_class(UpperCamelCase)
                 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]			,   UpperCamelCase)







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







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      model_class(UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

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

          lowerCamelCase__				   =      outputs.hidden_states

          lowerCamelCase__				   =      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
          lowerCamelCase__				   =      (
              config.patch_size
              if isinstance(config.patch_size			,   collections.abc.Iterable)
              else (config.patch_size, config.patch_size)
          )

          lowerCamelCase__				   =      image_size[0] // patch_size[0]
          lowerCamelCase__				   =      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":
                 lowerCamelCase__				   =      outputs.reshaped_hidden_states
                 self.assertEqual(len(UpperCamelCase)			,   UpperCamelCase)

                 lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      reshaped_hidden_states[0].shape
                 lowerCamelCase__				   =      (
                     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     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs_for_common()

          lowerCamelCase__				   =      (
              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:
                 lowerCamelCase__				   =      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"]
                 lowerCamelCase__				   =      True

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







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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs_for_common()

          lowerCamelCase__				   =      _config_zero_init(UpperCamelCase)
          for model_class in self.all_model_classes:
                 lowerCamelCase__				   =      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 		snake_case_       (						unittest.TestCase						):



   """simple docstring"""





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







   @slow
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(UpperCamelCase)
          lowerCamelCase__				   =      self.default_image_processor

          lowerCamelCase__				   =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
          lowerCamelCase__				   =      image_processor(images=UpperCamelCase			,   return_tensors="pt").to(UpperCamelCase)

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

          # verify the logits
          lowerCamelCase__				   =      torch.Size((1, 10_00))
          self.assertEqual(outputs.logits.shape			,   UpperCamelCase)
          lowerCamelCase__				   =      torch.tensor([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]).to(UpperCamelCase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]			,   UpperCamelCase			,   atol=1E-4))







@require_torch
@require_natten
class 		snake_case_       (						unittest.TestCase		,	A__						):



   """simple docstring"""





   __lowerCAmelCase	:							List[str]								=(DinatBackbone,) if is_torch_available() else ()
   __lowerCAmelCase	:							int								=DinatConfig







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      DinatModelTester(self)



'''simple docstring'''







def   lowerCAmelCase(  a__    :  float      , a__    :  float      , a__    :  int      ):







       '''simple docstring'''






       lowerCamelCase__				   =      [1 / first_term]
       lowerCamelCase__				   =      1 / first_term
       for _ in range(number_of_terms - 1      ):
              first_term += common_difference
              arithmetic_progression.append(a__      )
       lowerCamelCase__				   =      [1 / step for step in arithmetic_progression]
       return sum(a__      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(sum_of_harmonic_progression(1 / 2, 2, 2))

