

'''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 							A__					:





  def __init__(	self   :			Optional[int]							,      _a   :			Any							,      _a   :			Dict=13							,      _a   :			int=64							,      _a   :			str=4							,      _a   :			Union[str, Any]=3							,      _a   :			Dict=16							,      _a   :			str=[1, 2, 1]							,      _a   :			int=[2, 4, 8]							,      _a   :			Any=3							,      _a   :			Any=[[3], [1, 2], [1]]							,      _a   :			List[str]=2.0							,      _a   :			Any=True							,      _a   :			Optional[Any]=0.0							,      _a   :			Optional[int]=0.0							,      _a   :			Dict=0.1							,      _a   :			List[Any]="gelu"							,      _a   :			Union[str, Any]=True							,      _a   :			str=0.02							,      _a   :			List[str]=1e-5							,      _a   :			List[Any]=True							,      _a   :			List[str]=None							,      _a   :			Optional[Any]=True							,      _a   :			Any=10							,      _a   :			int=["stage1", "stage2"]							,      _a   :			Any=[1, 2]							,      )			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=parent
       _SCREAMING_SNAKE_CASE												=batch_size
       _SCREAMING_SNAKE_CASE												=image_size
       _SCREAMING_SNAKE_CASE												=patch_size
       _SCREAMING_SNAKE_CASE												=num_channels
       _SCREAMING_SNAKE_CASE												=embed_dim
       _SCREAMING_SNAKE_CASE												=depths
       _SCREAMING_SNAKE_CASE												=num_heads
       _SCREAMING_SNAKE_CASE												=kernel_size
       _SCREAMING_SNAKE_CASE												=dilations
       _SCREAMING_SNAKE_CASE												=mlp_ratio
       _SCREAMING_SNAKE_CASE												=qkv_bias
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=drop_path_rate
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=patch_norm
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=is_training
       _SCREAMING_SNAKE_CASE												=scope
       _SCREAMING_SNAKE_CASE												=use_labels
       _SCREAMING_SNAKE_CASE												=num_labels
       _SCREAMING_SNAKE_CASE												=out_features
       _SCREAMING_SNAKE_CASE												=out_indices





  def 		A						(	self   :			List[Any]				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

       _SCREAMING_SNAKE_CASE												=self.get_config()

       return config, pixel_values, labels





  def 		A						(	self   :			str				)			->					List[Any]:





       '''simple docstring'''






       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 		A						(	self   :			Optional[int]							,      _a   :			Optional[int]							,      _a   :			Any							,      _a   :			List[Any]				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=DinatModel(config=a_				)
       model.to(a_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(a_				)

       _SCREAMING_SNAKE_CASE												=_SCREAMING_SNAKE_CASE												=(config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
       _SCREAMING_SNAKE_CASE												=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 		A						(	self   :			List[Any]							,      _a   :			Optional[Any]							,      _a   :			Union[str, Any]							,      _a   :			List[str]				)			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=DinatForImageClassification(a_				)
       model.to(a_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(a_							,      labels=a_				)
       self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)				)

       # test greyscale images
       _SCREAMING_SNAKE_CASE												=1
       _SCREAMING_SNAKE_CASE												=DinatForImageClassification(a_				)
       model.to(a_				)
       model.eval()

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





  def 		A						(	self   :			int							,      _a   :			int							,      _a   :			Any							,      _a   :			Optional[int]				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=DinatBackbone(config=a_				)
       model.to(a_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(a_				)

       # 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
       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=DinatBackbone(config=a_				)
       model.to(a_				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(a_				)

       # 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 		A						(	self   :			Tuple				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.prepare_config_and_inputs()
       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=config_and_inputs
       _SCREAMING_SNAKE_CASE												={'pixel_values': pixel_values}
       return config, inputs_dict







@require_natten
@require_torch
class 							A__					( A__  ,     A__  ,     unittest.TestCase						):
  A__					=       (
      (
          DinatModel,
          DinatForImageClassification,
          DinatBackbone,
      )
      if is_torch_available()
      else ()
  )
  A__					=       (
      {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
      if is_torch_available()
      else {}
  )
  A__					=       False

  A__					=       False
  A__					=       False
  A__					=       False
  A__					=       False





  def 		A						(	self   :			Optional[Any]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=DinatModelTester(self				)
       _SCREAMING_SNAKE_CASE												=ConfigTester(self							,      config_class=a_							,      embed_dim=37				)





  def 		A						(	self   :			Optional[Any]				)			->					int:





       '''simple docstring'''






       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 		A						(	self   :			Optional[int]				)			->					Dict:





       '''simple docstring'''






       return





  def 		A						(	self   :			Optional[int]				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a_				)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a_				)





  def 		A						(	self   :			int				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*a_				)





  @unittest.skip(reason='Dinat does not use inputs_embeds'				)
  def 		A						(	self   :			List[str]				)			->					str:





       '''simple docstring'''






       pass





  @unittest.skip(reason='Dinat does not use feedforward chunking'				)
  def 		A						(	self   :			Any				)			->					List[Any]:





       '''simple docstring'''






       pass





  def 		A						(	self   :			Union[str, Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

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





  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

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

            _SCREAMING_SNAKE_CASE												=['pixel_values']
            self.assertListEqual(arg_names[:1]							,      a_				)





  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       self.skipTest('Dinat\'s attention operation is handled entirely by NATTEN.'				)





  def 		A						(	self   :			Optional[int]							,      _a   :			int							,      _a   :			Optional[int]							,      _a   :			Optional[int]							,      _a   :			Union[str, Any]				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=model_class(a_				)
       model.to(a_				)
       model.eval()

       with torch.no_grad():
            _SCREAMING_SNAKE_CASE												=model(**self._prepare_for_class(a_							,      a_				)				)

       _SCREAMING_SNAKE_CASE												=outputs.hidden_states

       _SCREAMING_SNAKE_CASE												=getattr(
           self.model_tester							,      'expected_num_hidden_layers'							,      len(self.model_tester.depths				) + 1				)
       self.assertEqual(len(a_				)							,      a_				)

       # Dinat has a different seq_length
       _SCREAMING_SNAKE_CASE												=(
           config.patch_size
           if isinstance(config.patch_size							,      collections.abc.Iterable				)
           else (config.patch_size, config.patch_size)
       )

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

            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=reshaped_hidden_states[0].shape
            _SCREAMING_SNAKE_CASE												=(
                reshaped_hidden_states[0].view(a_							,      a_							,      a_							,      a_				).permute(0							,      2							,      3							,      1				)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-3:]				)							,      [height, width, self.model_tester.embed_dim]							,      )





  def 		A						(	self   :			int				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

       _SCREAMING_SNAKE_CASE												=(
           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:
            _SCREAMING_SNAKE_CASE												=True
            self.check_hidden_states_output(a_							,      a_							,      a_							,      a_				)

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

            self.check_hidden_states_output(a_							,      a_							,      a_							,      a_				)





  @slow
  def 		A						(	self   :			Tuple				)			->					int:





       '''simple docstring'''






       for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE												=DinatModel.from_pretrained(a_				)
            self.assertIsNotNone(a_				)





  def 		A						(	self   :			Tuple				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs_for_common()

       _SCREAMING_SNAKE_CASE												=_config_zero_init(a_				)
       for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE												=model_class(config=a_				)
            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 							A__					( unittest.TestCase						):





  @cached_property
  def 		A						(	self   :			Union[str, Any]				)			->					Optional[Any]:





       '''simple docstring'''






       return AutoImageProcessor.from_pretrained('shi-labs/dinat-mini-in1k-224'				) if is_vision_available() else None





  @slow
  def 		A						(	self   :			Optional[Any]				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=DinatForImageClassification.from_pretrained('shi-labs/dinat-mini-in1k-224'				).to(a_				)
       _SCREAMING_SNAKE_CASE												=self.default_image_processor

       _SCREAMING_SNAKE_CASE												=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       _SCREAMING_SNAKE_CASE												=image_processor(images=a_							,      return_tensors='pt'				).to(a_				)

       # forward pass
       with torch.no_grad():
            _SCREAMING_SNAKE_CASE												=model(**a_				)

       # verify the logits
       _SCREAMING_SNAKE_CASE												=torch.Size((1, 1000)				)
       self.assertEqual(outputs.logits.shape							,      a_				)
       _SCREAMING_SNAKE_CASE												=torch.tensor([-0.15_45, -0.76_67, 0.46_42]				).to(a_				)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      a_							,      atol=1e-4				)				)







@require_torch
@require_natten
class 							A__					( unittest.TestCase  ,     A__						):
  A__					=       (DinatBackbone,) if is_torch_available() else ()
  A__					=       DinatConfig





  def 		A						(	self   :			Optional[int]				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=DinatModelTester(self				)



'''simple docstring'''
def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  Optional[Any]       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=len(_UpperCamelCase       )
     # Create a stack and push index of fist element to it
     _SCREAMING_SNAKE_CASE												=[]
     st.append(0       )

     # Span value of first element is always 1
     _SCREAMING_SNAKE_CASE												=1

     # Calculate span values for rest of the elements
     for i in range(1							,				_UpperCamelCase       ):
          # Pop elements from stack while stack is not
          # empty and top of stack is smaller than price[i]
          while len(_UpperCamelCase       ) > 0 and price[st[0]] <= price[i]:
               st.pop()

          # If stack becomes empty, then price[i] is greater
          # than all elements on left of it, i.e. price[0],
          # price[1], ..price[i-1]. Else the price[i]  is
          # greater than elements after top of stack
          _SCREAMING_SNAKE_CASE												=i + 1 if len(_UpperCamelCase       ) <= 0 else (i - st[0])

          # Push this element to stack
          st.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Any							,				_UpperCamelCase		:  str       )   ->    Any:
     """simple docstring"""




     for i in range(0							,				_UpperCamelCase       ):
          print(arr[i]							,				end=' '       )


# Driver program to test above function
lowerCamelCase				:   str      				=  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
lowerCamelCase				:   str      				=  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)







print_array(S, len(price))
