

'''simple docstring'''
import collections
import inspect
import unittest

from transformers import NatConfig
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 NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







class 							A__					:





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





       '''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   :			Dict				)			->					int:





       '''simple docstring'''






       return NatConfig(
           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							,      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   :			Any							,      _a   :			str							,      _a   :			Dict							,      _a   :			Union[str, Any]				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=NatModel(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[str]							,      _a   :			Optional[int]							,      _a   :			Dict							,      _a   :			Any				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=NatForImageClassification(_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												=NatForImageClassification(_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   :			Optional[int]							,      _a   :			Tuple							,      _a   :			Dict							,      _a   :			str				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=NatBackbone(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												=NatBackbone(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   :			Optional[Any]				)			->					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__					=       (
      (
          NatModel,
          NatForImageClassification,
          NatBackbone,
      )
      if is_torch_available()
      else ()
  )
  A__					=       (
      {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
      if is_torch_available()
      else {}
  )
  A__					=       False

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





  def 		A						(	self   :			str				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=NatModelTester(self				)
       _SCREAMING_SNAKE_CASE												=ConfigTester(self							,      config_class=_a							,      embed_dim=37				)





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





       '''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   :			Dict				)			->					List[Any]:





       '''simple docstring'''






       return





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





       '''simple docstring'''






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





  def 		A						(	self   :			Dict				)			->					str:





       '''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   :			str				)			->					Tuple:





       '''simple docstring'''






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





  @unittest.skip(reason='Nat does not use inputs_embeds'				)
  def 		A						(	self   :			str				)			->					Dict:





       '''simple docstring'''






       pass





  @unittest.skip(reason='Nat does not use feedforward chunking'				)
  def 		A						(	self   :			Dict				)			->					Union[str, Any]:





       '''simple docstring'''






       pass





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





       '''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   :			str				)			->					Optional[Any]:





       '''simple docstring'''






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





  def 		A						(	self   :			str							,      _a   :			int							,      _a   :			Tuple							,      _a   :			Tuple							,      _a   :			Tuple				)			->					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				)

       # Nat 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__ != "NatBackbone":
            _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   :			Any				)			->					Any:





       '''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   :			Optional[int]				)			->					Optional[Any]:





       '''simple docstring'''






       for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _SCREAMING_SNAKE_CASE												=NatModel.from_pretrained(_a				)
            self.assertIsNotNone(_a				)





  def 		A						(	self   :			str				)			->					Tuple:





       '''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]				)			->					List[str]:





       '''simple docstring'''






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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=NatForImageClassification.from_pretrained('shi-labs/nat-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([0.38_05, -0.86_76, -0.39_12]				).to(_a				)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      _a							,      atol=1e-4				)				)







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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=NatModelTester(self				)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase				:   str      				=  {"configuration_dinat": ["DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DinatConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase				:   Optional[int]      				=  [
         "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DinatForImageClassification",
         "DinatModel",
         "DinatPreTrainedModel",
         "DinatBackbone",
     ]

if TYPE_CHECKING:
     from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dinat import (
              DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DinatBackbone,
              DinatForImageClassification,
              DinatModel,
              DinatPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase				:   Optional[Any]      				=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
