

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 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=13							,       lowerCAmelCase=64							,       lowerCAmelCase=4							,       lowerCAmelCase=3							,       lowerCAmelCase=16							,       lowerCAmelCase=[1, 2, 1]							,       lowerCAmelCase=[2, 4, 8]							,       lowerCAmelCase=3							,       lowerCAmelCase=[[3], [1, 2], [1]]							,       lowerCAmelCase=2.0							,       lowerCAmelCase=True							,       lowerCAmelCase=0.0							,       lowerCAmelCase=0.0							,       lowerCAmelCase=0.1							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=True							,       lowerCAmelCase=0.02							,       lowerCAmelCase=1e-5							,       lowerCAmelCase=True							,       lowerCAmelCase=None							,       lowerCAmelCase=True							,       lowerCAmelCase=10							,       lowerCAmelCase=["stage1", "stage2"]							,       lowerCAmelCase=[1, 2]							,       )  ->			str:
        '''simple docstring'''






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




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






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

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

        _lowercase     	=self.get_config()

        return config, pixel_values, labels




  def   A__					(    self )  ->			Optional[int]:
        '''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							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=DinatModel(config=__A )
        model.to(__A )
        model.eval()
        _lowercase     	=model(__A )

        _lowercase     	=(config.image_size // config.patch_size) // (2 ** (len(config.depths ) - 1))
        _lowercase     	=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							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






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

        # test greyscale images
        _lowercase     	=1
        _lowercase     	=DinatForImageClassification(__A )
        model.to(__A )
        model.eval()

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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=DinatBackbone(config=__A )
        model.to(__A )
        model.eval()
        _lowercase     	=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
        _lowercase     	=None
        _lowercase     	=DinatBackbone(config=__A )
        model.to(__A )
        model.eval()
        _lowercase     	=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 )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.prepare_config_and_inputs()
        _lowercase     	=config_and_inputs
        _lowercase     	={"pixel_values": pixel_values}
        return config, inputs_dict





@require_natten
@require_torch
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      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 )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=DinatModelTester(self )
        _lowercase     	=ConfigTester(self							,       config_class=__A							,       embed_dim=37 )




  def   A__					(    self )  ->			Union[str, Any]:
        '''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 )  ->			Union[str, Any]:
        '''simple docstring'''






        return




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A )




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A )




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=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 )  ->			str:
        '''simple docstring'''






        pass




  @unittest.skip(reason='Dinat does not use feedforward chunking' )
  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        pass




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

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




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowercase     	=["pixel_values"]
              self.assertListEqual(arg_names[:1]							,       __A )




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=model_class(__A )
        model.to(__A )
        model.eval()

        with torch.no_grad():
              _lowercase     	=model(**self._prepare_for_class(__A							,       __A ) )

        _lowercase     	=outputs.hidden_states

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

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

              _lowercase     	=reshaped_hidden_states[0].shape
              _lowercase     	=(
                  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 )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

        _lowercase     	=(
            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:
              _lowercase     	=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"]
              _lowercase     	=True

              self.check_hidden_states_output(__A							,       __A							,       __A							,       __A )




  @slow
  def   A__					(    self )  ->			str:
        '''simple docstring'''






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




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

        _lowercase     	=_config_zero_init(__A )
        for model_class in self.all_model_classes:
              _lowercase     	=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 			__lowerCAmelCase					( unittest.TestCase							):




  @cached_property
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






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




  @slow
  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






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

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

        # forward pass
        with torch.no_grad():
              _lowercase     	=model(**__A )

        # verify the logits
        _lowercase     	=torch.Size((1, 1_000) )
        self.assertEqual(outputs.logits.shape							,       __A )
        _lowercase     	=torch.tensor([-0.1545, -0.7667, 0.4642] ).to(__A )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]							,       __A							,       atol=1e-4 ) )





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




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=DinatModelTester(self )



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


