

import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
  from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class 							a__							:







  """simple docstring"""




  def __init__(      self     :			Optional[Any]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			int=3					,					UpperCAmelCase__     :			List[str]=3_2					,					UpperCAmelCase__     :			Optional[Any]=3					,					UpperCAmelCase__     :			int=1_0					,					UpperCAmelCase__     :			Optional[int]=[1_0, 2_0, 3_0, 4_0]					,					UpperCAmelCase__     :			Union[str, Any]=[1, 1, 2, 1]					,					UpperCAmelCase__     :			Optional[int]=True					,					UpperCAmelCase__     :			Tuple=True					,					UpperCAmelCase__     :			Union[str, Any]="relu"					,					UpperCAmelCase__     :			Union[str, Any]=3					,					UpperCAmelCase__     :			List[str]=None					,					UpperCAmelCase__     :			List[str]=["stage2", "stage3", "stage4"]					,					UpperCAmelCase__     :			Union[str, Any]=[2, 3, 4]					,					)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = parent
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = batch_size
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = image_size
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = num_channels
        SCREAMING_SNAKE_CASE			:   List[str]	  = embeddings_size
        SCREAMING_SNAKE_CASE			:   Tuple	  = hidden_sizes
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = depths
        SCREAMING_SNAKE_CASE			:   str	  = is_training
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = use_labels
        SCREAMING_SNAKE_CASE			:   List[Any]	  = hidden_act
        SCREAMING_SNAKE_CASE			:   str	  = num_labels
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = scope
        SCREAMING_SNAKE_CASE			:   List[Any]	  = len(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   List[Any]	  = out_features
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = out_indices


  def 			_lowercase   (      self     :			List[Any]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   str	  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.get_config()

        return config, pixel_values, labels


  def 			_lowercase   (      self     :			Union[str, Any]		)       ->int:
        """simple docstring"""







        return ResNetConfig(
            num_channels=self.num_channels					,					embeddings_size=self.embeddings_size					,					hidden_sizes=self.hidden_sizes					,					depths=self.depths					,					hidden_act=self.hidden_act					,					num_labels=self.num_labels					,					out_features=self.out_features					,					out_indices=self.out_indices					,					)


  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Optional[Any]					,					UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			int		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = ResNetModel(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE			:   List[Any]	  = model(UpperCAmelCase__		)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape					,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,					)


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			Any					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Union[str, Any]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.num_labels
        SCREAMING_SNAKE_CASE			:   List[Any]	  = ResNetForImageClassification(UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE			:   Tuple	  = model(UpperCAmelCase__					,					labels=UpperCAmelCase__		)
        self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels)		)


  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			Any					,					UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			Dict		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = ResNetBackbone(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = model(UpperCAmelCase__		)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps		)					,					len(config.out_features		)		)
        self.parent.assertListEqual(list(result.feature_maps[0].shape		)					,					[self.batch_size, self.hidden_sizes[1], 4, 4]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		)					,					len(config.out_features		)		)
        self.parent.assertListEqual(model.channels					,					config.hidden_sizes[1:]		)

        # verify backbone works with out_features=None
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = None
        SCREAMING_SNAKE_CASE			:   Tuple	  = ResNetBackbone(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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, self.hidden_sizes[-1], 1, 1]		)

        # verify channels
        self.parent.assertEqual(len(model.channels		)					,					1		)
        self.parent.assertListEqual(model.channels					,					[config.hidden_sizes[-1]]		)




  def 			_lowercase   (      self     :			List[str]		)       ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = config_and_inputs
        SCREAMING_SNAKE_CASE			:   int	  = {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 							a__							( UpperCAmelCase						,      UpperCAmelCase						,      unittest.TestCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Tuple      							=(
      (
          ResNetModel,
          ResNetForImageClassification,
          ResNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  UpperCAmelCase__   :			List[Any]      							=(
      {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCAmelCase__   :			List[Any]      							=True
  UpperCAmelCase__   :			str      							=False
  UpperCAmelCase__   :			List[str]      							=False
  UpperCAmelCase__   :			List[Any]      							=False
  UpperCAmelCase__   :			Any      							=False


  def 			_lowercase   (      self     :			int		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = ResNetModelTester(self		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = ConfigTester(self					,					config_class=UpperCAmelCase__					,					has_text_modality=UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Dict		)       ->List[str]:
        """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 			_lowercase   (      self     :			Any		)       ->Dict:
        """simple docstring"""







        return


  @unittest.skip(reason="""ResNet does not use inputs_embeds"""		)
  def 			_lowercase   (      self     :			Optional[int]		)       ->Dict:
        """simple docstring"""







        pass


  @unittest.skip(reason="""ResNet does not support input and output embeddings"""		)
  def 			_lowercase   (      self     :			Optional[Any]		)       ->List[str]:
        """simple docstring"""







        pass


  def 			_lowercase   (      self     :			Optional[Any]		)       ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Tuple	  = self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE			:   str	  = ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]					,					UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[Any]		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[str]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Optional[int]		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Tuple	  = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE			:   Dict	  = model_class(config=UpperCAmelCase__		)
              for name, module in model.named_modules():
                    if isinstance(UpperCAmelCase__					,					(nn.BatchNormad, nn.GroupNorm)		):
                          self.assertTrue(
                              torch.all(module.weight == 1		)					,					msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,					)
                          self.assertTrue(
                              torch.all(module.bias == 0		)					,					msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,					)


  def 			_lowercase   (      self     :			int		)       ->str:
        """simple docstring"""







        def check_hidden_states_output(UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			int		):
              SCREAMING_SNAKE_CASE			:   str	  = model_class(UpperCAmelCase__		)
              model.to(UpperCAmelCase__		)
              model.eval()

              with torch.no_grad():
                    SCREAMING_SNAKE_CASE			:   int	  = model(**self._prepare_for_class(UpperCAmelCase__					,					UpperCAmelCase__		)		)

              SCREAMING_SNAKE_CASE			:   Dict	  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              SCREAMING_SNAKE_CASE			:   Any	  = self.model_tester.num_stages
              self.assertEqual(len(UpperCAmelCase__		)					,					expected_num_stages + 1		)

              # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]		)					,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,					)

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Any	  = self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE			:   int	  = ["""basic""", """bottleneck"""]
        for model_class in self.all_model_classes:
              for layer_type in layers_type:
                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = layer_type
                    SCREAMING_SNAKE_CASE			:   str	  = True
                    check_hidden_states_output(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__		)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    SCREAMING_SNAKE_CASE			:   int	  = True

                    check_hidden_states_output(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__		)


  @unittest.skip(reason="""ResNet does not use feedforward chunking"""		)
  def 			_lowercase   (      self     :			str		)       ->Dict:
        """simple docstring"""







        pass


  def 			_lowercase   (      self     :			Tuple		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)




  @slow
  def 			_lowercase   (      self     :			str		)       ->str:
        """simple docstring"""







        for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE			:   Dict	  = ResNetModel.from_pretrained(UpperCAmelCase__		)
              self.assertIsNotNone(UpperCAmelCase__		)






def        __lowercase   (  )  ->    List[Any]:
      SCREAMING_SNAKE_CASE			:   Tuple	  = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
      return image







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







  """simple docstring"""




  @cached_property
  def 			_lowercase   (      self     :			Optional[int]		)       ->Optional[Any]:
        """simple docstring"""







        return (
            AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
            if is_vision_available()
            else None
        )




  @slow
  def 			_lowercase   (      self     :			Dict		)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[int]	  = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   str	  = self.default_image_processor
        SCREAMING_SNAKE_CASE			:   Dict	  = prepare_img()
        SCREAMING_SNAKE_CASE			:   int	  = image_processor(images=UpperCAmelCase__					,					return_tensors="""pt"""		).to(UpperCAmelCase__		)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE			:   Dict	  = model(**UpperCAmelCase__		)

        # verify the logits
        SCREAMING_SNAKE_CASE			:   str	  = torch.Size((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape					,					UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = torch.tensor([-11.10_69, -9.78_77, -8.37_77]		).to(UpperCAmelCase__		)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]					,					UpperCAmelCase__					,					atol=1e-4		)		)







@require_torch
class 							a__							( UpperCAmelCase						,      unittest.TestCase							):







  """simple docstring"""




  UpperCAmelCase__   :			List[str]      							=(ResNetBackbone,) if is_torch_available() else ()
  UpperCAmelCase__   :			int      							=False
  UpperCAmelCase__   :			Optional[int]      							=ResNetConfig


  def 			_lowercase   (      self     :			int		)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = ResNetModelTester(self		)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__			:  Dict					     =     {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Union[str, Any]					     =     [
      """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """HubertForCTC""",
      """HubertForSequenceClassification""",
      """HubertModel""",
      """HubertPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  List[Any]					     =     [
      """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFHubertForCTC""",
      """TFHubertModel""",
      """TFHubertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_hubert import (
        HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        HubertForCTC,
        HubertForSequenceClassification,
        HubertModel,
        HubertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_hubert import (
        TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFHubertForCTC,
        TFHubertModel,
        TFHubertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase__			:  str					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




