




import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor






class  _lowerCAmelCase     (  lowerCAmelCase__	):

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.config_class(**self.inputs_dict		)
        self.parent.assertTrue(hasattr(_lowerCamelCase      ,				'''tf_padding'''		)		)
        self.parent.assertTrue(hasattr(_lowerCamelCase      ,				'''depth_multiplier'''		)		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=13      ,				__UpperCAmelCase=3      ,				__UpperCAmelCase=32      ,				__UpperCAmelCase=0.25      ,				__UpperCAmelCase=8      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=1024      ,				__UpperCAmelCase=32      ,				__UpperCAmelCase="relu6"      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=0.02      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=10      ,				__UpperCAmelCase=None      ,				):
        lowerCAmelCase__				:  List[str]		       =     parent
        lowerCAmelCase__				:  Optional[Any]		       =     batch_size
        lowerCAmelCase__				:  int		       =     num_channels
        lowerCAmelCase__				:  Optional[int]		       =     image_size
        lowerCAmelCase__				:  Union[str, Any]		       =     depth_multiplier
        lowerCAmelCase__				:  Union[str, Any]		       =     min_depth
        lowerCAmelCase__				:  Any		       =     tf_padding
        lowerCAmelCase__				:  List[Any]		       =     int(last_hidden_size * depth_multiplier		)
        lowerCAmelCase__				:  Optional[Any]		       =     output_stride
        lowerCAmelCase__				:  Optional[Any]		       =     hidden_act
        lowerCAmelCase__				:  Optional[Any]		       =     classifier_dropout_prob
        lowerCAmelCase__				:  Union[str, Any]		       =     use_labels
        lowerCAmelCase__				:  Tuple		       =     is_training
        lowerCAmelCase__				:  Any		       =     num_labels
        lowerCAmelCase__				:  List[Any]		       =     initializer_range
        lowerCAmelCase__				:  Optional[Any]		       =     scope

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        lowerCAmelCase__				:  List[str]		       =     None
        lowerCAmelCase__				:  List[str]		       =     None
        if self.use_labels:
          lowerCAmelCase__				:  Union[str, Any]		       =     ids_tensor([self.batch_size]      ,				self.num_labels		)
          lowerCAmelCase__				:  Tuple		       =     ids_tensor([self.batch_size, self.image_size, self.image_size]      ,				self.num_labels		)

        lowerCAmelCase__				:  Any		       =     self.get_config()

        return config, pixel_values, labels, pixel_labels

      def 	__magic_name__(   self		):
        return MobileNetVaConfig(
            num_channels=self.num_channels      ,				image_size=self.image_size      ,				depth_multiplier=self.depth_multiplier      ,				min_depth=self.min_depth      ,				tf_padding=self.tf_padding      ,				hidden_act=self.hidden_act      ,				classifier_dropout_prob=self.classifier_dropout_prob      ,				initializer_range=self.initializer_range      ,				)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[Any]		       =     MobileNetVaModel(config=_lowerCamelCase		)
        model.to(_lowerCamelCase		)
        model.eval()
        lowerCAmelCase__				:  Union[str, Any]		       =     model(_lowerCamelCase		)
        self.parent.assertEqual(
            result.last_hidden_state.shape      ,				(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )      ,				)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     self.num_labels
        lowerCAmelCase__				:  Union[str, Any]		       =     MobileNetVaForImageClassification(_lowerCamelCase		)
        model.to(_lowerCamelCase		)
        model.eval()
        lowerCAmelCase__				:  Any		       =     model(_lowerCamelCase      ,				labels=_lowerCamelCase		)
        self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     self.prepare_config_and_inputs()
        lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     config_and_inputs
        lowerCAmelCase__				:  Any		       =     {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class  _lowerCAmelCase     (  lowerCAmelCase__    ,			lowerCAmelCase__    ,			unittest.TestCase	):
      A__		     =		(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
      A__		     =		(
          {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
          if is_torch_available()
          else {}
      )

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     MobileNetVaModelTester(self		)
        lowerCAmelCase__				:  Optional[Any]		       =     MobileNetVaConfigTester(self      ,				config_class=_lowerCamelCase      ,				has_text_modality=_lowerCamelCase		)

      def 	__magic_name__(   self		):
        self.config_tester.run_common_tests()

      @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''		)
      def 	__magic_name__(   self		):
        pass

      @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''		)
      def 	__magic_name__(   self		):
        pass

      @unittest.skip(reason='''MobileNetV1 does not output attentions'''		)
      def 	__magic_name__(   self		):
        pass

      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__				:  int		       =     ['''pixel_values''']
          self.assertListEqual(arg_names[:1]      ,				_lowerCamelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_lowerCamelCase		)

      def 	__magic_name__(   self		):





        def check_hidden_states_output(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[Any]		       =     model_class(_lowerCamelCase		)
          model.to(_lowerCamelCase		)
          model.eval()

          with torch.no_grad():
            lowerCAmelCase__				:  Union[str, Any]		       =     model(**self._prepare_for_class(_lowerCamelCase      ,				_lowerCamelCase		)		)

          lowerCAmelCase__				:  Union[str, Any]		       =     outputs.hidden_states

          lowerCAmelCase__				:  Optional[Any]		       =     26
          self.assertEqual(len(_lowerCamelCase		)      ,				_lowerCamelCase		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[Any]		       =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase__				:  Any		       =     True
          check_hidden_states_output(_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase		)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCAmelCase__				:  Optional[Any]		       =     True

          check_hidden_states_output(_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase		)







      @slow
      def 	__magic_name__(   self		):





        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__				:  Optional[int]		       =     MobileNetVaModel.from_pretrained(_lowerCamelCase		)
          self.assertIsNotNone(_lowerCamelCase		)



def      __lowerCAmelCase			(					)     ->	Union[str, Any]:
  lowerCAmelCase__				:  Optional[Any]		       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
  return image






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

      @cached_property
      def 	__magic_name__(   self		):
        return (
            MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''		) if is_vision_available() else None
        )







      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''		).to(_lowerCamelCase		)

        lowerCAmelCase__				:  int		       =     self.default_image_processor
        lowerCAmelCase__				:  Any		       =     prepare_img()
        lowerCAmelCase__				:  int		       =     image_processor(images=_lowerCamelCase      ,				return_tensors='''pt'''		).to(_lowerCamelCase		)

        # forward pass
        with torch.no_grad():
          lowerCAmelCase__				:  Optional[int]		       =     model(**_lowerCamelCase		)

        # verify the logits
        lowerCAmelCase__				:  Any		       =     torch.Size((1, 1001)		)
        self.assertEqual(outputs.logits.shape      ,				_lowerCamelCase		)

        lowerCAmelCase__				:  Dict		       =     torch.tensor([-4.1739, -1.1233, 3.1205]		).to(_lowerCamelCase		)

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







import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_					    = [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def      __lowerCAmelCase			(					)     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     Github(os.environ['''GITHUB_TOKEN''']      )
  lowerCAmelCase__				:  str		       =     g.get_repo('''huggingface/diffusers'''      )
  lowerCAmelCase__				:  List[str]		       =     repo.get_issues(state='''open'''      )

  for issue in open_issues:
    lowerCAmelCase__				:  Optional[Any]		       =     sorted(issue.get_comments()	,      key=lambda UpperCamelCase      : i.created_at	,      reverse=UpperCamelCase      )
    lowerCAmelCase__				:  int		       =     comments[0] if len(UpperCamelCase      ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''      )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''      )
      issue.remove_from_labels('''stale'''      )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''      )
      issue.add_to_labels('''stale'''      )


if __name__ == "__main__":
     main()

