





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFResNetForImageClassification, TFResNetModel
      from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[str]=3		,lowerCamelCase__		:					Optional[Any]=32		,lowerCamelCase__		:					Any=3		,lowerCamelCase__		:					Any=10		,lowerCamelCase__		:					Any=[10, 20, 30, 40]		,lowerCamelCase__		:					Any=[1, 1, 2, 1]		,lowerCamelCase__		:					Union[str, Any]=True		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					Union[str, Any]="relu"		,lowerCamelCase__		:					Dict=3		,lowerCamelCase__		:					Any=None		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		image_size
           SCREAMING_SNAKE_CASE	=		num_channels
           SCREAMING_SNAKE_CASE	=		embeddings_size
           SCREAMING_SNAKE_CASE	=		hidden_sizes
           SCREAMING_SNAKE_CASE	=		depths
           SCREAMING_SNAKE_CASE	=		is_training
           SCREAMING_SNAKE_CASE	=		use_labels
           SCREAMING_SNAKE_CASE	=		hidden_act
           SCREAMING_SNAKE_CASE	=		num_labels
           SCREAMING_SNAKE_CASE	=		scope
           SCREAMING_SNAKE_CASE	=		len(lowerCamelCase__ )






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




           '''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        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Union[str, Any]:




           '''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		,image_size=self.image_size		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModel(config=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ )
           # 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 // 32, self.image_size // 32)		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.num_labels
           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,labels=lowerCamelCase__ )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[Any]:




           '''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_tf
class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __snake_case					:			int				=    (
         {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case					:			Optional[int]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			str				=    False






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__ )






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




           '''simple docstring'''


           return






     @unittest.skip(reason="""ResNet does not use inputs_embeds""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           pass






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




           '''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(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		inspect.signature(model.call )
                 # 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]		,lowerCamelCase__ )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__		:					str		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict ):
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		model(**self._prepare_for_class(lowerCamelCase__		,lowerCamelCase__ ) )

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

                 SCREAMING_SNAKE_CASE	=		self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__ )		,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	=		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE	=		["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       SCREAMING_SNAKE_CASE	=		layer_type
                       SCREAMING_SNAKE_CASE	=		True
                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )

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

                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE	=		TFResNetModel.from_pretrained(lowerCamelCase__ )
                 self.assertIsNotNone(lowerCamelCase__ )



def      __lowercase     (   )				->     Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image



@require_tf
@require_vision
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


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






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(images=lowerCamelCase__		,return_tensors="""tf""" )

           # forward pass
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           # verify the logits
           SCREAMING_SNAKE_CASE	=		tf.TensorShape((1, 1000) )
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tf.constant([-11.1069, -9.7877, -8.3777] )

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









import os
from datetime import datetime as dt

from github import Github


SCREAMING_SNAKE_CASE_		     = [
    """good first issue""",
    """feature request""",
    """wip""",
]



def      __lowercase     (   )				->     Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		Github(os.environ["""GITHUB_TOKEN"""]			)
      SCREAMING_SNAKE_CASE	=		g.get_repo("""huggingface/accelerate"""			)
      SCREAMING_SNAKE_CASE	=		repo.get_issues(state="""open"""			)

      for issue in open_issues:
            SCREAMING_SNAKE_CASE	=		sorted([comment for comment in issue.get_comments()]				,		key=lambda _SCREAMING_SNAKE_CASE			: i.created_at				,		reverse=_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		comments[0] if len(_SCREAMING_SNAKE_CASE			) > 0 else None
            SCREAMING_SNAKE_CASE	=		dt.utcnow()
            SCREAMING_SNAKE_CASE	=		(current_time - issue.updated_at).days
            SCREAMING_SNAKE_CASE	=		(current_time - issue.created_at).days
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and days_since_updated > 7
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
            ):
                  # Close issue since it has been 7 days of inactivity since bot mention.
                  issue.edit(state="""closed"""			)
            elif (
                days_since_updated > 23
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
            ):
                  # Add stale comment
                  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/accelerate/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""			)


if __name__ == "__main__":
      main()


