


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, MobileNetVaForSemanticSegmentation, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor

class 						__a (							UpperCAmelCase     ):
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'tf_padding'		)		)
									self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE     ,       'depth_multiplier'		)		)



class 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=13     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=32     ,       _SCREAMING_SNAKE_CASE=0.25     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=6     ,       _SCREAMING_SNAKE_CASE=32     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE="relu6"     ,       _SCREAMING_SNAKE_CASE=1280     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=10     ,       _SCREAMING_SNAKE_CASE=None     ,       )					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     num_channels
									_UpperCAmelCase											=     image_size
									_UpperCAmelCase											=     depth_multiplier
									_UpperCAmelCase											=     depth_divisible_by
									_UpperCAmelCase											=     min_depth
									_UpperCAmelCase											=     expand_ratio
									_UpperCAmelCase											=     tf_padding
									_UpperCAmelCase											=     output_stride
									_UpperCAmelCase											=     first_layer_is_expansion
									_UpperCAmelCase											=     finegrained_output
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
									_UpperCAmelCase											=     classifier_dropout_prob
									_UpperCAmelCase											=     use_labels
									_UpperCAmelCase											=     is_training
									_UpperCAmelCase											=     num_labels
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     scope
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





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

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

									_UpperCAmelCase											=     self.get_config()

									return config, pixel_values, labels, pixel_labels
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									return MobileNetVaConfig(
									    num_channels=self.num_channels     ,       image_size=self.image_size     ,       depth_multiplier=self.depth_multiplier     ,       depth_divisible_by=self.depth_divisible_by     ,       min_depth=self.min_depth     ,       expand_ratio=self.expand_ratio     ,       output_stride=self.output_stride     ,       first_layer_is_expansion=self.first_layer_is_expansion     ,       finegrained_output=self.finegrained_output     ,       hidden_act=self.hidden_act     ,       tf_padding=self.tf_padding     ,       classifier_dropout_prob=self.classifier_dropout_prob     ,       initializer_range=self.initializer_range     ,       )
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     MobileNetVaModel(config=_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									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,
									    )     ,       )
									self.parent.assertEqual(
									    result.pooler_output.shape     ,       (self.batch_size, self.last_hidden_size)     ,       )
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     self.num_labels
									_UpperCAmelCase											=     MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(
									    result.logits.shape     ,       (
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )     ,       )
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(
									    result.logits.shape     ,       (
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )     ,       )






		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     self.prepare_config_and_inputs()
									_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     config_and_inputs
									_UpperCAmelCase											=     {'pixel_values': pixel_values}
									return config, inputs_dict



@require_torch
class 						__a (							UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase     ):
		_a      :    List[str]           =      (
		    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		_a      :    Optional[int]           =      (
		    {
		        'feature-extraction': MobileNetVaModel,
		        'image-classification': MobileNetVaForImageClassification,
		        'image-segmentation': MobileNetVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_a      :    Optional[Any]           =      False
		_a      :    str           =      False
		_a      :    Optional[int]           =      False
		_a      :    List[Any]           =      False
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     MobileNetVaModelTester(self		)
									_UpperCAmelCase											=     MobileNetVaConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       has_text_modality=_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		@unittest.skip(reason='MobileNetV2 does not use inputs_embeds'		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									pass
		@unittest.skip(reason='MobileNetV2 does not support input and output embeddings'		)
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									pass
		@unittest.skip(reason='MobileNetV2 does not output attentions'		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									pass
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCAmelCase											=     ['pixel_values']
																self.assertListEqual(arg_names[:1]     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									def check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																model.to(_SCREAMING_SNAKE_CASE		)
																model.eval()

																with torch.no_grad():
																							_UpperCAmelCase											=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)		)

																_UpperCAmelCase											=     outputs.hidden_states

																_UpperCAmelCase											=     16
																self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     True
																check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

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

																check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE		)






		@slow
		def       UpperCAmelCase__							(     self		)					->			Any:




									"""simple docstring"""





									for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase											=     MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)



def 			lowerCAmelCase__					(     )    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
							return image







@require_torch
@require_vision
class 						__a (							unittest.TestCase     ):
		@cached_property
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									return (
									    MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'		) if is_vision_available() else None
									)
		@slow
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'		).to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     self.default_image_processor
									_UpperCAmelCase											=     prepare_img()
									_UpperCAmelCase											=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'		).to(_SCREAMING_SNAKE_CASE		)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase											=     model(**_SCREAMING_SNAKE_CASE		)

									# verify the logits
									_UpperCAmelCase											=     torch.Size((1, 1001)		)
									self.assertEqual(outputs.logits.shape     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     torch.tensor([0.2445, -1.1993, 0.1905]		).to(_SCREAMING_SNAKE_CASE		)

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






		@slow
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)
									_UpperCAmelCase											=     model.to(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'		)

									_UpperCAmelCase											=     prepare_img()
									_UpperCAmelCase											=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'		).to(_SCREAMING_SNAKE_CASE		)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase											=     model(**_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     outputs.logits

									# verify the logits
									_UpperCAmelCase											=     torch.Size((1, 21, 65, 65)		)
									self.assertEqual(logits.shape     ,       _SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     torch.tensor(
									    [
									        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
									        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
									        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
									    ]     ,       device=_SCREAMING_SNAKE_CASE     ,       )

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




import argparse
import json
import subprocess

def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						Optional[int]			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     []

							_UpperCAmelCase											=     (
							    F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
							    ' https://api.github.com/repos/huggingface/transformers/actions/runners'
							)
							_UpperCAmelCase											=     subprocess.run(a__     ,    shell=a__     ,    stdout=subprocess.PIPE			)
							_UpperCAmelCase											=     output.stdout.decode('utf-8'			)
							_UpperCAmelCase											=     json.loads(a__			)

							_UpperCAmelCase											=     status['runners']
							for runner in runners:
														if runner["name"] in target_runners:
																					if runner["status"] == "offline":
																												offline_runners.append(a__			)

    # save the result so we can report them on Slack
							with open('offline_runners.txt'     ,    'w'			) as fp:
														fp.write(json.dumps(a__			)			)

							if len(a__			) > 0:
														_UpperCAmelCase											=     '\n'.join([x['name'] for x in offline_runners]			)
														raise ValueError(F'''The following runners are offline:\n{failed}'''			)


if __name__ == "__main__":

		def 			lowerCAmelCase__					(     a__:						int			)    ->						List[Any]:


									'''simple docstring'''





									return values.split(','			)

		lowerCAmelCase__    :Optional[Any]       =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--target_runners''',
		    default=None,
		    type=list_str,
		    required=True,
		    help='''Comma-separated list of runners to check status.''',
		)

		parser.add_argument(
		    '''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
		)
		lowerCAmelCase__    :Tuple       =				parser.parse_args()







		get_runner_status(args.target_runners, args.token)
