






from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
  from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    self.config_class(**self.inputs_dict							)
         self.parent.assertTrue(hasattr(lowerCamelCase       ,				"""embed_dim"""							)							)
         self.parent.assertTrue(hasattr(lowerCamelCase       ,				"""num_heads"""							)							)



class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Dict=13       ,				lowerCamelCase		:			List[Any]=64       ,				lowerCamelCase		:			str=3       ,				lowerCamelCase		:			List[str]=[16, 48, 96]       ,				lowerCamelCase		:			List[Any]=[1, 3, 6]       ,				lowerCamelCase		:			Tuple=[1, 2, 10]       ,				lowerCamelCase		:			Optional[int]=[7, 3, 3]       ,				lowerCamelCase		:			int=[4, 2, 2]       ,				lowerCamelCase		:			Dict=[2, 1, 1]       ,				lowerCamelCase		:			List[Any]=[2, 2, 2]       ,				lowerCamelCase		:			Optional[int]=[False, False, True]       ,				lowerCamelCase		:			Tuple=[0.0, 0.0, 0.0]       ,				lowerCamelCase		:			Optional[Any]=0.02       ,				lowerCamelCase		:			Optional[int]=1E-12       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			Union[str, Any]=True       ,				lowerCamelCase		:			Optional[int]=2       ,				)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_sizes
         _UpperCAmelCase							=    patch_stride
         _UpperCAmelCase							=    patch_padding
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    embed_dim
         _UpperCAmelCase							=    num_heads
         _UpperCAmelCase							=    stride_kv
         _UpperCAmelCase							=    depth
         _UpperCAmelCase							=    cls_token
         _UpperCAmelCase							=    attention_drop_rate
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    layer_norm_eps


  def  lowerCamelCase       (      self		:			Tuple							)						->	Tuple:


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

         _UpperCAmelCase							=    None
         if self.use_labels:
                # create a random int32 tensor of given shape
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_labels							)

         _UpperCAmelCase							=    self.get_config()
         return config, pixel_values, labels


  def  lowerCamelCase       (      self		:			Any							)						->	Tuple:


         """simple docstring"""
         return CvtConfig(
             image_size=self.image_size       ,				num_labels=self.num_labels       ,				num_channels=self.num_channels       ,				embed_dim=self.embed_dim       ,				num_heads=self.num_heads       ,				patch_sizes=self.patch_sizes       ,				patch_padding=self.patch_padding       ,				patch_stride=self.patch_stride       ,				stride_kv=self.stride_kv       ,				depth=self.depth       ,				cls_token=self.cls_token       ,				attention_drop_rate=self.attention_drop_rate       ,				initializer_range=self.initializer_range       ,				)


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


         """simple docstring"""
         _UpperCAmelCase							=    TFCvtModel(config=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				training=lowerCamelCase							)
         _UpperCAmelCase							=    (self.image_size, self.image_size)
         _UpperCAmelCase	,			_UpperCAmelCase							=    image_size[0], image_size[1]
         for i in range(len(self.depth							)							):
                _UpperCAmelCase							=    floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
                _UpperCAmelCase							=    floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.embed_dim[-1], height, width)							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    TFCvtForImageClassification(lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase       ,				training=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)





  def  lowerCamelCase       (      self		:			List[str]							)						->	Dict:


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



@require_tf
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
  _lowerCamelCase    =  (
      {'''feature-extraction''': TFCvtModel, '''image-classification''': TFCvtForImageClassification}
      if is_tf_available()
      else {}
  )
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Dict							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    TFCvtModelTester(self							)
         _UpperCAmelCase							=    TFCvtConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Dict:


         """simple docstring"""
         self.config_tester.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()


  @unittest.skip(reason="""Cvt does not output attentions"""							)
  def  lowerCamelCase       (      self		:			List[str]							)						->	List[str]:


         """simple docstring"""
         pass


  @unittest.skip(reason="""Cvt does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         pass


  @unittest.skip(reason="""Cvt does not support input and output embeddings"""							)
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         pass


  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""							)							) == 0       ,				reason="""TF does not support backprop for grouped convolutions on CPU."""       ,				)
  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         super().test_dataset_conversion()


  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""							)							) == 0       ,				reason="""TF does not support backprop for grouped convolutions on CPU."""       ,				)
  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	Dict:


         """simple docstring"""
         super().test_keras_fit()


  @unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"""							)
  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    tf.keras.mixed_precision.Policy("""mixed_float16"""							)
         tf.keras.mixed_precision.set_global_policy(lowerCamelCase							)
         super().test_keras_fit()
         tf.keras.mixed_precision.set_global_policy("""float32"""							)


  def  lowerCamelCase       (      self		:			int							)						->	List[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(lowerCamelCase							)
                _UpperCAmelCase							=    inspect.signature(model.call							)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase							=    [*signature.parameters.keys()]

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	str:


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			str       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict							):
                _UpperCAmelCase							=    model_class(lowerCamelCase							)

                _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)
                _UpperCAmelCase							=    outputs.hidden_states

                _UpperCAmelCase							=    len(self.model_tester.depth							)
                self.assertEqual(len(lowerCamelCase							)       ,				lowerCamelCase							)

                # verify the first hidden states (first block)
                self.assertListEqual(
                    list(hidden_states[0].shape[-3:]							)       ,				[
                        self.model_tester.embed_dim[0],
                        self.model_tester.image_size // 4,
                        self.model_tester.image_size // 4,
                    ]       ,				)

         _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(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

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

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    TFCvtModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						List[str]:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image





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

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)





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


         """simple docstring"""
         _UpperCAmelCase							=    TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""tf"""							)

         # forward pass
         _UpperCAmelCase							=    model(**lowerCamelCase							)

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

         _UpperCAmelCase							=    tf.constant([0.9285, 0.9015, -0.3150]							)
         self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,				lowerCamelCase       ,				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)
