





from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor
class        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       Optional[Any]    ,  a    :       List[Any]    ,  a    :       List[str]=13    ,  a    :       Union[str, Any]=30    ,  a    :       Dict=2    ,  a    :       Optional[int]=3    ,  a    :       Any=True    ,  a    :       Any=True    ,  a    :       Tuple=32    ,  a    :       Optional[Any]=2    ,  a    :       Tuple=4    ,  a    :       Optional[int]=37    ,  a    :       Optional[int]="gelu"    ,  a    :       int=0.1    ,  a    :       List[Any]=0.1    ,  a    :       Any=10    ,  a    :       int=0.02    ,  a    :       Dict=3    ,  a    :       Optional[Any]=0.6    ,  a    :       Optional[int]=None    ,  )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						image_size
     lowercase__			    =						patch_size
     lowercase__			    =						num_channels
     lowercase__			    =						is_training
     lowercase__			    =						use_labels
     lowercase__			    =						hidden_size
     lowercase__			    =						num_hidden_layers
     lowercase__			    =						num_attention_heads
     lowercase__			    =						intermediate_size
     lowercase__			    =						hidden_act
     lowercase__			    =						hidden_dropout_prob
     lowercase__			    =						attention_probs_dropout_prob
     lowercase__			    =						type_sequence_label_size
     lowercase__			    =						initializer_range
     lowercase__			    =						mask_ratio
     lowercase__			    =						scope

     # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
     # (we add 1 for the [CLS] token)
     lowercase__			    =						(image_size // patch_size) ** 2
     lowercase__			    =						int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Tuple:





     """simple docstring"""







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

     lowercase__			    =						None
     if self.use_labels:
       lowercase__			    =						ids_tensor([self.batch_size]    ,  self.type_sequence_label_size )

     lowercase__			    =						self.get_config()

     return config, pixel_values, labels


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Tuple:





     """simple docstring"""







     return ViTMAEConfig(
         image_size=self.image_size    ,  patch_size=self.patch_size    ,  num_channels=self.num_channels    ,  hidden_size=self.hidden_size    ,  num_hidden_layers=self.num_hidden_layers    ,  num_attention_heads=self.num_attention_heads    ,  intermediate_size=self.intermediate_size    ,  decoder_hidden_size=self.hidden_size    ,  decoder_num_hidden_layers=self.num_hidden_layers    ,  decoder_num_attention_heads=self.num_attention_heads    ,  decoder_intermediate_size=self.intermediate_size    ,  hidden_act=self.hidden_act    ,  hidden_dropout_prob=self.hidden_dropout_prob    ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,  is_decoder=UpperCAmelCase__    ,  initializer_range=self.initializer_range    ,  mask_ratio=self.mask_ratio    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       List[str]    ,  a    :       Optional[Any]    ,  a    :       Dict )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						TFViTMAEModel(config=UpperCAmelCase__ )
     lowercase__			    =						model(UpperCAmelCase__    ,  training=UpperCAmelCase__ )
     self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       List[str]    ,  a    :       Union[str, Any]    ,  a    :       Tuple )->     Tuple:





     """simple docstring"""







     lowercase__			    =						TFViTMAEForPreTraining(UpperCAmelCase__ )
     lowercase__			    =						model(UpperCAmelCase__    ,  training=UpperCAmelCase__ )
     # expected sequence length = num_patches
     lowercase__			    =						(self.image_size // self.patch_size) ** 2
     lowercase__			    =						self.patch_size**2 * self.num_channels
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, num_patches, expected_num_channels) )

     # test greyscale images
     lowercase__			    =						1
     lowercase__			    =						TFViTMAEForPreTraining(UpperCAmelCase__ )

     lowercase__			    =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     lowercase__			    =						model(UpperCAmelCase__    ,  training=UpperCAmelCase__ )
     lowercase__			    =						self.patch_size**2
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, num_patches, expected_num_channels) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Tuple:





     """simple docstring"""







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






@require_tf
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Union[str, Any]				      = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   _UpperCamelCase					:	Optional[Any]				      = {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

   _UpperCamelCase					:	Union[str, Any]				      = False
   _UpperCamelCase					:	Any				      = False
   _UpperCamelCase					:	List[str]				      = False
   _UpperCamelCase					:	Dict				      = False


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





     """simple docstring"""







     lowercase__			    =						TFViTMAEModelTester(self )
     lowercase__			    =						ConfigTester(self    ,  config_class=UpperCAmelCase__    ,  has_text_modality=UpperCAmelCase__    ,  hidden_size=37 )


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





     """simple docstring"""







     self.config_tester.run_common_tests()


   @unittest.skip(reason='ViTMAE does not use inputs_embeds' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Dict:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     List[str]:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(UpperCAmelCase__ )
       self.assertIsInstance(model.get_input_embeddings()    ,  (tf.keras.layers.Layer) )
       lowercase__			    =						model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCAmelCase__    ,  tf.keras.layers.Layer ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     str:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

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

       lowercase__			    =						['pixel_values']
       self.assertListEqual(arg_names[:1]    ,  UpperCAmelCase__ )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase__ )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__ )


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





     """simple docstring"""







     np.random.seed(2 )

     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__			    =						int((config.image_size // config.patch_size) ** 2 )
     lowercase__			    =						np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(UpperCAmelCase__ )
       lowercase__			    =						self._prepare_for_class(UpperCAmelCase__    ,  UpperCAmelCase__ )

       lowercase__			    =						model(UpperCAmelCase__    ,  noise=UpperCAmelCase__ )

       lowercase__			    =						copy.deepcopy(self._prepare_for_class(UpperCAmelCase__    ,  UpperCAmelCase__ ) )
       lowercase__			    =						model(**UpperCAmelCase__    ,  noise=UpperCAmelCase__ )
       lowercase__			    =						outputs_dict[0].numpy()
       lowercase__			    =						outputs_keywords[0].numpy()

       self.assertLess(np.sum(np.abs(output_dict - output_keywords ) )    ,  1E-6 )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Optional[int]:





     """simple docstring"""







     np.random.seed(2 )

     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__			    =						int((config.image_size // config.patch_size) ** 2 )
     lowercase__			    =						np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

     def prepare_numpy_arrays(a    :       Optional[Any] ):
       lowercase__			    =						{}
       for k, v in inputs_dict.items():
         if tf.is_tensor(UpperCAmelCase__ ):
           lowercase__			    =						v.numpy()
         else:
           lowercase__			    =						np.array(UpperCAmelCase__ )

       return inputs_np_dict

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(UpperCAmelCase__ )

       lowercase__			    =						self._prepare_for_class(UpperCAmelCase__    ,  UpperCAmelCase__ )
       lowercase__			    =						prepare_numpy_arrays(UpperCAmelCase__ )

       lowercase__			    =						model(UpperCAmelCase__    ,  noise=UpperCAmelCase__ )
       lowercase__			    =						model(**UpperCAmelCase__    ,  noise=UpperCAmelCase__ )
       self.assert_outputs_same(UpperCAmelCase__    ,  UpperCAmelCase__ )


   def  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       Any    ,  a    :       Any    ,  a    :       Optional[int] )->     Tuple:





     """simple docstring"""







     np.random.seed(2 )

     lowercase__			    =						int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
     lowercase__			    =						np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
     lowercase__			    =						tf.constant(UpperCAmelCase__ )

     # Add `noise` argument.
     # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
     lowercase__			    =						tf_noise

     super().check_pt_tf_models(UpperCAmelCase__    ,  UpperCAmelCase__    ,  UpperCAmelCase__ )


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





     """simple docstring"""







     np.random.seed(2 )

     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__			    =						{
         module_member
         for model_class in self.all_model_classes
         for module in (import_module(model_class.__module__ ),)
         for module_member_name in dir(UpperCAmelCase__ )
         if module_member_name.endswith('MainLayer' )
         # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
         and module_member_name[: -len('MainLayer' )] == model_class.__name__[: -len('Model' )]
         for module_member in (getattr(UpperCAmelCase__    ,  UpperCAmelCase__ ),)
         if isinstance(UpperCAmelCase__    ,  UpperCAmelCase__ )
         and tf.keras.layers.Layer in module_member.__bases__
         and getattr(UpperCAmelCase__    ,  '_keras_serializable'    ,  UpperCAmelCase__ )
     }

     lowercase__			    =						int((config.image_size // config.patch_size) ** 2 )
     lowercase__			    =						np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
     lowercase__			    =						tf.convert_to_tensor(UpperCAmelCase__ )
     inputs_dict.update({'noise': noise} )

     for main_layer_class in tf_main_layer_classes:
       lowercase__			    =						main_layer_class(UpperCAmelCase__ )

       lowercase__			    =						{
           name: tf.keras.Input(tensor.shape[1:]    ,  dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
       }

       lowercase__			    =						tf.keras.Model(UpperCAmelCase__    ,  outputs=main_layer(UpperCAmelCase__ ) )
       lowercase__			    =						model(UpperCAmelCase__ )

       with tempfile.TemporaryDirectory() as tmpdirname:
         lowercase__			    =						os.path.join(UpperCAmelCase__    ,  'keras_model.h5' )
         model.save(UpperCAmelCase__ )
         lowercase__			    =						tf.keras.models.load_model(
             UpperCAmelCase__    ,  custom_objects={main_layer_class.__name__: main_layer_class} )
         assert isinstance(UpperCAmelCase__    ,  tf.keras.Model )
         lowercase__			    =						model(UpperCAmelCase__ )
         self.assert_outputs_same(UpperCAmelCase__    ,  UpperCAmelCase__ )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[str]:





     """simple docstring"""







     np.random.seed(2 )

     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__			    =						int((config.image_size // config.patch_size) ** 2 )
     lowercase__			    =						np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(UpperCAmelCase__ )
       lowercase__			    =						self._prepare_for_class(UpperCAmelCase__    ,  UpperCAmelCase__ )
       lowercase__			    =						model(UpperCAmelCase__    ,  noise=UpperCAmelCase__ )

       if model_class.__name__ == "TFViTMAEModel":
         lowercase__			    =						outputs.last_hidden_state.numpy()
         lowercase__			    =						0
       else:
         lowercase__			    =						outputs.logits.numpy()
         lowercase__			    =						0

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(UpperCAmelCase__    ,  saved_model=UpperCAmelCase__ )
         lowercase__			    =						model_class.from_pretrained(UpperCAmelCase__ )
         lowercase__			    =						model(UpperCAmelCase__    ,  noise=UpperCAmelCase__ )

         if model_class.__name__ == "TFViTMAEModel":
           lowercase__			    =						after_outputs['last_hidden_state'].numpy()
           lowercase__			    =						0
         else:
           lowercase__			    =						after_outputs['logits'].numpy()
           lowercase__			    =						0

         lowercase__			    =						np.amax(np.abs(out_a - out_a ) )
         self.assertLessEqual(UpperCAmelCase__    ,  1E-5 )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     List[Any]:





     """simple docstring"""







     np.random.seed(2 )

     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     lowercase__			    =						int((config.image_size // config.patch_size) ** 2 )
     lowercase__			    =						np.random.uniform(size=(self.model_tester.batch_size, num_patches) )

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(UpperCAmelCase__ )
       lowercase__			    =						self._prepare_for_class(UpperCAmelCase__    ,  UpperCAmelCase__ )

       lowercase__			    =						model(UpperCAmelCase__    ,  noise=UpperCAmelCase__ )
       lowercase__			    =						model.get_config()
       # make sure that returned config is jsonifiable, which is required by keras
       json.dumps(UpperCAmelCase__ )
       lowercase__			    =						model_class.from_config(model.get_config() )
       # make sure it also accepts a normal config
       lowercase__			    =						model_class.from_config(model.config )
       lowercase__			    =						new_model(UpperCAmelCase__ )  # Build model
       new_model.set_weights(model.get_weights() )
       lowercase__			    =						new_model(UpperCAmelCase__    ,  noise=UpperCAmelCase__ )

       self.assert_outputs_same(UpperCAmelCase__    ,  UpperCAmelCase__ )


   @unittest.skip(
       reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Optional[int]:





     """simple docstring"""







     pass


   @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     List[str]:





     """simple docstring"""







     pass


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						TFViTMAEModel.from_pretrained('google/vit-base-patch16-224' )
     self.assertIsNotNone(UpperCAmelCase__ )






def     __UpperCamelCase						()       ->     Optional[Any]:
  lowercase__			    =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
  return image


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


   @cached_property
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







     return ViTImageProcessor.from_pretrained('facebook/vit-mae-base' ) if is_vision_available() else None


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Union[str, Any]:





     """simple docstring"""







     np.random.seed(2 )

     lowercase__			    =						TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base' )

     lowercase__			    =						self.default_image_processor
     lowercase__			    =						prepare_img()
     lowercase__			    =						image_processor(images=UpperCAmelCase__    ,  return_tensors='tf' )

     # prepare a noise vector that will be also used for testing the TF model
     # (this way we can ensure that the PT and TF models operate on the same inputs)
     lowercase__			    =						ViTMAEConfig()
     lowercase__			    =						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
     lowercase__			    =						np.random.uniform(size=(1, num_patches) )

     # forward pass
     lowercase__			    =						model(**UpperCAmelCase__    ,  noise=UpperCAmelCase__ )

     # verify the logits
     lowercase__			    =						tf.convert_to_tensor([1, 196, 768] )
     self.assertEqual(outputs.logits.shape    ,  UpperCAmelCase__ )

     lowercase__			    =						tf.convert_to_tensor(
         [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )

     tf.debugging.assert_near(outputs.logits[0, :3, :3]    ,  UpperCAmelCase__    ,  atol=1E-4 )











import argparse
import json
import subprocess




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  lowercase__			    =						[]

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

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

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

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


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return values.split(','	)

      lowercase_					     =	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."""
      )
      lowercase_					     =	parser.parse_args()


      get_runner_status(args.target_runners, args.token)


