





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormModel,
      )
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       Tuple    ,  a    :       str    ,  a    :       Union[str, Any]=13    ,  a    :       List[str]=7    ,  a    :       Tuple=True    ,  a    :       List[Any]=True    ,  a    :       str=True    ,  a    :       Union[str, Any]=True    ,  a    :       List[Any]=99    ,  a    :       Any=32    ,  a    :       Tuple=5    ,  a    :       Union[str, Any]=4    ,  a    :       Union[str, Any]=37    ,  a    :       int="gelu"    ,  a    :       Any=0.1    ,  a    :       List[str]=0.1    ,  a    :       Union[str, Any]=512    ,  a    :       str=16    ,  a    :       Tuple=2    ,  a    :       List[Any]=0.02    ,  a    :       int=4    ,  )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						seq_length
     lowercase__			    =						is_training
     lowercase__			    =						use_attention_mask
     lowercase__			    =						use_token_type_ids
     lowercase__			    =						use_labels
     lowercase__			    =						vocab_size
     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__			    =						max_position_embeddings
     lowercase__			    =						type_vocab_size
     lowercase__			    =						type_sequence_label_size
     lowercase__			    =						initializer_range
     lowercase__			    =						num_choices


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





     """simple docstring"""







     lowercase__			    =						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )

     lowercase__			    =						None
     if self.use_attention_mask:
       lowercase__			    =						random_attention_mask([self.batch_size, self.seq_length] )

     lowercase__			    =						None
     if self.use_token_type_ids:
       lowercase__			    =						ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size )

     lowercase__			    =						RobertaPreLayerNormConfig(
         vocab_size=self.vocab_size    ,  hidden_size=self.hidden_size    ,  num_hidden_layers=self.num_hidden_layers    ,  num_attention_heads=self.num_attention_heads    ,  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    ,  max_position_embeddings=self.max_position_embeddings    ,  type_vocab_size=self.type_vocab_size    ,  is_decoder=_lowercase    ,  initializer_range=self.initializer_range    ,  )

     return config, input_ids, token_type_ids, attention_mask


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





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()
     lowercase__			    =						config_and_inputs
     lowercase__			    =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
     return config, inputs_dict


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





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()
     lowercase__			    =						config_and_inputs

     lowercase__			    =						True
     lowercase__			    =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
     lowercase__			    =						ids_tensor([self.batch_size, self.seq_length]    ,  vocab_size=2 )

     return (
         config,
         input_ids,
         token_type_ids,
         encoder_hidden_states,
         encoder_attention_mask,
     )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        SCREAMING_SNAKE_CASE     (SCREAMING_SNAKE_CASE__    ,			unittest.TestCase   ):
   _UpperCamelCase					:	str				      = True

   _UpperCamelCase					:	Optional[int]				      = (
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


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





     """simple docstring"""







     lowercase__			    =						FlaxRobertaPreLayerNormModelTester(self )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Tuple:





     """simple docstring"""







     for model_class_name in self.all_model_classes:
       lowercase__			    =						model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,  from_pt=_lowercase )
       lowercase__			    =						model(np.ones((1, 1) ) )
       self.assertIsNotNone(_lowercase )






@require_flax
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


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





     """simple docstring"""







     lowercase__			    =						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,  from_pt=_lowercase )

     lowercase__			    =						np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,  dtype=jnp.intaa )
     lowercase__			    =						model(_lowercase )[0]
     lowercase__			    =						[1, 11, 50_265]
     self.assertEqual(list(output.shape )    ,  _lowercase )
     # compare the actual values for a slice.
     lowercase__			    =						np.array(
         [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]    ,  dtype=np.floataa )
     self.assertTrue(np.allclose(output[:, :3, :3]    ,  _lowercase    ,  atol=1E-4 ) )


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





     """simple docstring"""







     lowercase__			    =						FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,  from_pt=_lowercase )

     lowercase__			    =						np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,  dtype=jnp.intaa )
     lowercase__			    =						model(_lowercase )[0]
     # compare the actual values for a slice.
     lowercase__			    =						np.array(
         [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]    ,  dtype=np.floataa )
     self.assertTrue(np.allclose(output[:, :3, :3]    ,  _lowercase    ,  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)


