"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers.models.distilbert.modeling_tf_distilbert import (
         TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFDistilBertForMaskedLM,
         TFDistilBertForMultipleChoice,
         TFDistilBertForQuestionAnswering,
         TFDistilBertForSequenceClassification,
         TFDistilBertForTokenClassification,
         TFDistilBertModel,
     )
class 		__A							:






     def __init__(	self       ,  a__       ,  ):
       _lowerCAmelCase		: Any						    =  parent
       _lowerCAmelCase		: str						    =  13
       _lowerCAmelCase		: Dict						    =  7
       _lowerCAmelCase		: Optional[int]						    =  True
       _lowerCAmelCase		: Optional[Any]						    =  True
       _lowerCAmelCase		: Dict						    =  False
       _lowerCAmelCase		: Optional[Any]						    =  True
       _lowerCAmelCase		: Union[str, Any]						    =  99
       _lowerCAmelCase		: List[str]						    =  32
       _lowerCAmelCase		: List[str]						    =  2
       _lowerCAmelCase		: str						    =  4
       _lowerCAmelCase		: Tuple						    =  37
       _lowerCAmelCase		: Dict						    =  """gelu"""
       _lowerCAmelCase		: Union[str, Any]						    =  0.1
       _lowerCAmelCase		: Dict						    =  0.1
       _lowerCAmelCase		: Tuple						    =  512
       _lowerCAmelCase		: Optional[int]						    =  16
       _lowerCAmelCase		: Optional[Any]						    =  2
       _lowerCAmelCase		: Optional[int]						    =  0.0_2
       _lowerCAmelCase		: int						    =  3
       _lowerCAmelCase		: Tuple						    =  4
       _lowerCAmelCase		: Union[str, Any]						    =  None






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

       _lowerCAmelCase		: Any						    =  None
       if self.use_input_mask:
         _lowerCAmelCase		: Tuple						    =  random_attention_mask([self.batch_size, self.seq_length]						)

       _lowerCAmelCase		: List[str]						    =  None
       _lowerCAmelCase		: int						    =  None
       _lowerCAmelCase		: List[Any]						    =  None
       if self.use_labels:
         _lowerCAmelCase		: Optional[int]						    =  ids_tensor([self.batch_size]       ,  self.type_sequence_label_size						)
         _lowerCAmelCase		: List[str]						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels						)
         _lowerCAmelCase		: int						    =  ids_tensor([self.batch_size]       ,  self.num_choices						)

       _lowerCAmelCase		: Optional[int]						    =  DistilBertConfig(
           vocab_size=self.vocab_size       ,  dim=self.hidden_size       ,  n_layers=self.num_hidden_layers       ,  n_heads=self.num_attention_heads       ,  hidden_dim=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  dropout=self.hidden_dropout_prob       ,  attention_dropout=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  initializer_range=self.initializer_range       ,  )

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Tuple						    =  TFDistilBertModel(config=a__						)
       _lowerCAmelCase		: Dict						    =  {"""input_ids""": input_ids, """attention_mask""": input_mask}

       _lowerCAmelCase		: List[Any]						    =  model(a__						)

       _lowerCAmelCase		: Optional[int]						    =  [input_ids, input_mask]

       _lowerCAmelCase		: Tuple						    =  model(a__						)

       self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[str]						    =  TFDistilBertForMaskedLM(config=a__						)
       _lowerCAmelCase		: List[Any]						    =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
       _lowerCAmelCase		: List[str]						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[str]						    =  TFDistilBertForQuestionAnswering(config=a__						)
       _lowerCAmelCase		: Tuple						    =  {
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
       }
       _lowerCAmelCase		: Optional[Any]						    =  model(a__						)
       self.parent.assertEqual(result.start_logits.shape       ,  (self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape       ,  (self.batch_size, self.seq_length)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Any						    =  self.num_labels
       _lowerCAmelCase		: Dict						    =  TFDistilBertForSequenceClassification(a__						)
       _lowerCAmelCase		: str						    =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
       _lowerCAmelCase		: int						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Optional[Any]						    =  self.num_choices
       _lowerCAmelCase		: Optional[Any]						    =  TFDistilBertForMultipleChoice(a__						)
       _lowerCAmelCase		: str						    =  tf.tile(tf.expand_dims(a__       ,  1						)       ,  (1, self.num_choices, 1)						)
       _lowerCAmelCase		: Tuple						    =  tf.tile(tf.expand_dims(a__       ,  1						)       ,  (1, self.num_choices, 1)						)
       _lowerCAmelCase		: Optional[int]						    =  {
           """input_ids""": multiple_choice_inputs_ids,
           """attention_mask""": multiple_choice_input_mask,
       }
       _lowerCAmelCase		: Union[str, Any]						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_choices)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  self.num_labels
       _lowerCAmelCase		: int						    =  TFDistilBertForTokenClassification(a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
       _lowerCAmelCase		: str						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  self.prepare_config_and_inputs()
       (_lowerCAmelCase)		: List[str]						    =  config_and_inputs
       _lowerCAmelCase		: Optional[int]						    =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_tf
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Tuple	      =	(
         (
             TFDistilBertModel,
             TFDistilBertForMaskedLM,
             TFDistilBertForQuestionAnswering,
             TFDistilBertForSequenceClassification,
             TFDistilBertForTokenClassification,
             TFDistilBertForMultipleChoice,
         )
         if is_tf_available()
         else None
     )
     _UpperCamelCase							:    Tuple	      =	(
         {
             "feature-extraction": TFDistilBertModel,
             "fill-mask": TFDistilBertForMaskedLM,
             "question-answering": TFDistilBertForQuestionAnswering,
             "text-classification": TFDistilBertForSequenceClassification,
             "token-classification": TFDistilBertForTokenClassification,
             "zero-shot": TFDistilBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     _UpperCamelCase							:    Any	      =	False
     _UpperCamelCase							:    List[str]	      =	False






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  TFDistilBertModelTester(self						)
       _lowerCAmelCase		: List[str]						    =  ConfigTester(self       ,  config_class=a__       ,  dim=37						)






     def 					__A						(	self						):
       self.config_tester.run_common_tests()






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_model(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_masked_lm(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_question_answering(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_sequence_classification(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_multiple_choice(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_token_classification(*a__						)






     @slow
     def 					__A						(	self						):
       for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]						):
         _lowerCAmelCase		: int						    =  TFDistilBertModel.from_pretrained(a__						)
         self.assertIsNotNone(a__						)







@require_tf
class 		__A							(				unittest.TestCase	):






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""						)
       _lowerCAmelCase		: Dict						    =  tf.constant([[0, 1, 2, 3, 4, 5]]						)
       _lowerCAmelCase		: Optional[Any]						    =  model(a__						)[0]

       _lowerCAmelCase		: Dict						    =  [1, 6, 768]
       self.assertEqual(output.shape       ,  a__						)

       _lowerCAmelCase		: Optional[Any]						    =  tf.constant(
           [
               [
                   [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                   [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                   [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
               ]
           ]						)
       tf.debugging.assert_near(output[:, :3, :3]       ,  a__       ,  atol=1e-4						)


"""simple docstring"""




import argparse
import json
import subprocess





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Any			) ->   List[Any]:
  _lowerCAmelCase		: Tuple						    =  []

  _lowerCAmelCase		: Optional[int]						    =  (
      f"curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""
      """ https://api.github.com/repos/huggingface/transformers/actions/runners"""
  )
  _lowerCAmelCase		: List[str]						    =  subprocess.run(_lowerCamelCase    ,shell=_lowerCamelCase    ,stdout=subprocess.PIPE			)
  _lowerCAmelCase		: int						    =  output.stdout.decode("""utf-8"""			)
  _lowerCAmelCase		: Tuple						    =  json.loads(_lowerCamelCase			)

  _lowerCAmelCase		: int						    =  status["""runners"""]
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_lowerCamelCase			)

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

  if len(_lowerCamelCase			) > 0:
    _lowerCAmelCase		: int						    =  """\n""".join([x["""name"""] for x in offline_runners]			)
    raise ValueError(f"The following runners are offline:\n{failed}"			)


if __name__ == "__main__":





     def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   Optional[int]:
       return values.split(""","""			)

     _a   :	int 					=     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.'
     )
     _a   :	Tuple 					=     parser.parse_args()






     get_runner_status(args.target_runners, args.token)




