"""simple docstring"""




import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

     from transformers import MaskFormerSwinBackbone
     from transformers.models.maskformer import MaskFormerSwinModel
class 		__A							:






     def __init__(	self       ,  a__       ,  a__=13       ,  a__=32       ,  a__=2       ,  a__=3       ,  a__=16       ,  a__=[1, 2, 1]       ,  a__=[2, 2, 4]       ,  a__=2       ,  a__=2.0       ,  a__=True       ,  a__=0.0       ,  a__=0.0       ,  a__=0.1       ,  a__="gelu"       ,  a__=False       ,  a__=True       ,  a__=0.0_2       ,  a__=1e-5       ,  a__=True       ,  a__=None       ,  a__=True       ,  a__=10       ,  a__=8       ,  a__=["stage1", "stage2", "stage3"]       ,  a__=[1, 2, 3]       ,  ):
       _lowerCAmelCase		: List[Any]						    =  parent
       _lowerCAmelCase		: Dict						    =  batch_size
       _lowerCAmelCase		: Any						    =  image_size
       _lowerCAmelCase		: Optional[int]						    =  patch_size
       _lowerCAmelCase		: Any						    =  num_channels
       _lowerCAmelCase		: List[str]						    =  embed_dim
       _lowerCAmelCase		: str						    =  depths
       _lowerCAmelCase		: Tuple						    =  num_heads
       _lowerCAmelCase		: Dict						    =  window_size
       _lowerCAmelCase		: Optional[Any]						    =  mlp_ratio
       _lowerCAmelCase		: Tuple						    =  qkv_bias
       _lowerCAmelCase		: Tuple						    =  hidden_dropout_prob
       _lowerCAmelCase		: List[str]						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: Any						    =  drop_path_rate
       _lowerCAmelCase		: List[Any]						    =  hidden_act
       _lowerCAmelCase		: List[Any]						    =  use_absolute_embeddings
       _lowerCAmelCase		: Tuple						    =  patch_norm
       _lowerCAmelCase		: Optional[Any]						    =  layer_norm_eps
       _lowerCAmelCase		: List[str]						    =  initializer_range
       _lowerCAmelCase		: Optional[int]						    =  is_training
       _lowerCAmelCase		: str						    =  scope
       _lowerCAmelCase		: List[Any]						    =  use_labels
       _lowerCAmelCase		: List[str]						    =  type_sequence_label_size
       _lowerCAmelCase		: List[Any]						    =  encoder_stride
       _lowerCAmelCase		: Any						    =  out_features
       _lowerCAmelCase		: int						    =  out_indices






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       _lowerCAmelCase		: Tuple						    =  None
       if self.use_labels:
         _lowerCAmelCase		: Optional[int]						    =  ids_tensor([self.batch_size]       ,  self.type_sequence_label_size						)

       _lowerCAmelCase		: Optional[int]						    =  self.get_config()

       return config, pixel_values, labels






     def 					__A						(	self						):
       return MaskFormerSwinConfig(
           image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  embed_dim=self.embed_dim       ,  depths=self.depths       ,  num_heads=self.num_heads       ,  window_size=self.window_size       ,  mlp_ratio=self.mlp_ratio       ,  qkv_bias=self.qkv_bias       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  drop_path_rate=self.drop_path_rate       ,  hidden_act=self.hidden_act       ,  use_absolute_embeddings=self.use_absolute_embeddings       ,  path_norm=self.patch_norm       ,  layer_norm_eps=self.layer_norm_eps       ,  initializer_range=self.initializer_range       ,  encoder_stride=self.encoder_stride       ,  out_features=self.out_features       ,  out_indices=self.out_indices       ,  )






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: str						    =  MaskFormerSwinModel(config=a__						)
       model.to(a__						)
       model.eval()
       _lowerCAmelCase		: Union[str, Any]						    =  model(a__						)

       _lowerCAmelCase		: Dict						    =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
       _lowerCAmelCase		: Dict						    =  int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

       self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, expected_seq_len, expected_dim)						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  MaskFormerSwinBackbone(config=a__						)
       model.to(a__						)
       model.eval()
       _lowerCAmelCase		: int						    =  model(a__						)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps						)       ,  len(config.out_features						)						)
       self.parent.assertListEqual(list(result.feature_maps[0].shape						)       ,  [13, 16, 16, 16]						)

       # verify channels
       self.parent.assertEqual(len(model.channels						)       ,  len(config.out_features						)						)
       self.parent.assertListEqual(model.channels       ,  [16, 32, 64]						)

       # verify ValueError
       with self.parent.assertRaises(a__						):
         _lowerCAmelCase		: int						    =  ["""stem"""]
         _lowerCAmelCase		: Union[str, Any]						    =  MaskFormerSwinBackbone(config=a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  self.prepare_config_and_inputs()
       _lowerCAmelCase		: Any						    =  config_and_inputs
       _lowerCAmelCase		: Optional[int]						    =  {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    List[str]	      =	(
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:    List[Any]	      =	{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
     _UpperCamelCase							:    Optional[int]	      =	False
     _UpperCamelCase							:    Union[str, Any]	      =	False
     _UpperCamelCase							:    List[str]	      =	False
     _UpperCamelCase							:    Tuple	      =	False
     _UpperCamelCase							:    Tuple	      =	False






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  MaskFormerSwinModelTester(self						)
       _lowerCAmelCase		: List[str]						    =  ConfigTester(self       ,  config_class=a__       ,  embed_dim=37						)






     @require_torch_multi_gpu
     @unittest.skip(
         reason=(
             """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
             """ `nn.DataParallel`"""
         )						)
     def 					__A						(	self						):
       pass






     def 					__A						(	self						):
       self.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()






     def 					__A						(	self						):
       return






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*a__						)






     @unittest.skip("""Swin does not use inputs_embeds"""						)
     def 					__A						(	self						):
       pass






     @unittest.skip("""Swin does not support feedforward chunking"""						)
     def 					__A						(	self						):
       pass






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase		: str						    =  model_class(a__						)
         self.assertIsInstance(model.get_input_embeddings()       ,  (nn.Module)						)
         _lowerCAmelCase		: Tuple						    =  model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(a__       ,  nn.Linear						)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase		: Union[str, Any]						    =  ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]       ,  a__						)






     @unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""						)
     def 					__A						(	self						):
       pass






     @unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""						)
     def 					__A						(	self						):
       pass






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Dict						    =  model_class(a__						)
       model.to(a__						)
       model.eval()

       with torch.no_grad():
         _lowerCAmelCase		: Optional[int]						    =  model(**self._prepare_for_class(a__       ,  a__						)						)

       _lowerCAmelCase		: Union[str, Any]						    =  outputs.hidden_states

       _lowerCAmelCase		: Any						    =  getattr(
           self.model_tester       ,  """expected_num_hidden_layers"""       ,  len(self.model_tester.depths						) + 1						)
       self.assertEqual(len(a__						)       ,  a__						)

       # Swin has a different seq_length
       _lowerCAmelCase		: int						    =  (
           config.patch_size
           if isinstance(config.patch_size       ,  collections.abc.Iterable						)
           else (config.patch_size, config.patch_size)
       )

       _lowerCAmelCase		: List[Any]						    =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]						)       ,  [num_patches, self.model_tester.embed_dim]       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.model_tester.prepare_config_and_inputs_for_common()

       _lowerCAmelCase		: int						    =  (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,  collections.abc.Iterable						)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
         _lowerCAmelCase		: Union[str, Any]						    =  True
         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  a__						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase		: int						    =  True

         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[Any]						    =  self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase		: Tuple						    =  3

       _lowerCAmelCase		: Union[str, Any]						    =  (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size       ,  collections.abc.Iterable						)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       _lowerCAmelCase		: Dict						    =  (
           config.patch_size
           if isinstance(config.patch_size       ,  collections.abc.Iterable						)
           else (config.patch_size, config.patch_size)
       )

       _lowerCAmelCase		: List[str]						    =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       _lowerCAmelCase		: Optional[Any]						    =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
         _lowerCAmelCase		: Union[str, Any]						    =  True
         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  (padded_height, padded_width)						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCAmelCase		: Optional[Any]						    =  True
         self.check_hidden_states_output(a__       ,  a__       ,  a__       ,  (padded_height, padded_width)						)






     @unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""						)
     def 					__A						(	self						):
       pass






     @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""						)
     def 					__A						(	self						):
       pass






     @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""						)
     def 					__A						(	self						):
       pass






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  self.model_tester.prepare_config_and_inputs_for_common()

       def set_nan_tensor_to_zero(a__						):
         _lowerCAmelCase		: List[str]						    =  0
         return t

       def check_equivalence(a__       ,  a__       ,  a__       ,  a__={}						):
         with torch.no_grad():
           _lowerCAmelCase		: Dict						    =  model(**a__       ,  return_dict=a__       ,  **a__						)
           _lowerCAmelCase		: Dict						    =  model(**a__       ,  return_dict=a__       ,  **a__						).to_tuple()

           def recursive_check(a__       ,  a__						):
             if isinstance(a__       ,  (List, Tuple)						):
               for tuple_iterable_value, dict_iterable_value in zip(a__       ,  a__						):
                 recursive_check(a__       ,  a__						)
             elif isinstance(a__       ,  a__						):
               for tuple_iterable_value, dict_iterable_value in zip(
                   tuple_object.values()       ,  dict_object.values()						):
                 recursive_check(a__       ,  a__						)
             elif tuple_object is None:
               return
             else:
               self.assertTrue(
                   torch.allclose(
                       set_nan_tensor_to_zero(a__						)       ,  set_nan_tensor_to_zero(a__						)       ,  atol=1e-5						)       ,  msg=(
                       """Tuple and dict output are not equal. Difference:"""
                       F" {torch.max(torch.abs(tuple_object - dict_object						)						)}. Tuple has `nan`:"
                       F" {torch.isnan(a__						).any()} and `inf`: {torch.isinf(a__						)}. Dict has"
                       F" `nan`: {torch.isnan(a__						).any()} and `inf`: {torch.isinf(a__						)}."
                   )       ,  )

           recursive_check(a__       ,  a__						)

       for model_class in self.all_model_classes:
         _lowerCAmelCase		: List[str]						    =  model_class(a__						)
         model.to(a__						)
         model.eval()

         _lowerCAmelCase		: Any						    =  self._prepare_for_class(a__       ,  a__						)
         _lowerCAmelCase		: Optional[Any]						    =  self._prepare_for_class(a__       ,  a__						)
         check_equivalence(a__       ,  a__       ,  a__						)

         _lowerCAmelCase		: List[Any]						    =  self._prepare_for_class(a__       ,  a__       ,  return_labels=a__						)
         _lowerCAmelCase		: Tuple						    =  self._prepare_for_class(a__       ,  a__       ,  return_labels=a__						)
         check_equivalence(a__       ,  a__       ,  a__						)

         _lowerCAmelCase		: Union[str, Any]						    =  self._prepare_for_class(a__       ,  a__						)
         _lowerCAmelCase		: Dict						    =  self._prepare_for_class(a__       ,  a__						)
         check_equivalence(a__       ,  a__       ,  a__       ,  {"""output_hidden_states""": True}						)

         _lowerCAmelCase		: Union[str, Any]						    =  self._prepare_for_class(a__       ,  a__       ,  return_labels=a__						)
         _lowerCAmelCase		: Any						    =  self._prepare_for_class(a__       ,  a__       ,  return_labels=a__						)
         check_equivalence(a__       ,  a__       ,  a__       ,  {"""output_hidden_states""": True}						)







@require_torch
class 		__A							(				unittest.TestCase					,			SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    Any	      =	(MaskFormerSwinBackbone,) if is_torch_available() else ()
     _UpperCamelCase							:    Optional[Any]	      =	MaskFormerSwinConfig






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  MaskFormerSwinModelTester(self						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase		: Optional[Any]						    =  inputs_dict["""pixel_values"""].shape[0]

       for backbone_class in self.all_model_classes:
         _lowerCAmelCase		: Optional[Any]						    =  backbone_class(a__						)
         backbone.to(a__						)
         backbone.eval()

         _lowerCAmelCase		: Optional[Any]						    =  backbone(**a__						)

         # Test default outputs and verify feature maps
         self.assertIsInstance(outputs.feature_maps       ,  a__						)
         self.assertTrue(len(outputs.feature_maps						) == len(backbone.channels						)						)
         for feature_map, n_channels in zip(outputs.feature_maps       ,  backbone.channels						):
           self.assertTrue(feature_map.shape[:2]       ,  (batch_size, n_channels)						)
         self.assertIsNone(outputs.hidden_states						)
         self.assertIsNone(outputs.attentions						)

         # Test output_hidden_states=True
         _lowerCAmelCase		: str						    =  backbone(**a__       ,  output_hidden_states=a__						)
         self.assertIsNotNone(outputs.hidden_states						)
         self.assertTrue(len(outputs.hidden_states						)       ,  len(backbone.stage_names						)						)
         # We skip the stem layer
         for hidden_states, n_channels in zip(outputs.hidden_states[1:]       ,  backbone.channels						):
           for hidden_state in hidden_states:
             # Hidden states are in the format (batch_size, (height * width), n_channels)
             _lowerCAmelCase		: str						    =  hidden_state.shape
             self.assertTrue((h_batch_size, h_n_channels)       ,  (batch_size, n_channels)						)

            # Test output_attentions=True
         if self.has_attentions:
           _lowerCAmelCase		: Any						    =  backbone(**a__       ,  output_attentions=a__						)
           self.assertIsNotNone(outputs.attentions						)


"""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)




