import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase ,						__UpperCamelCase		)
def       __magic_name__(  __UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				list(s_dict.keys()		)
      for key in keys:
            if "transformer_layers" in key:
                  _lowerCamelCase			    =				s_dict.pop(__UpperCamelCase		)
            elif "subsample" in key:
                  _lowerCamelCase			    =				s_dict.pop(__UpperCamelCase		)
def       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

      _lowerCamelCase			    =				emb.weight.shape
      _lowerCamelCase			    =				nn.Linear(__UpperCamelCase ,						__UpperCamelCase ,						bias=__UpperCamelCase		)
      _lowerCamelCase			    =				emb.weight.data
      return lin_layer
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				torch.load(__UpperCamelCase ,						map_location='''cpu'''		)
      _lowerCamelCase			    =				mam_aaa["""args"""]
      _lowerCamelCase			    =				mam_aaa["""model"""]
      _lowerCamelCase			    =				state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(__UpperCamelCase		)
      rename_keys(__UpperCamelCase		)

      _lowerCamelCase			    =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

      _lowerCamelCase			    =				args.share_decoder_input_output_embed

      _lowerCamelCase			    =				[int(__UpperCamelCase		) for i in args.conv_kernel_sizes.split(''','''		)]
      _lowerCamelCase			    =				SpeechaTextConfig(
          vocab_size=__UpperCamelCase ,						max_source_positions=args.max_source_positions ,						max_target_positions=args.max_target_positions ,						encoder_layers=args.encoder_layers ,						decoder_layers=args.decoder_layers ,						encoder_attention_heads=args.encoder_attention_heads ,						decoder_attention_heads=args.decoder_attention_heads ,						encoder_ffn_dim=args.encoder_ffn_embed_dim ,						decoder_ffn_dim=args.decoder_ffn_embed_dim ,						d_model=args.encoder_embed_dim ,						dropout=args.dropout ,						attention_dropout=args.attention_dropout ,						activation_dropout=args.activation_dropout ,						activation_function='''relu''' ,						num_conv_layers=len(__UpperCamelCase		) ,						conv_channels=args.conv_channels ,						conv_kernel_sizes=__UpperCamelCase ,						input_feat_per_channel=args.input_feat_per_channel ,						input_channels=args.input_channels ,						tie_word_embeddings=__UpperCamelCase ,						num_beams=5 ,						max_length=200 ,						use_cache=__UpperCamelCase ,						decoder_start_token_id=2 ,						early_stopping=__UpperCamelCase ,						)

      _lowerCamelCase			    =				SpeechaTextForConditionalGeneration(__UpperCamelCase		)
      _lowerCamelCase			    =				model.model.load_state_dict(__UpperCamelCase ,						strict=__UpperCamelCase		)
      if len(__UpperCamelCase		) > 0 and not set(__UpperCamelCase		) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
                F' but all the following weights are missing {missing}'		)

      if tie_embeds:
            _lowerCamelCase			    =				make_linear_from_emb(model.model.decoder.embed_tokens		)
      else:
            _lowerCamelCase			    =				lm_head_weights

      model.save_pretrained(__UpperCamelCase		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     snake_case__				= parser.parse_args()
     convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
import argparse
import json
import subprocess
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				[]

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

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

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

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


if __name__ == "__main__":
     def       __magic_name__(  __UpperCAmelCase		)						->    str:



           '''simple docstring'''

           return values.split(''','''		)

     snake_case__				= 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.'
     )
     snake_case__				= parser.parse_args()




     get_runner_status(args.target_runners, args.token)