





"""simple docstring"""





import argparse
import json
import subprocess



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Dict     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							__lowerCamelCase   :   str       =[]

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

							__lowerCamelCase   :   Optional[int]       =status['runners']
							for runner in runners:
														if runner["name"] in target_runners:
																					if runner["status"] == "offline":
																												offline_runners.append(_snake_case )

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

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


if __name__ == "__main__":



							def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				List[str] ):


														'''simple docstring'''







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

							_UpperCamelCase					      =   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.'
							)
							_UpperCamelCase					      =   parser.parse_args()
							get_runner_status(args.target_runners, args.token)






"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      int     							=					[
        '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(_snake_case    ,_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					emb.weight.shape
    UpperCAmelCase__	:      List[str]     							=					nn.Linear(_snake_case    ,_snake_case    ,bias=_snake_case )
    UpperCAmelCase__	:      Any     							=					emb.weight.data
    return lin_layer
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
    UpperCAmelCase__	:      Any     							=					mam_aaa['args'] or mam_aaa['cfg']['model']
    UpperCAmelCase__	:      Optional[int]     							=					mam_aaa['model']
    remove_ignore_keys_(_snake_case )
    UpperCAmelCase__	:      Optional[int]     							=					state_dict['encoder.embed_tokens.weight'].shape[0]

    UpperCAmelCase__	:      List[str]     							=					MaMaaaConfig(
        vocab_size=_snake_case    ,max_position_embeddings=1024    ,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    ,encoder_layerdrop=args.encoder_layerdrop    ,decoder_layerdrop=args.decoder_layerdrop    ,dropout=args.dropout    ,attention_dropout=args.attention_dropout    ,activation_dropout=args.activation_dropout    ,activation_function='relu'    ,)

    UpperCAmelCase__	:      Optional[int]     							=					state_dict['decoder.embed_tokens.weight']
    UpperCAmelCase__	:      Union[str, Any]     							=					MaMaaaForConditionalGeneration(_snake_case )
    model.model.load_state_dict(_snake_case    ,strict=_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    UpperCamelCase__          =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    UpperCamelCase__          =	parser.parse_args()
    UpperCamelCase__          =	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)

