"""simple docstring"""
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  a__:					Any      							=     [
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(lowerCAmelCase__		,					lowerCAmelCase__       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Dict:
  a__							,   a__:					Tuple      							=     emb.weight.shape
  a__:					Optional[int]      							=     nn.Linear(lowerCAmelCase__		,					lowerCAmelCase__		,					bias=lowerCAmelCase__       )
  a__:					Optional[Any]      							=     emb.weight.data
  return lin_layer







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[str]:
  a__:					List[str]      							=     torch.load(lowerCAmelCase__		,					map_location='cpu'       )
  a__:					int      							=     Namespace(**checkpoint['cfg']['model']       )
  a__:					List[str]      							=     checkpoint['model']
  remove_ignore_keys_(lowerCAmelCase__       )
  a__:					Dict      							=     state_dict['decoder.embed_tokens.weight'].shape[0]

  a__:					Union[str, Any]      							=     {key.replace('decoder'		,					'model'       ): val for key, val in state_dict.items()}

  a__:					List[Any]      							=     XGLMConfig(
      vocab_size=lowerCAmelCase__		,					max_position_embeddings=args.max_target_positions		,					num_layers=args.decoder_layers		,					attention_heads=args.decoder_attention_heads		,					ffn_dim=args.decoder_ffn_embed_dim		,					d_model=args.decoder_embed_dim		,					layerdrop=args.decoder_layerdrop		,					dropout=args.dropout		,					attention_dropout=args.attention_dropout		,					activation_dropout=args.activation_dropout		,					activation_function='gelu'		,					scale_embedding=not args.no_scale_embedding		,					tie_word_embeddings=args.share_decoder_input_output_embed		,					)

  a__:					Union[str, Any]      							=     XGLMForCausalLM(lowerCAmelCase__       )
  a__:					Tuple      							=     model.load_state_dict(lowerCAmelCase__		,					strict=lowerCAmelCase__       )
  print(lowerCAmelCase__       )
  a__:					str      							=     make_linear_from_emb(model.model.embed_tokens       )

  return model


if __name__ == "__main__":
      lowercase__ 	=     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.')
      lowercase__ 	=     parser.parse_args()
      lowercase__ 	=     convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
      model.save_pretrained(args.pytorch_dump_folder_path)



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__:					Tuple      							=     {}

  a__:					Tuple      							=     job['started_at']
  a__:					int      							=     job['completed_at']

  a__:					Any      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )

  a__:					str      							=     round((end_datetime - start_datetime).total_seconds() / 60.0       )

  a__:					Any      							=     start
  a__:					Dict      							=     end
  a__:					Optional[int]      							=     duration_in_min

  return job_info







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[int]:
  a__:					Tuple      							=     None
  if token is not None:
    a__:					List[str]      							=     {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:					int      							=     F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
  a__:					Union[str, Any]      							=     requests.get(_SCREAMING_SNAKE_CASE		,					headers=_SCREAMING_SNAKE_CASE       ).json()
  a__:					str      							=     {}

  try:
    job_time.update({job['name']: extract_time_from_single_job(_SCREAMING_SNAKE_CASE       ) for job in result['jobs']}       )
    a__:					Dict      							=     math.ceil((result['total_count'] - 100) / 100       )

    for i in range(_SCREAMING_SNAKE_CASE       ):
      a__:					str      							=     requests.get(url + F'&page={i + 2}'		,					headers=_SCREAMING_SNAKE_CASE       ).json()
      job_time.update({job['name']: extract_time_from_single_job(_SCREAMING_SNAKE_CASE       ) for job in result['jobs']}       )

    return job_time
  except Exception:
    print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'       )

  return {}


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
 lowercase__ 	=     parser.parse_args()

 lowercase__ 	=     get_job_time(args.workflow_run_id)
 lowercase__ 	=     dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

 for k, v in job_time.items():
  print(f"{k}: {v['duration']}")


