
'''simple docstring'''
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       Any:





  """simple docstring"""

  __A		:     str												=		[
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str] )				->       Optional[Any]:





  """simple docstring"""

  __A		:     Optional[Any]												=		emb.weight.shape
  __A		:     Optional[Any]												=		nn.Linear(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     bias=__SCREAMING_SNAKE_CASE )
  __A		:     int												=		emb.weight.data
  return lin_layer







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str] )				->       Any:





  """simple docstring"""

  __A		:     Optional[Any]												=		torch.load(__SCREAMING_SNAKE_CASE			,     map_location="""cpu""" )
  __A		:     Optional[int]												=		Namespace(**checkpoint["""cfg"""]["""model"""] )
  __A		:     int												=		checkpoint['model']
  remove_ignore_keys_(__SCREAMING_SNAKE_CASE )
  __A		:     List[str]												=		state_dict['decoder.embed_tokens.weight'].shape[0]

  __A		:     Any												=		{key.replace("""decoder"""			,     """model""" ): val for key, val in state_dict.items()}

  __A		:     Optional[Any]												=		XGLMConfig(
      vocab_size=__SCREAMING_SNAKE_CASE			,     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		:     Optional[Any]												=		XGLMForCausalLM(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		model.load_state_dict(__SCREAMING_SNAKE_CASE			,     strict=__SCREAMING_SNAKE_CASE )
  print(__SCREAMING_SNAKE_CASE )
  __A		:     Union[str, Any]												=		make_linear_from_emb(model.model.embed_tokens )

  return model


if __name__ == "__main__":
     A__   :		List[str]   		=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.')
     A__   :		List[str]   		=parser.parse_args()
     A__   :		Tuple   		=convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
     model.save_pretrained(args.pytorch_dump_folder_path)





'''simple docstring'''
import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Optional[int]=7 )				->       List[Any]:





  """simple docstring"""

  __A		:     Optional[int]												=		None
  if token is not None:
    __A		:     str												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  # The id of a workflow (not of a workflow run)
  __A		:     Union[str, Any]												=		"""636036"""

  __A		:     Optional[int]												=		F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
  # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
  url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

  __A		:     int												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()

  return result["workflow_runs"]







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Tuple:





  """simple docstring"""

  __A		:     str												=		get_daily_ci_runs(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      __A		:     Dict												=		workflow_run["""id"""]
      break

  return workflow_run_id







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  Tuple )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		get_last_daily_ci_runs(__SCREAMING_SNAKE_CASE )
  if workflow_run_id is not None:
    __A		:     str												=		get_artifacts_links(worflow_run_id=__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        __A		:     List[Any]												=		artifacts_links[artifact_name]
        download_artifact(
            artifact_name=__SCREAMING_SNAKE_CASE			,     artifact_url=__SCREAMING_SNAKE_CASE			,     output_dir=__SCREAMING_SNAKE_CASE			,     token=__SCREAMING_SNAKE_CASE )







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  Optional[Any] )				->       Union[str, Any]:





  """simple docstring"""

  get_last_daily_ci_artifacts(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  __A		:     List[Any]												=		{}
  for artifact_name in artifact_names:
    __A		:     Any												=		os.path.join(__SCREAMING_SNAKE_CASE			,     F"{artifact_name}.zip" )
    if os.path.isfile(__SCREAMING_SNAKE_CASE ):
      __A		:     str												=		{}
      with zipfile.ZipFile(__SCREAMING_SNAKE_CASE ) as z:
        for filename in z.namelist():
          if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
            # read the file
            with z.open(__SCREAMING_SNAKE_CASE ) as f:
              __A		:     Tuple												=		f.read().decode("""UTF-8""" )

  return results



