'''simple docstring'''






import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE_	   =			logging.getLogger(__name__)


SCREAMING_SNAKE_CASE_	   =			'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE_	   =			namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)






def  UpperCamelCase__					(					_lowercase    :				Optional[Any]   ,    _lowercase    :				Tuple     )							->	Dict:
  __UpperCAmelCase:					str		= BertAbsConfig(
      temp_dir="""."""   ,    finetune_bert=_lowercase   ,    large=_lowercase   ,    share_emb=_lowercase   ,    use_bert_emb=_lowercase   ,    encoder="""bert"""   ,    max_pos=5_1_2   ,    enc_layers=6   ,    enc_hidden_size=5_1_2   ,    enc_heads=8   ,    enc_ff_size=5_1_2   ,    enc_dropout=0.2   ,    dec_layers=6   ,    dec_hidden_size=7_6_8   ,    dec_heads=8   ,    dec_ff_size=2_0_4_8   ,    dec_dropout=0.2   ,    )
  __UpperCAmelCase:					str		= torch.load(_lowercase   ,    lambda _lowercase   ,    _lowercase     : storage     )
  __UpperCAmelCase:					Tuple		= AbsSummarizer(_lowercase   ,    torch.device("""cpu"""     )   ,    _lowercase     )
  original.eval()

  __UpperCAmelCase:					Tuple		= BertAbsSummarizer(_lowercase   ,    torch.device("""cpu"""     )     )
  new_model.eval()

  # -------------------
  # Convert the weights
  # -------------------

  logging.info("""convert the model"""     )
  new_model.bert.load_state_dict(original.bert.state_dict()     )
  new_model.decoder.load_state_dict(original.decoder.state_dict()     )
  new_model.generator.load_state_dict(original.generator.state_dict()     )

  # ----------------------------------
  # Make sure the outpus are identical
  # ----------------------------------

  logging.info("""Make sure that the models' outputs are identical"""     )
  __UpperCAmelCase:					Dict		= BertTokenizer.from_pretrained("""bert-base-uncased"""     )

  # prepare the model inputs
  __UpperCAmelCase:					Any		= tokenizer.encode("""This is sample éàalj'-."""     )
  encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_lowercase     ))     )
  __UpperCAmelCase:					List[str]		= torch.tensor(_lowercase     ).unsqueeze(0     )
  __UpperCAmelCase:					str		= tokenizer.encode("""This is sample 3 éàalj'-."""     )
  decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_lowercase     ))     )
  __UpperCAmelCase:					int		= torch.tensor(_lowercase     ).unsqueeze(0     )

  # failsafe to make sure the weights reset does not affect the
  # loaded weights.
  assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight     )     ) == 0

  # forward pass
  __UpperCAmelCase:					Any		= encoder_input_ids
  __UpperCAmelCase:					Any		= decoder_input_ids
  __UpperCAmelCase:					int		= None
  __UpperCAmelCase:					Any		= None
  __UpperCAmelCase:					Dict		= None
  __UpperCAmelCase:					Dict		= None
  __UpperCAmelCase:					List[str]		= None

  # The original model does not apply the geneator layer immediatly but rather in
  # the beam search (where it combines softmax + linear layer). Since we already
  # apply the softmax in our generation process we only apply the linear layer here.
  # We make sure that the outputs of the full stack are identical
  __UpperCAmelCase:					Dict		= original(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     )[0]
  __UpperCAmelCase:					Any		= original.generator(_lowercase     )

  __UpperCAmelCase:					str		= new_model(
      _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     )[0]
  __UpperCAmelCase:					Tuple		= new_model.generator(_lowercase     )

  __UpperCAmelCase:					str		= torch.max(torch.abs(output_converted_model - output_original_model     )     ).item()
  print("""Maximum absolute difference beween weights: {:.2f}""".format(_lowercase     )     )
  __UpperCAmelCase:					Tuple		= torch.max(torch.abs(output_converted_generator - output_original_generator     )     ).item()
  print("""Maximum absolute difference beween weights: {:.2f}""".format(_lowercase     )     )

  __UpperCAmelCase:					Any		= torch.allclose(_lowercase   ,    _lowercase   ,    atol=1E-3     )
  if are_identical:
    logging.info("""all weights are equal up to 1e-3"""     )
  else:
    raise ValueError("""the weights are different. The new model is likely different from the original one."""     )

  # The model has been saved with torch.save(model) and this is bound to the exact
  # directory structure. We save the state_dict instead.
  logging.info("""saving the model's state dictionary"""     )
  torch.save(
      new_model.state_dict()   ,    """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    parser.add_argument(
        '--bertabs_checkpoint_path',
        default=None,
        type=str,
        required=True,
        help='Path the official PyTorch dump.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the output PyTorch model.',
    )
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()

    convert_bertabs_checkpoints(
        args.bertabs_checkpoint_path,
        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  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]=7     )							->	Union[str, Any]:
  __UpperCAmelCase:					Optional[int]		= None
  if token is not None:
    __UpperCAmelCase:					List[Any]		= {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}

  # The id of a workflow (not of a workflow run)
  __UpperCAmelCase:					List[str]		= """636036"""

  __UpperCAmelCase:					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}'''

  __UpperCAmelCase:					List[Any]		= requests.get(_lowercase   ,    headers=_lowercase     ).json()

  return result["workflow_runs"]






def  UpperCamelCase__					(					_lowercase    :				Optional[int]     )							->	List[str]:
  __UpperCAmelCase:					str		= get_daily_ci_runs(_lowercase     )
  __UpperCAmelCase:					str		= None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      __UpperCAmelCase:					Optional[int]		= workflow_run["""id"""]
      break

  return workflow_run_id






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Dict:
  __UpperCAmelCase:					List[Any]		= get_last_daily_ci_runs(_lowercase     )
  if workflow_run_id is not None:
    __UpperCAmelCase:					List[Any]		= get_artifacts_links(worflow_run_id=_lowercase   ,    token=_lowercase     )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        __UpperCAmelCase:					str		= artifacts_links[artifact_name]
        download_artifact(
            artifact_name=_lowercase   ,    artifact_url=_lowercase   ,    output_dir=_lowercase   ,    token=_lowercase     )







def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				Tuple   ,    _lowercase    :				Tuple     )							->	int:
  get_last_daily_ci_artifacts(_lowercase   ,    _lowercase   ,    _lowercase     )

  __UpperCAmelCase:					int		= {}
  for artifact_name in artifact_names:
    __UpperCAmelCase:					Optional[Any]		= os.path.join(_lowercase   ,    F'''{artifact_name}.zip'''     )
    if os.path.isfile(_lowercase     ):
      __UpperCAmelCase:					Any		= {}
      with zipfile.ZipFile(_lowercase     ) as z:
        for filename in z.namelist():
          if not os.path.isdir(_lowercase     ):
            # read the file
            with z.open(_lowercase     ) as f:
              __UpperCAmelCase:					Dict		= f.read().decode("""UTF-8"""     )

  return results