'''simple docstring'''






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Optional[Any]   ,    _lowercase    :				Optional[Any]   ,    _lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Tuple:
  # Load configuration defined in the metadata file
  with open(_lowercase     ) as metadata_file:
    __UpperCAmelCase:					Tuple		= json.load(_lowercase     )
  __UpperCAmelCase:					str		= LukeConfig(use_entity_aware_attention=_lowercase   ,    **metadata["""model_config"""]     )

  # Load in the weights from the checkpoint_path
  __UpperCAmelCase:					Optional[Any]		= torch.load(_lowercase   ,    map_location="""cpu"""     )

  # Load the entity vocab file
  __UpperCAmelCase:					Any		= load_entity_vocab(_lowercase     )

  __UpperCAmelCase:					str		= RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]     )

  # Add special tokens to the token vocabulary for downstream tasks
  __UpperCAmelCase:					Optional[Any]		= AddedToken("""<ent>"""   ,    lstrip=_lowercase   ,    rstrip=_lowercase     )
  __UpperCAmelCase:					List[Any]		= AddedToken("""<ent2>"""   ,    lstrip=_lowercase   ,    rstrip=_lowercase     )
  tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}     )
  config.vocab_size += 2

  print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''     )
  tokenizer.save_pretrained(_lowercase     )
  with open(os.path.join(_lowercase   ,    LukeTokenizer.vocab_files_names["""entity_vocab_file"""]     )   ,    """w"""     ) as f:
    json.dump(_lowercase   ,    _lowercase     )

  __UpperCAmelCase:					Tuple		= LukeTokenizer.from_pretrained(_lowercase     )

  # Initialize the embeddings of the special tokens
  __UpperCAmelCase:					Tuple		= state_dict["""embeddings.word_embeddings.weight"""]
  __UpperCAmelCase:					int		= word_emb[tokenizer.convert_tokens_to_ids(["""@"""]     )[0]].unsqueeze(0     )
  __UpperCAmelCase:					List[str]		= word_emb[tokenizer.convert_tokens_to_ids(["""#"""]     )[0]].unsqueeze(0     )
  __UpperCAmelCase:					List[str]		= torch.cat([word_emb, ent_emb, enta_emb]     )

  # Initialize the query layers of the entity-aware self-attention mechanism
  for layer_index in range(config.num_hidden_layers     ):
    for matrix_name in ["query.weight", "query.bias"]:
      __UpperCAmelCase:					Any		= F'''encoder.layer.{layer_index}.attention.self.'''
      __UpperCAmelCase:					Tuple		= state_dict[prefix + matrix_name]
      __UpperCAmelCase:					Optional[Any]		= state_dict[prefix + matrix_name]
      __UpperCAmelCase:					str		= state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
  __UpperCAmelCase:					Tuple		= state_dict["""entity_embeddings.entity_embeddings.weight"""]
  __UpperCAmelCase:					Optional[int]		= entity_emb[entity_vocab["""[MASK]"""]]

  __UpperCAmelCase:					List[str]		= LukeModel(config=_lowercase     ).eval()

  __UpperCAmelCase, __UpperCAmelCase:					List[str]		= model.load_state_dict(_lowercase   ,    strict=_lowercase     )
  if not (len(_lowercase     ) == 1 and missing_keys[0] == "embeddings.position_ids"):
    raise ValueError(F'''Missing keys {', '.join(_lowercase     )}. Expected only missing embeddings.position_ids'''     )
  if not (all(key.startswith("""entity_predictions"""     ) or key.startswith("""lm_head"""     ) for key in unexpected_keys     )):
    raise ValueError(
        """Unexpected keys"""
        F''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'     ) or key.startswith('lm_head'     ))]     )}'''     )

  # Check outputs
  __UpperCAmelCase:					List[Any]		= LukeTokenizer.from_pretrained(_lowercase   ,    task="""entity_classification"""     )

  __UpperCAmelCase:					Optional[Any]		= (
      """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
      """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
  )
  __UpperCAmelCase:					List[Any]		= (3_9, 4_2)
  __UpperCAmelCase:					int		= tokenizer(_lowercase   ,    entity_spans=[span]   ,    add_prefix_space=_lowercase   ,    return_tensors="""pt"""     )

  __UpperCAmelCase:					Optional[Any]		= model(**_lowercase     )

  # Verify word hidden states
  if model_size == "large":
    __UpperCAmelCase:					Tuple		= torch.Size((1, 4_2, 1_0_2_4)     )
    __UpperCAmelCase:					List[Any]		= torch.tensor(
        [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]     )
  else:  # base
    __UpperCAmelCase:					str		= torch.Size((1, 4_2, 7_6_8)     )
    __UpperCAmelCase:					List[Any]		= torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]     )

  if not (outputs.last_hidden_state.shape == expected_shape):
    raise ValueError(
        F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''     )
  if not torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,    _lowercase   ,    atol=1E-4     ):
    raise ValueError

  # Verify entity hidden states
  if model_size == "large":
    __UpperCAmelCase:					int		= torch.Size((1, 1, 1_0_2_4)     )
    __UpperCAmelCase:					Any		= torch.tensor([[0.04_66, -0.01_06, -0.01_79]]     )
  else:  # base
    __UpperCAmelCase:					List[str]		= torch.Size((1, 1, 7_6_8)     )
    __UpperCAmelCase:					Dict		= torch.tensor([[0.14_57, 0.10_44, 0.01_74]]     )

  if not (outputs.entity_last_hidden_state.shape != expected_shape):
    raise ValueError(
        F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
        F''' {expected_shape}'''     )
  if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]   ,    _lowercase   ,    atol=1E-4     ):
    raise ValueError

  # Finally, save our PyTorch model and tokenizer
  print("""Saving PyTorch model to {}""".format(_lowercase     )     )
  model.save_pretrained(_lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	List[str]:
  __UpperCAmelCase:					str		= {}
  with open(_lowercase   ,    """r"""   ,    encoding="""utf-8"""     ) as f:
    for index, line in enumerate(_lowercase     ):
      __UpperCAmelCase, __UpperCAmelCase:					Tuple		= line.rstrip().split("""\t"""     )
      __UpperCAmelCase:					int		= index

  return entity_vocab


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
    parser.add_argument(
        '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
    )
    parser.add_argument(
        '--entity_vocab_path',
        default=None,
        type=str,
        help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
    )
    parser.add_argument(
        '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
    )
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )
'''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