



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				List[str]			,							_A			:				str			,							_A			:				int			,							_A			:				List[str]    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       TapasConfig.from_json_file(_A    )
						# set absolute/relative position embeddings parameter
						A__							=       reset_position_index_per_cell

						# set remaining parameters of TapasConfig as well as the model based on the task
						if task == "SQA":
												A__							=       TapasForQuestionAnswering(config=_A    )
						elif task == "WTQ":
												# run_task_main.py hparams
												A__							=       4
												A__							=       True
												# hparam_utils.py hparams
												A__							=       0.66_4694
												A__							=       0.20_7951
												A__							=       0.12_1194
												A__							=       True
												A__							=       True
												A__							=       False
												A__							=       0.035_2513

												A__							=       TapasForQuestionAnswering(config=_A    )
						elif task == "WIKISQL_SUPERVISED":
												# run_task_main.py hparams
												A__							=       4
												A__							=       False
												# hparam_utils.py hparams
												A__							=       36.4519
												A__							=       0.90_3421
												A__							=       222.088
												A__							=       True
												A__							=       True
												A__							=       True
												A__							=       0.76_3141

												A__							=       TapasForQuestionAnswering(config=_A    )
						elif task == "TABFACT":
												A__							=       TapasForSequenceClassification(config=_A    )
						elif task == "MLM":
												A__							=       TapasForMaskedLM(config=_A    )
						elif task == "INTERMEDIATE_PRETRAINING":
												A__							=       TapasModel(config=_A    )
						else:
												raise ValueError(f"""Task {task} not supported."""    )

						print(f"""Building PyTorch model from configuration: {config}"""    )
						# Load weights from tf checkpoint
						load_tf_weights_in_tapas(_A			,							_A			,							_A    )

						# Save pytorch-model (weights and configuration)
						print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
						model.save_pretrained(_A    )

						# Save tokenizer files
						print(f"""Save tokenizer files to {pytorch_dump_path}"""    )
						A__							=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,							model_max_length=512    )
						tokenizer.save_pretrained(_A    )

						print("Used relative position embeddings:"			,							model.config.reset_position_index_per_cell    )


if __name__ == "__main__":
						UpperCAmelCase_							:      Union[str, Any]      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
						)
						parser.add_argument(
						    "--reset_position_index_per_cell",
						    default=False,
						    action="store_true",
						    help="Whether to use relative position embeddings or not. Defaults to True.",
						)
						parser.add_argument(
						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
						)
						parser.add_argument(
						    "--tapas_config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained TAPAS model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.task,
						    args.reset_position_index_per_cell,
						    args.tf_checkpoint_path,
						    args.tapas_config_file,
						    args.pytorch_dump_path,
						)










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[str]=7    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       None
						if token is not None:
												A__							=       {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

						# The id of a workflow (not of a workflow run)
						A__							=       "636036"

						A__							=       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__							=       requests.get(_A			,							headers=_A    ).json()

						return result["workflow_runs"]
def  UpperCamelCase						(       _A			:				str    )->       Dict:





						"""simple docstring"""





						A__							=       get_daily_ci_runs(_A    )
						A__							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		A__							=       workflow_run["id"]
																		break

						return workflow_run_id
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       get_last_daily_ci_runs(_A    )
						if workflow_run_id is not None:
												A__							=       get_artifacts_links(worflow_run_id=_A			,							token=_A    )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								A__							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=_A			,							artifact_url=_A			,							output_dir=_A			,							token=_A    )
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Any			,							_A			:				List[Any]    )->       Optional[int]:





						"""simple docstring"""





						get_last_daily_ci_artifacts(_A			,							_A			,							_A    )

						A__							=       {}
						for artifact_name in artifact_names:
												A__							=       os.path.join(_A			,							f"""{artifact_name}.zip"""    )
												if os.path.isfile(_A    ):
																		A__							=       {}
																		with zipfile.ZipFile(_A    ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(_A    ):
																																				# read the file
																																				with z.open(_A    ) as f:
																																										A__							=       f.read().decode("UTF-8"    )

						return results





