



import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Tuple      =  [
    ("bert.bert", "visual_bert"),
    ("bert.cls", "cls"),
    ("bert.classifier", "cls"),
    ("token_type_embeddings_visual", "visual_token_type_embeddings"),
    ("position_embeddings_visual", "visual_position_embeddings"),
    ("projection", "visual_projection"),
]

UpperCAmelCase_							:      Dict      =  [
    "nlvr2_coco_pre_trained.th",
    "nlvr2_fine_tuned.th",
    "nlvr2_pre_trained.th",
    "vcr_coco_pre_train.th",
    "vcr_fine_tune.th",
    "vcr_pre_train.th",
    "vqa_coco_pre_trained.th",
    "vqa_fine_tuned.th",
    "vqa_pre_trained.th",
]
def  UpperCamelCase						(       _A			:				str    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       torch.load(_A			,							map_location="cpu"    )
						return sd
def  UpperCamelCase						(       _A			:				Any			,							_A			:				Union[str, Any]			,							_A			:				str=rename_keys_prefix    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       OrderedDict()
						A__							=       torch.arange(config.max_position_embeddings    ).expand((1, -1)    )
						# detector_d = OrderedDict()
						for key in d:
												if "detector" in key:
																		# detector_d[key.replace('detector.','')] = d[key]
																		continue
												A__							=       key
												for name_pair in rename_keys_prefix:
																		A__							=       new_key.replace(name_pair[0]			,							name_pair[1]    )
												A__							=       d[key]
												if key == "bert.cls.predictions.decoder.weight":
																		# Old bert code didn't have `decoder.bias`, but was added separately
																		A__							=       new_d["cls.predictions.bias"]
						return new_d
@torch.no_grad()
def  UpperCamelCase						(       _A			:				Dict			,							_A			:				Optional[int]    )->       str:





						"""simple docstring"""





						assert (
						    checkpoint_path.split("/"    )[-1] in ACCEPTABLE_CHECKPOINTS
						), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

						# Get Config
						if "pre" in checkpoint_path:
												A__							=       "pretraining"
												if "vcr" in checkpoint_path:
																		A__							=       {"visual_embedding_dim": 512}
												elif "vqa_advanced" in checkpoint_path:
																		A__							=       {"visual_embedding_dim": 2048}
												elif "vqa" in checkpoint_path:
																		A__							=       {"visual_embedding_dim": 2048}
												elif "nlvr" in checkpoint_path:
																		A__							=       {"visual_embedding_dim": 1024}
												else:
																		raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""    )
						else:
												if "vcr" in checkpoint_path:
																		A__							=       {"visual_embedding_dim": 512}
																		A__							=       "multichoice"
												elif "vqa_advanced" in checkpoint_path:
																		A__							=       {"visual_embedding_dim": 2048}
																		A__							=       "vqa_advanced"
												elif "vqa" in checkpoint_path:
																		A__							=       {"visual_embedding_dim": 2048, "num_labels": 3129}
																		A__							=       "vqa"
												elif "nlvr" in checkpoint_path:
																		A__							=       {
																		    "visual_embedding_dim": 1024,
																		    "num_labels": 2,
																		}
																		A__							=       "nlvr"

						A__							=       VisualBertConfig(**_A    )

						# Load State Dict
						A__							=       load_state_dict(_A    )

						A__							=       get_new_dict(_A			,							_A    )

						if model_type == "pretraining":
												A__							=       VisualBertForPreTraining(_A    )
						elif model_type == "vqa":
												A__							=       VisualBertForQuestionAnswering(_A    )
						elif model_type == "nlvr":
												A__							=       VisualBertForVisualReasoning(_A    )
						elif model_type == "multichoice":
												A__							=       VisualBertForMultipleChoice(_A    )

						model.load_state_dict(_A    )
						# Save Checkpoints
						Path(_A    ).mkdir(exist_ok=_A    )
						model.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      Optional[Any]      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
						parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
						UpperCAmelCase_							:      Optional[Any]      =  parser.parse_args()
						convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_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





