






"""simple docstring"""



import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case							:   Dict	  =       logging.get_logger(__name__)




@dataclass
class 							__lowercase:
						lowerCAmelCase_		:				Union[str, Any]=							42
						lowerCAmelCase_		:				List[Any]=							field(default_factory=lowercase__	)
						lowerCAmelCase_		:				Optional[int]=							field(default_factory=lowercase__	)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	int:




											"""simple docstring"""





											lowerCamelCase__					=len(list(m.modules())) == 1 or isinstance(_SCREAMING_SNAKE_CASE     ,    nn.Convad) or isinstance(_SCREAMING_SNAKE_CASE     ,    nn.BatchNormad)
											if has_not_submodules:
																if not isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																					self.traced.append(_SCREAMING_SNAKE_CASE)






						def __call__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	str:




											"""simple docstring"""





											for m in self.module.modules():
																self.handles.append(m.register_forward_hook(self._forward_hook))
											self.module(_SCREAMING_SNAKE_CASE)
											[x.remove() for x in self.handles]
											return self






						@property
						def 		lowerCAmelCase (       self)      ->	Union[str, Any]:




											"""simple docstring"""





											return list(filter(lambda _SCREAMING_SNAKE_CASE: len(list(x.state_dict().keys())) > 0     ,    self.traced))




@dataclass
class 							__lowercase:
						lowerCAmelCase_		:				Dict=							42
						lowerCAmelCase_		:				int=							42
						lowerCAmelCase_		:				Optional[Any]=							0
						lowerCAmelCase_		:				List[str]=							field(default_factory=lowercase__	)
						lowerCAmelCase_		:				Any=							field(default_factory=lowercase__	)






						def __call__(       self     ,    _SCREAMING_SNAKE_CASE)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=Tracker(self.dest)(_SCREAMING_SNAKE_CASE).parametrized
											lowerCamelCase__					=Tracker(self.src)(_SCREAMING_SNAKE_CASE).parametrized

											lowerCamelCase__					=list(filter(lambda _SCREAMING_SNAKE_CASE: type(_SCREAMING_SNAKE_CASE) not in self.src_skip     ,    _SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=list(filter(lambda _SCREAMING_SNAKE_CASE: type(_SCREAMING_SNAKE_CASE) not in self.dest_skip     ,    _SCREAMING_SNAKE_CASE))

											if len(_SCREAMING_SNAKE_CASE) != len(_SCREAMING_SNAKE_CASE):
																raise Exception(
																    F"Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE)} operations while"
																    F" destination module has {len(_SCREAMING_SNAKE_CASE)}.")

											for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																dest_m.load_state_dict(src_m.state_dict())
																if self.verbose == 1:
																					print(F"Transfered from={src_m} to={dest_m}")




def     lowerCamelCase_					(	__A	:		nn.Module      ,						__A	:		nn.Module      ):
					lowerCamelCase__					=from_model.state_dict()
					lowerCamelCase__					=our_model.state_dict()
					lowerCamelCase__					=our_model.config
					lowerCamelCase__					=[]
					for stage_idx in range(len(config.hidden_sizes      )      ):
										for block_id in range(config.depths[stage_idx]      ):
															lowerCamelCase__					=f"block{stage_idx + 1}.{block_id}.layer_scale_1"
															lowerCamelCase__					=f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

															all_keys.append((from_key, to_key)      )
															lowerCamelCase__					=f"block{stage_idx + 1}.{block_id}.layer_scale_2"
															lowerCamelCase__					=f"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

															all_keys.append((from_key, to_key)      )

					for from_key, to_key in all_keys:
										lowerCamelCase__					=from_state_dict.pop(_lowerCamelCase      )

					our_model.load_state_dict(_lowerCamelCase      )
					return our_model




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		VanConfig      ,						__A	:		str      ,						__A	:		nn.Module      ,						__A	:		Path      ,						__A	:		bool = True      ,						):
					print(f"Downloading weights for {name}..."      )
					lowerCamelCase__					=cached_download(_lowerCamelCase      )
					print(f"Converting {name}..."      )
					lowerCamelCase__					=torch.load(_lowerCamelCase      )["state_dict"]
					from_model.load_state_dict(_lowerCamelCase      )
					from_model.eval()
					with torch.no_grad():
										lowerCamelCase__					=VanForImageClassification(_lowerCamelCase      ).eval()
										lowerCamelCase__					=ModuleTransfer(src=_lowerCamelCase      ,						dest=_lowerCamelCase      )
										lowerCamelCase__					=torch.randn((1, 3, 224, 224)      )
										module_transfer(_lowerCamelCase      )
										lowerCamelCase__					=copy_parameters(_lowerCamelCase      ,						_lowerCamelCase      )

					if not torch.allclose(from_model(_lowerCamelCase      )      ,						our_model(_lowerCamelCase      ).logits      ):
										raise ValueError('The model logits don\'t match the original one.'      )

					lowerCamelCase__					=name
					print(_lowerCamelCase      )

					if push_to_hub:
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name      ,						commit_message='Add model'      ,						use_temp_dir=_lowerCamelCase      ,						)

										# we can use the convnext one
										lowerCamelCase__					=AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'      )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name      ,						commit_message='Add image processor'      ,						use_temp_dir=_lowerCamelCase      ,						)

										print(f"Pushed {checkpoint_name}"      )




def     lowerCamelCase_					(	__A	:		Path      ,						__A	:		str = None      ,						__A	:		bool = True      ):
					lowerCamelCase__					="imagenet-1k-id2label.json"
					lowerCamelCase__					=1000

					lowerCamelCase__					="huggingface/label-files"
					lowerCamelCase__					=num_labels
					lowerCamelCase__					=json.load(open(hf_hub_download(_lowerCamelCase      ,						_lowerCamelCase      ,						repo_type='dataset'      )      ,						'r'      )      )
					lowerCamelCase__					={int(_lowerCamelCase      ): v for k, v in idalabel.items()}

					lowerCamelCase__					=idalabel
					lowerCamelCase__					={v: k for k, v in idalabel.items()}

					lowerCamelCase__					=partial(_lowerCamelCase      ,						num_labels=_lowerCamelCase      ,						idalabel=_lowerCamelCase      ,						labelaid=_lowerCamelCase      )

					lowerCamelCase__					={
					    "van-tiny": ImageNetPreTrainedConfig(
					        hidden_sizes=[32, 64, 160, 256]      ,						depths=[3, 3, 5, 2]      ,						mlp_ratios=[8, 8, 4, 4]      ,						),
					    "van-small": ImageNetPreTrainedConfig(
					        hidden_sizes=[64, 128, 320, 512]      ,						depths=[2, 2, 4, 2]      ,						mlp_ratios=[8, 8, 4, 4]      ,						),
					    "van-base": ImageNetPreTrainedConfig(
					        hidden_sizes=[64, 128, 320, 512]      ,						depths=[3, 3, 12, 3]      ,						mlp_ratios=[8, 8, 4, 4]      ,						),
					    "van-large": ImageNetPreTrainedConfig(
					        hidden_sizes=[64, 128, 320, 512]      ,						depths=[3, 5, 27, 3]      ,						mlp_ratios=[8, 8, 4, 4]      ,						),
					}

					lowerCamelCase__					={
					    "van-tiny": van_tiny,
					    "van-small": van_small,
					    "van-base": van_base,
					    "van-large": van_large,
					}

					lowerCamelCase__					={
					    "van-tiny": (
					        "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
					    ),
					    "van-small": (
					        "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
					    ),
					    "van-base": (
					        "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
					    ),
					    "van-large": (
					        "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
					    ),
					}

					if model_name:
										convert_weight_and_push(
										    _lowerCamelCase      ,						names_to_config[model_name]      ,						checkpoint=names_to_original_checkpoints[model_name]      ,						from_model=names_to_original_models[model_name]()      ,						save_directory=_lowerCamelCase      ,						push_to_hub=_lowerCamelCase      ,						)
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(
															    _lowerCamelCase      ,						_lowerCamelCase      ,						checkpoint=names_to_original_checkpoints[model_name]      ,						from_model=names_to_original_models[model_name]()      ,						save_directory=_lowerCamelCase      ,						push_to_hub=_lowerCamelCase      ,						)


if __name__ == "__main__":
						_snake_case							:   List[str]	  =       argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model-name",
						    default=None,
						    type=str,
						    help=(
						        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
						        " currently: van-tiny/small/base/large. If `None`, all of them will the converted."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=Path,
						    required=True,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument(
						    "--van_dir",
						    required=True,
						    type=Path,
						    help=(
						        "A path to VAN's original implementation directory. You can download from here:"
						        " https://github.com/Visual-Attention-Network/VAN-Classification"
						    ),
						)
						parser.add_argument(
						    "--push_to_hub",
						    default=True,
						    type=bool,
						    required=False,
						    help="If True, push model and image processor to the hub.",
						)

						_snake_case							:   List[Any]	  =       parser.parse_args()
						_snake_case							:   Path	  =       args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						_snake_case							:   Tuple	  =       args.van_dir
						# append the path to the parents to maskformer dir
						sys.path.append(str(van_dir.parent))
						from van.models.van import van_base, van_large, van_small, van_tiny

						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






import argparse
import json
import re
import string
import sys

import numpy as np


__lowercase							:Optional[Any]							=	0.5







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       [".", ",", ";", ":"]

						for token in remove_tokens:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.replace(_lowerCamelCase      , ""  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       prediction.replace(_lowerCamelCase      , ""  )

						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.lower(), prediction.lower()
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       ground_truth.replace("/"      , " "  ), prediction.replace("/"      , " "  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       set(ground_truth.split(" "  )  ), set(prediction.split(" "  )  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       ground_truth.intersection(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       ground_truth.union(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       len(_lowerCamelCase  ) / len(_lowerCamelCase  )
						return jaccard







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						def remove_articles(_lowerCamelCase   :	Any  ):
												return re.sub(r"\b(a|an|the)\b"      , " "      , _lowerCamelCase  )

						def white_space_fix(_lowerCamelCase   :	Any  ):
												return " ".join(text.split()  )

						def remove_punc(_lowerCamelCase   :	List[Any]  ):
												SCREAMING_SNAKE_CASE__  :					Tuple =       set(string.punctuation  )
												return "".join(ch for ch in text if ch not in exclude  )

						def lower(_lowerCamelCase   :	str  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase  )  )  )  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       0, 0, 0

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       "Parties" in qa_id

						# first check if ground truth is empty
						if len(_lowerCamelCase  ) == 0:
												if len(_lowerCamelCase  ) > 0:
																		fp += len(_lowerCamelCase  )  # false positive for each one
						else:
												for ground_truth in ground_truths:
																		assert len(_lowerCamelCase  ) > 0
																		# check if there is a match
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for pred in predictions:
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					str =       True

																		if match_found:
																								tp += 1
																		else:
																								fn += 1

        # now also get any fps by looping through preds
												for pred in predictions:
																		# Check if there's a match. if so, don't count (don't want to double count based on the above)
																		# but if there's no match, then this is a false positive.
																		# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
																		# multiple predictions that are matched with the same answer.)
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for ground_truth in ground_truths:
																								assert len(_lowerCamelCase  ) > 0
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Dict =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					List[Any] =       True

																		if not match_found:
																								fp += 1

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tp / (tp + fp) if tp + fp > 0 else np.nan
						SCREAMING_SNAKE_CASE__  :					Dict =       tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       precisions[::-1]
						for i in range(1      , len(_lowerCamelCase  )  ):
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       max(precision_best[i - 1]      , precision_best[i]  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       precision_best[::-1]
						return precisions







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       np.trapz(_lowerCamelCase      , _lowerCamelCase  )
						if np.isnan(_lowerCamelCase  ):
												return 0
						return aupr







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       0
						for prec, recall in zip(_lowerCamelCase      , _lowerCamelCase  ):
												if recall >= recall_thresh:
																		SCREAMING_SNAKE_CASE__  :					Optional[int] =       prec
																		break
						return prec_at_recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						return normalize_answer(_lowerCamelCase  ) == normalize_answer(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       0
						for pred in predictions:
												for ground_truth in ground_truths:
																		SCREAMING_SNAKE_CASE__  :					Optional[Any] =       metric_fn(_lowerCamelCase      , _lowerCamelCase  )
																		if score == 1:  # break the loop when one prediction matches the ground truth
																								break
												if score == 1:
																		break
						return score







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       0
						SCREAMING_SNAKE_CASE__  :					List[str] =       []
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       "Unanswered question " + qa["id"] + " will receive score 0."
																														print(_lowerCamelCase      , file=sys.stderr  )
																														continue
																								SCREAMING_SNAKE_CASE__  :					Any =       [x["text"] for x in qa["answers"]]
																								SCREAMING_SNAKE_CASE__  :					int =       predictions[qa["id"]]
																								SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       compute_precision_recall(_lowerCamelCase      , _lowerCamelCase      , qa["id"]  )

																								precisions.append(_lowerCamelCase  )
																								recalls.append(_lowerCamelCase  )

																								if precision == 0 and recall == 0:
																														fa += 0
																								else:
																														fa += 2 * (precision * recall) / (precision + recall)

																								exact_match += metric_max_over_ground_truths(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       [x for _, x in sorted(zip(_lowerCamelCase      , _lowerCamelCase  )  )]
						recalls.sort()

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       1_0_0.0 * fa / total
						SCREAMING_SNAKE_CASE__  :					Dict =       1_0_0.0 * exact_match / total
						SCREAMING_SNAKE_CASE__  :					str =       get_aupr(_lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.9  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.8  )

						return {
						    "exact_match": exact_match,
						    "f1": fa,
						    "aupr": aupr,
						    "prec_at_80_recall": prec_at_aa_recall,
						    "prec_at_90_recall": prec_at_aa_recall,
						}


if __name__ == "__main__":
				__lowercase							:Tuple							=	argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__lowercase							:Tuple							=	parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__lowercase							:Union[str, Any]							=	json.load(dataset_file)
								__lowercase							:Union[str, Any]							=	dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__lowercase							:Optional[Any]							=	json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))