


"""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()
__SCREAMING_SNAKE_CASE				:     Optional[int]		=     logging.get_logger(__name__)





@dataclass
class   __A  :





							'''simple docstring'''

							__lowercase:		nn.Module
							__lowercase:		List[nn.Module]				   =     field(default_factory=snake_case__)
							__lowercase:		list				   =     field(default_factory=snake_case__)



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Tensor		,							UpperCAmelCase_				:   Tensor							)       ->Tuple:



														"""simple docstring"""



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



							def __call__(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Tensor							)       ->Dict:



														"""simple docstring"""



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



							@property
							def 		lowerCAmelCase							(					self				:   Dict							)       ->Optional[Any]:



														"""simple docstring"""



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





@dataclass
class   __A  :





							'''simple docstring'''

							__lowercase:		nn.Module
							__lowercase:		nn.Module
							__lowercase:		int				   =     0
							__lowercase:		List				   =     field(default_factory=snake_case__)
							__lowercase:		List				   =     field(default_factory=snake_case__)



							def __call__(					self				:   List[Any]		,							UpperCAmelCase_				:   Tensor							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		Tracker(self.dest							)(UpperCAmelCase_							).parametrized
														snake_case_     							=		Tracker(self.src							)(UpperCAmelCase_							).parametrized

														snake_case_     							=		list(filter(lambda UpperCAmelCase_							: type(UpperCAmelCase_							) not in self.src_skip		,							UpperCAmelCase_							)							)
														snake_case_     							=		list(filter(lambda UpperCAmelCase_							: type(UpperCAmelCase_							) not in self.dest_skip		,							UpperCAmelCase_							)							)

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

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







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					nn.Module:
							# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
							snake_case_     							=		from_model.state_dict()
							snake_case_     							=		our_model.state_dict()
							snake_case_     							=		our_model.config
							snake_case_     							=		[]
							for stage_idx in range(len(config.hidden_sizes					)					):
														for block_id in range(config.depths[stage_idx]					):
																					snake_case_     							=		f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																					snake_case_     							=		f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																					all_keys.append((from_key, to_key)					)
																					snake_case_     							=		f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																					snake_case_     							=		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:
														snake_case_     							=		from_state_dict.pop(_SCREAMING_SNAKE_CASE					)

							our_model.load_state_dict(_SCREAMING_SNAKE_CASE					)
							return our_model







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE = True					,			)     ->					List[Any]:
							print(f"""Downloading weights for {name}..."""					)
							snake_case_     							=		cached_download(_SCREAMING_SNAKE_CASE					)
							print(f"""Converting {name}..."""					)
							snake_case_     							=		torch.load(_SCREAMING_SNAKE_CASE					)["""state_dict"""]
							from_model.load_state_dict(_SCREAMING_SNAKE_CASE					)
							from_model.eval()
							with torch.no_grad():
														snake_case_     							=		VanForImageClassification(_SCREAMING_SNAKE_CASE					).eval()
														snake_case_     							=		ModuleTransfer(src=_SCREAMING_SNAKE_CASE					,			dest=_SCREAMING_SNAKE_CASE					)
														snake_case_     							=		torch.randn((1, 3, 224, 224)					)
														module_transfer(_SCREAMING_SNAKE_CASE					)
														snake_case_     							=		copy_parameters(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)

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

							snake_case_     							=		name
							print(_SCREAMING_SNAKE_CASE					)

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

														# we can use the convnext one
														snake_case_     							=		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=_SCREAMING_SNAKE_CASE					,			)

														print(f"""Pushed {checkpoint_name}"""					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE = None					,			_SCREAMING_SNAKE_CASE = True					)     ->					Dict:
							snake_case_     							=		"""imagenet-1k-id2label.json"""
							snake_case_     							=		1_000

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

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

							snake_case_     							=		partial(_SCREAMING_SNAKE_CASE					,			num_labels=_SCREAMING_SNAKE_CASE					,			idalabel=_SCREAMING_SNAKE_CASE					,			labelaid=_SCREAMING_SNAKE_CASE					)

							snake_case_     							=		{
							    """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]					,			),
							}

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

							snake_case_     							=		{
							    """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(
														    _SCREAMING_SNAKE_CASE					,			names_to_config[model_name]					,			checkpoint=names_to_original_checkpoints[model_name]					,			from_model=names_to_original_models[model_name]()					,			save_directory=_SCREAMING_SNAKE_CASE					,			push_to_hub=_SCREAMING_SNAKE_CASE					,			)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			checkpoint=names_to_original_checkpoints[model_name]					,			from_model=names_to_original_models[model_name]()					,			save_directory=_SCREAMING_SNAKE_CASE					,			push_to_hub=_SCREAMING_SNAKE_CASE					,			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     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.',
				)

				__SCREAMING_SNAKE_CASE				:     Tuple		=     parser.parse_args()
				__SCREAMING_SNAKE_CASE				:     Path		=     args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				__SCREAMING_SNAKE_CASE				:     List[str]		=     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)







"""simple docstring"""
import argparse
import json
import re
import string
import sys
from collections import Counter







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Any:

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

							def white_space_fix(_SCREAMING_SNAKE_CASE					):
														return " ".join(text.split()					)

							def remove_punc(_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		set(string.punctuation					)
														return "".join(ch for ch in text if ch not in exclude					)

							def lower(_SCREAMING_SNAKE_CASE					):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE					)					)					)					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		normalize_answer(_SCREAMING_SNAKE_CASE					).split()
							snake_case_     							=		Counter(_SCREAMING_SNAKE_CASE					) & Counter(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		sum(common.values()					)
							if num_same == 0:
														return 0
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		(2 * precision * recall) / (precision + recall)
							return fa







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							return normalize_answer(_SCREAMING_SNAKE_CASE					) == normalize_answer(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		[]
							for ground_truth in ground_truths:
														snake_case_     							=		metric_fn(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
														scores_for_ground_truths.append(_SCREAMING_SNAKE_CASE					)
							return max(_SCREAMING_SNAKE_CASE					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[int]:
							snake_case_     							=		snake_case_     							=		snake_case_     							=		0
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			snake_case_     							=		"""Unanswered question """ + qa["""id"""] + """ will receive score 0."""
																																			print(_SCREAMING_SNAKE_CASE					,			file=sys.stderr					)
																																			continue
																												snake_case_     							=		[x["""text"""] for x in qa["""answers"""]]
																												snake_case_     							=		predictions[qa["""id"""]]
																												exact_match += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
																												fa += metric_max_over_ground_truths(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)

							snake_case_     							=		100.0 * exact_match / total
							snake_case_     							=		100.0 * fa / total

							return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Tuple		=     '1.1'
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
				parser.add_argument('dataset_file', help='Dataset file')
				parser.add_argument('prediction_file', help='Prediction File')
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     json.load(dataset_file)
								if dataset_json["version"] != expected_version:
												print(
												    'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
												    file=sys.stderr,
												)
								__SCREAMING_SNAKE_CASE				:     str		=     dataset_json['data']
				with open(args.prediction_file) as prediction_file:
								__SCREAMING_SNAKE_CASE				:     int		=     json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



