




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase						:							str			     =						logging.get_logger()




@dataclass
class        snake_case__				:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				nn.Module
				SCREAMING_SNAKE_CASE_			:				List[nn.Module]									=   field(default_factory=_UpperCamelCase  )
				SCREAMING_SNAKE_CASE_			:				list									=   field(default_factory=_UpperCamelCase  )
				SCREAMING_SNAKE_CASE_			:				Dict[str, nn.Module]									=   field(default_factory=_UpperCamelCase  )





				def 				__UpperCAmelCase				( self							:	str   , __lowerCamelCase							:	Union[str, Any]   , __lowerCamelCase							:	Tensor   , __lowerCamelCase							:	Tensor   , __lowerCamelCase							:	str				)   ->	int:
							a       =     len(list(m.modules()				)				) == 1 or isinstance(__lowerCamelCase   , nn.Convad				) or isinstance(__lowerCamelCase   , nn.BatchNormad				)
							if has_not_submodules:
										self.traced.append(__lowerCamelCase				)
										a       =     m





				def __call__( self							:	Dict   , __lowerCamelCase							:	Tensor				)   ->	Any:
							for name, m in self.module.named_modules():
										self.handles.append(m.register_forward_hook(partial(self._forward_hook   , name=__lowerCamelCase				)				)				)
							self.module(__lowerCamelCase				)
							[x.remove() for x in self.handles]
							return self





				@property
				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	List[Any]:
							# check the len of the state_dict keys to see if we have learnable params
							return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()				)				) > 0}




class        snake_case__				(nn.Module  ):
				"""simple docstring"""



				def __init__( self							:	List[Any]   , __lowerCamelCase							:	nn.Module				)   ->	int:
							super().__init__()

							a       =     []
							# - get the stem
							feature_blocks.append(("conv1", model.stem)				)
							# - get all the feature blocks
							for k, v in model.trunk_output.named_children():
										assert k.startswith("block"				), f"""Unexpected layer name {k}"""
										a       =     len(__lowerCamelCase				) + 1
										feature_blocks.append((f"""res{block_index}""", v)				)

							a       =     nn.ModuleDict(__lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	Tensor				)   ->	Tuple:
							return get_trunk_forward_outputs(
							    __lowerCamelCase   , out_feat_keys=__lowerCamelCase   , feature_blocks=self._feature_blocks   , )




class        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def      __magic_name__							(					A						:   str  ):






			'''simple docstring'''







			a       =     RegNetConfig(depths=[2, 7, 17, 1],    hidden_sizes=[8, 8, 8, 8],    groups_width=8  )
			if "in1k" in model_name:
						a       =     RegNetForImageClassification(A  )
			else:
						a       =     RegNetModel(A  )
			# create from model (with small weights)
			a       =     FakeRegNetVisslWrapper(
			    RegNet(FakeRegNetParams(depth=27,    group_width=1010,    w_a=1744,    w_a=6_20.83,    w_m=2.52  )  )  )

			with torch.no_grad():
						a       =     from_model.eval()
						a       =     our_model.eval()

						a       =     torch.randn((1, 3, 32, 32)  )
						# trace both
						a       =     Tracker(A  )
						a       =     dest_tracker(A  ).parametrized

						pprint(dest_tracker.nameamodule  )
						a       =     Tracker(A  )
						a       =     src_tracker(A  ).parametrized

			# convert the keys -> module dict to keys -> params
			def to_params_dict(A						:   Union[str, Any]  ):
						a       =     OrderedDict()
						for name, module in dict_with_modules.items():
									for param_name, param in module.state_dict().items():
												a       =     param
						return params_dict

			a       =     {}

			a       =     to_params_dict(A  )
			a       =     to_params_dict(A  )

			for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),    dst_state_dict.items()  ):
						a       =     dest_key
						logger.info(F"""{src_key} -> {dest_key}"""  )
			# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
			if "in1k" in model_name:
						a       =     "classifier.1.weight"
						a       =     "classifier.1.bias"

			return from_to_ours_keys




def      __magic_name__							(					A						:   Path,    A						:   str = None,    A						:   bool = True  ):






			'''simple docstring'''







			a       =     "imagenet-1k-id2label.json"
			a       =     1000

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

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

			a       =     partial(A,    num_labels=A,    idalabel=A,    labelaid=A  )

			a       =     {
			    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1],    hidden_sizes=[2020, 4040, 11110, 28280],    groups_width=1010  ),
			    # finetuned on imagenet
			    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1],    hidden_sizes=[2020, 4040, 11110, 28280],    groups_width=1010  ),
			}

			# add seer weights logic
			def load_using_classy_vision(A						:   str  ) -> Tuple[Dict, Dict]:
						a       =     torch.hub.load_state_dict_from_url(A,    model_dir=str(A  ),    map_location="cpu"  )
						# check if we have a head, if yes add it
						a       =     files["classy_state_dict"]["base_model"]["model"]
						return model_state_dict["trunk"], model_state_dict["heads"]

			a       =     {
			    "regnet-y-10b-seer": partial(
			        A,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",    ),
			    "regnet-y-10b-seer-in1k": partial(
			        A,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",    ),
			}

			a       =     get_from_to_our_keys(A  )

			if not (save_directory / F"""{model_name}.pth""").exists():
						logger.info("Loading original state_dict."  )
						a       ,						a       =     names_to_from_model[model_name]()
						a       =     from_state_dict_trunk
						if "in1k" in model_name:
									# add the head
									a       =     {**from_state_dict_trunk, **from_state_dict_head}
						logger.info("Done!"  )

						a       =     {}

						a       =     list(from_state_dict.keys()  )
						a       =     R"\.block.-part."
						# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
						for key in from_state_dict.keys():
									# remove the weird "block[0,1]-part" from the key
									a       =     re.sub(A,    "",    A  )
									# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
									a       =     from_to_ours_keys[src_key]
									# store the parameter with our key
									a       =     from_state_dict[key]
									not_used_keys.remove(A  )
						# check that all keys have been updated
						assert len(A  ) == 0, F"""Some keys where not used {",".join(A  )}"""

						logger.info(F"""The following keys were not used: {",".join(A  )}"""  )

						# save our state dict to disk
						torch.save(A,    save_directory / F"""{model_name}.pth"""  )

						del converted_state_dict
			else:
						logger.info("The state_dict was already stored on disk."  )
			if push_to_hub:
						logger.info(F"""Token is {os.environ["HF_TOKEN"]}"""  )
						logger.info("Loading our model."  )
						# create our model
						a       =     names_to_config[model_name]
						a       =     RegNetModel
						if "in1k" in model_name:
									a       =     RegNetForImageClassification
						a       =     our_model_func(A  )
						# place our model to the meta device (so remove all the weights)
						our_model.to(torch.device("meta"  )  )
						logger.info("Loading state_dict in our model."  )
						# load state dict
						a       =     our_model.state_dict().keys()
						PreTrainedModel._load_pretrained_model_low_mem(
						    A,    A,    [save_directory / F"""{model_name}.pth"""]  )
						logger.info("Finally, pushing!"  )
						# push it to hub
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / model_name,    commit_message="Add model",    output_dir=save_directory / model_name,    )
						a       =     384
						# we can use the convnext one
						a       =     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k",    size=A  )
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / model_name,    commit_message="Add image processor",    output_dir=save_directory / model_name,    )


if __name__ == "__main__":
		__lowerCAmelCase						:							Tuple			     =						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 regnet* architecture,'
		        ' currently: regnetx-*, regnety-*. 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(
		    '--push_to_hub',
		    default=True,
		    type=bool,
		    required=False,
		    help='If True, push model and image processor to the hub.',
		)

		__lowerCAmelCase						:							List[str]			     =						parser.parse_args()

		__lowerCAmelCase						:							Path			     =						args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__lowerCAmelCase						:							Optional[int]			     =						'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__lowerCAmelCase						:							str			     =						'\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

__lowerCAmelCase						:							Optional[Any]			     =						'\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,			_KWARGS_DESCRIPTION  )
class        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"				),
							                "prediction_text": datasets.Value("string"				),
							                "no_answer_probability": datasets.Value("float32"				),
							            },
							            "references": {
							                "id": datasets.Value("string"				),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"				), "answer_start": datasets.Value("int32"				)}				),
							            },
							        }				)   , codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , )





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict=1.0				)   ->	List[str]:
							a       =     {p["id"]: p["no_answer_probability"] for p in predictions}
							a       =     [{"paragraphs": [{"qas": references}]}]
							a       =     {p["id"]: p["prediction_text"] for p in predictions}

							a       =     make_qid_to_has_ans(__lowerCamelCase				)  # maps qid to True/False
							a       =     [k for k, v in qid_to_has_ans.items() if v]
							a       =     [k for k, v in qid_to_has_ans.items() if not v]

							a       ,						a       =     get_raw_scores(__lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase				)

							if has_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "HasAns"				)
							if no_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "NoAns"				)
							find_all_best_thresh(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							return dict(__lowerCamelCase				)






