




'''simple docstring'''







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()
A					       =logging.get_logger()


@dataclass
class      _a    :
       __a							:	nn.Module
       __a							:	List[nn.Module]  							=   field(default_factory=__a					)
       __a							:	list  							=   field(default_factory=__a					)
       __a							:	Dict[str, nn.Module]  							=   field(default_factory=__a					)
       def        A (      self		:      Optional[int]  ,							lowercase		:      Optional[Any]  ,							lowercase		:      Tensor  ,							lowercase		:      Tensor  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						len(list(m.modules()							)							) == 1 or isinstance(lowercase  ,							nn.Convad							) or isinstance(lowercase  ,							nn.BatchNormad							)
           if has_not_submodules:
               self.traced.append(lowercase							)
               UpperCAmelCase     			=						m
       def __call__(      self		:      Optional[int]  ,							lowercase		:      Tensor							):



           '''simple docstring'''

           for name, m in self.module.named_modules():
               self.handles.append(m.register_forward_hook(partial(self._forward_hook  ,							name=lowercase							)							)							)
           self.module(lowercase							)
           [x.remove() for x in self.handles]
           return self







       @property
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}






class      _a    (      nn.Module					):
       def __init__(      self		:      List[Any]  ,							lowercase		:      nn.Module							):



           '''simple docstring'''

           super().__init__()

           UpperCAmelCase     			=						[]
           # - 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}"
               UpperCAmelCase     			=						len(lowercase							) + 1
               feature_blocks.append((f"res{block_index}", v)							)

           UpperCAmelCase     			=						nn.ModuleDict(lowercase							)







       def        A (      self		:      List[str]  ,							lowercase		:      Tensor							):



           '''simple docstring'''

           return get_trunk_forward_outputs(
               lowercase  ,							out_feat_keys=lowercase  ,							feature_blocks=self._feature_blocks  ,							)






class      _a    (      __a					):
       def        A (      self		:      Optional[int]							):



           '''simple docstring'''

           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 		snake_case_				(_a      :				str	):
    UpperCAmelCase     			=						RegNetConfig(depths=[2, 7, 1_7, 1]       ,  hidden_sizes=[8, 8, 8, 8]       ,  groups_width=8	)
    if "in1k" in model_name:
        UpperCAmelCase     			=						RegNetForImageClassification(_a	)
    else:
        UpperCAmelCase     			=						RegNetModel(_a	)
    # create from model (with small weights)
    UpperCAmelCase     			=						FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=2_7       ,  group_width=1_0_1_0       ,  w_a=1_7_4_4       ,  w_a=620.83       ,  w_m=2.52	)	)	)

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

        UpperCAmelCase     			=						torch.randn((1, 3, 3_2, 3_2)	)
        # trace both
        UpperCAmelCase     			=						Tracker(_a	)
        UpperCAmelCase     			=						dest_tracker(_a	).parametrized

        pprint(dest_tracker.nameamodule	)
        UpperCAmelCase     			=						Tracker(_a	)
        UpperCAmelCase     			=						src_tracker(_a	).parametrized

    # convert the keys -> module dict to keys -> params
    def to_params_dict(_a      :				Any	):
        UpperCAmelCase     			=						OrderedDict()
        for name, module in dict_with_modules.items():
            for param_name, param in module.state_dict().items():
                UpperCAmelCase     			=						param
        return params_dict

    UpperCAmelCase     			=						{}

    UpperCAmelCase     			=						to_params_dict(_a	)
    UpperCAmelCase     			=						to_params_dict(_a	)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,  dst_state_dict.items()	):
        UpperCAmelCase     			=						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:
        UpperCAmelCase     			=						'''classifier.1.weight'''
        UpperCAmelCase     			=						'''classifier.1.bias'''

    return from_to_ours_keys





def 		snake_case_				(_a      :				Path       ,  _a      :				str = None       ,  _a      :				bool = True	):
    UpperCAmelCase     			=						'''imagenet-1k-id2label.json'''
    UpperCAmelCase     			=						1_0_0_0

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

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

    UpperCAmelCase     			=						partial(_a       ,  num_labels=_a       ,  idalabel=_a       ,  labelaid=_a	)

    UpperCAmelCase     			=						{
        '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
            depths=[2, 7, 1_7, 1]       ,  hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]       ,  groups_width=1_0_1_0	),
        # finetuned on imagenet
        '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
            depths=[2, 7, 1_7, 1]       ,  hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]       ,  groups_width=1_0_1_0	),
    }

    # add seer weights logic
    def load_using_classy_vision(_a      :				str	) -> Tuple[Dict, Dict]:
        UpperCAmelCase     			=						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
        UpperCAmelCase     			=						files['''classy_state_dict''']['''base_model''']['''model''']
        return model_state_dict["trunk"], model_state_dict["heads"]

    UpperCAmelCase     			=						{
        '''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'''       ,  ),
    }

    UpperCAmelCase     			=						get_from_to_our_keys(_a	)

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

        UpperCAmelCase     			=						{}

        UpperCAmelCase     			=						list(from_state_dict.keys()	)
        UpperCAmelCase     			=						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
            UpperCAmelCase     			=						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
            UpperCAmelCase     			=						from_to_ours_keys[src_key]
            # store the parameter with our key
            UpperCAmelCase     			=						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
        UpperCAmelCase     			=						names_to_config[model_name]
        UpperCAmelCase     			=						RegNetModel
        if "in1k" in model_name:
            UpperCAmelCase     			=						RegNetForImageClassification
        UpperCAmelCase     			=						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
        UpperCAmelCase     			=						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       ,  )
        UpperCAmelCase     			=						3_8_4
        # we can use the convnext one
        UpperCAmelCase     			=						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__":
   A					       =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.',
   )

   A					       =parser.parse_args()

   A					       =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)










'''simple docstring'''







A					       =[[0, 1, 1], [0, 0, 1], [1, 0, 0]]


class      _a    :
       def __init__(      self		:      Optional[int]  ,							lowercase		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						name
           UpperCAmelCase     			=						[]
           UpperCAmelCase     			=						[]
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      int							):



           '''simple docstring'''

           self.inbound.append(lowercase							)
       def        A (      self		:      List[str]  ,							lowercase		:      Optional[Any]							):



           '''simple docstring'''

           self.outbound.append(lowercase							)







       def __repr__(      self		:      Any							):



           '''simple docstring'''

           return f"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"






def 		snake_case_				(_a      :				str       ,  _a      :				str=3       ,  _a      :				Any=0.85	):
    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						1

    UpperCAmelCase     			=						{}
    for node in nodes:
        UpperCAmelCase     			=						len(node.outbound	)

    for i in range(_a	):
        print(F"======= Iteration {i + 1} ======="	)
        for _, node in enumerate(_a	):
            UpperCAmelCase     			=						(1 - d) + d * sum(
                ranks[ib] / outbounds[ib] for ib in node.inbound	)
        print(_a	)





def 		snake_case_				():
    UpperCAmelCase     			=						list(input('''Enter Names of the Nodes: '''	).split()	)

    UpperCAmelCase     			=						[Node(_a	) for name in names]

    for ri, row in enumerate(_a	):
        for ci, col in enumerate(_a	):
            if col == 1:
                nodes[ci].add_inbound(names[ri]	)
                nodes[ri].add_outbound(names[ci]	)

    print('''======= Nodes ======='''	)
    for node in nodes:
        print(_a	)

    page_rank(_a	)


if __name__ == "__main__":
   main()




