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     :			Optional[Any]					    =						logging.get_logger()

@dataclass
class 				_lowercase						:






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Tuple        =						42
    SCREAMING_SNAKE_CASE:							Any        =						field(default_factory=lowerCamelCase__ )
    SCREAMING_SNAKE_CASE:							Any        =						field(default_factory=lowerCamelCase__ )
    SCREAMING_SNAKE_CASE:							Optional[Any]        =						field(default_factory=lowerCamelCase__ )




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
        lowerCAmelCase_:       Optional[int]         =  len(list(m.modules()   )   ) == 1 or isinstance(SCREAMING_SNAKE_CASE_					,	nn.Convad   ) or isinstance(SCREAMING_SNAKE_CASE_					,	nn.BatchNormad   )
        if has_not_submodules:
            self.traced.append(SCREAMING_SNAKE_CASE_   )
            lowerCAmelCase_:       Union[str, Any]         =  m




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




    @property
    def     _a						(						self   ):
        # 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 				_lowercase						(    nn.Module ):






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__   ):
        super().__init__()

        lowerCAmelCase_:       Union[str, Any]         =  []
        # - 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}'''
            lowerCAmelCase_:       Union[str, Any]         =  len(SCREAMING_SNAKE_CASE_   ) + 1
            feature_blocks.append((F'''res{block_index}''', v)   )

        lowerCAmelCase_:       Optional[int]         =  nn.ModuleDict(SCREAMING_SNAKE_CASE_   )




    def     _a						(						self					,	lowerCamelCase__   ):
        return get_trunk_forward_outputs(
            SCREAMING_SNAKE_CASE_					,	out_feat_keys=SCREAMING_SNAKE_CASE_					,	feature_blocks=self._feature_blocks					,	)





class 				_lowercase						(    lowerCamelCase__ ):






    '''simple docstring'''




    def     _a						(						self   ):
        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__  (		lowercase				):
    lowerCAmelCase_:       List[Any]         =  RegNetConfig(depths=[2, 7, 17, 1]       ,							hidden_sizes=[8, 8, 8, 8]       ,							groups_width=8				)
    if "in1k" in model_name:
        lowerCAmelCase_:       Optional[int]         =  RegNetForImageClassification(_UpperCAmelCase				)
    else:
        lowerCAmelCase_:       Optional[int]         =  RegNetModel(_UpperCAmelCase				)
    # create from model (with small weights)
    lowerCAmelCase_:       str         =  FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27       ,							group_width=1010       ,							w_a=1744       ,							w_a=620.83       ,							w_m=2.52				)				)				)

    with torch.no_grad():
        lowerCAmelCase_:       int         =  from_model.eval()
        lowerCAmelCase_:       str         =  our_model.eval()

        lowerCAmelCase_:       List[str]         =  torch.randn((1, 3, 32, 32)				)
        # trace both
        lowerCAmelCase_:       Dict         =  Tracker(_UpperCAmelCase				)
        lowerCAmelCase_:       Dict         =  dest_tracker(_UpperCAmelCase				).parametrized

        pprint(dest_tracker.nameamodule				)
        lowerCAmelCase_:       List[str]         =  Tracker(_UpperCAmelCase				)
        lowerCAmelCase_:       int         =  src_tracker(_UpperCAmelCase				).parametrized

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

    lowerCAmelCase_:       int         =  {}

    lowerCAmelCase_:       int         =  to_params_dict(_UpperCAmelCase				)
    lowerCAmelCase_:       Tuple         =  to_params_dict(_UpperCAmelCase				)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,							dst_state_dict.items()				):
        lowerCAmelCase_:       int         =  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:
        lowerCAmelCase_:       Optional[Any]         =  "classifier.1.weight"
        lowerCAmelCase_:       Dict         =  "classifier.1.bias"

    return from_to_ours_keys
def 		snake_case__  (		lowercase       ,							lowercase = None       ,							lowercase = True				):
    lowerCAmelCase_:       int         =  "imagenet-1k-id2label.json"
    lowerCAmelCase_:       Optional[int]         =  1000

    lowerCAmelCase_:       str         =  "huggingface/label-files"
    lowerCAmelCase_:       int         =  num_labels
    lowerCAmelCase_:       List[Any]         =  json.load(open(cached_download(hf_hub_url(_UpperCAmelCase       ,							_UpperCAmelCase       ,							repo_type="dataset"				)				)       ,							"r"				)				)
    lowerCAmelCase_:       int         =  {int(_UpperCAmelCase				): v for k, v in idalabel.items()}

    lowerCAmelCase_:       List[Any]         =  idalabel
    lowerCAmelCase_:       Tuple         =  {v: k for k, v in idalabel.items()}

    lowerCAmelCase_:       Dict         =  partial(_UpperCAmelCase       ,							num_labels=_UpperCAmelCase       ,							idalabel=_UpperCAmelCase       ,							labelaid=_UpperCAmelCase				)

    lowerCAmelCase_:       Optional[Any]         =  {
        "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(lowercase				) -> Tuple[Dict, Dict]:
        lowerCAmelCase_:       Dict         =  torch.hub.load_state_dict_from_url(_UpperCAmelCase       ,							model_dir=str(_UpperCAmelCase				)       ,							map_location="cpu"				)
        # check if we have a head, if yes add it
        lowerCAmelCase_:       List[Any]         =  files["classy_state_dict"]["base_model"]["model"]
        return model_state_dict["trunk"], model_state_dict["heads"]

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

    lowerCAmelCase_:       List[Any]         =  get_from_to_our_keys(_UpperCAmelCase				)

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

        lowerCAmelCase_:       Dict         =  {}

        lowerCAmelCase_:       Optional[int]         =  list(from_state_dict.keys()				)
        lowerCAmelCase_:       Tuple         =  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
            lowerCAmelCase_:       Optional[Any]         =  re.sub(_UpperCAmelCase       ,							""       ,							_UpperCAmelCase				)
            # 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
            lowerCAmelCase_:       int         =  from_to_ours_keys[src_key]
            # store the parameter with our key
            lowerCAmelCase_:       List[str]         =  from_state_dict[key]
            not_used_keys.remove(_UpperCAmelCase				)
        # check that all keys have been updated
        assert len(_UpperCAmelCase				) == 0, F'''Some keys where not used {",".join(_UpperCAmelCase				)}'''

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

        # save our state dict to disk
        torch.save(_UpperCAmelCase       ,							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
        lowerCAmelCase_:       List[Any]         =  names_to_config[model_name]
        lowerCAmelCase_:       List[str]         =  RegNetModel
        if "in1k" in model_name:
            lowerCAmelCase_:       Dict         =  RegNetForImageClassification
        lowerCAmelCase_:       Tuple         =  our_model_func(_UpperCAmelCase				)
        # 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
        lowerCAmelCase_:       List[Any]         =  our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            _UpperCAmelCase       ,							_UpperCAmelCase       ,							[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       ,							)
        lowerCAmelCase_:       Union[str, Any]         =  384
        # we can use the convnext one
        lowerCAmelCase_:       Dict         =  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"       ,							size=_UpperCAmelCase				)
        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     :			Optional[int]					    =						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     :			Union[str, Any]					    =						parser.parse_args()

 a     :			List[str]					    =						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)
def 		snake_case__  (		lowercase = 998001				):

   # fetches the next number
   for number in range(n - 1       ,							9999       ,							-1				):
      lowerCAmelCase_:       str         =  str(lowercase				)

      # checks whether 'str_number' is a palindrome.
      if str_number == str_number[::-1]:
         lowerCAmelCase_:       List[str]         =  999

         # if 'number' is a product of two 3-digit numbers
         # then number is the answer otherwise fetch next number.
         while divisor != 99:
            if (number % divisor == 0) and (len(str(number // divisor				)				) == 3.0):
               return number
            divisor -= 1
   raise ValueError("That number is larger than our acceptable range."				)


if __name__ == "__main__":
 print(f'''{solution() = }''')