





'''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__       :     List[str]          =logging.get_logger()




@dataclass
class 				UpperCAmelCase  :
      _lowercase:    nn.Module
      _lowercase:    List[nn.Module]		  =     field(default_factory=snake_case_     )
      _lowercase:    list		  =     field(default_factory=snake_case_     )
      _lowercase:    Dict[str, nn.Module]		  =     field(default_factory=snake_case_     )




      def        lowercase__					(    self    :				str    ,    __snake_case    :				List[str]    ,    __snake_case    :				Tensor    ,    __snake_case    :				Tensor    ,    __snake_case    :				str						)       ->   Tuple:
            _lowerCAmelCase							  =							len(list(m.modules()						)						) == 1 or isinstance(__snake_case    ,    nn.Convad						) or isinstance(__snake_case    ,    nn.BatchNormad						)
            if has_not_submodules:
                  self.traced.append(__snake_case						)
                  _lowerCAmelCase							  =							m




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




      @property
      def        lowercase__					(    self    :				Union[str, Any]						)       ->   Optional[int]:
            # 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 				UpperCAmelCase  (    nn.Module     ):




      def __init__(    self    :				Optional[Any]    ,    __snake_case    :				nn.Module						)       ->   Union[str, Any]:
            super().__init__()

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

            _lowerCAmelCase							  =							nn.ModuleDict(__snake_case						)




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				Tensor						)       ->   List[str]:
            return get_trunk_forward_outputs(
                __snake_case    ,    out_feat_keys=__snake_case    ,    feature_blocks=self._feature_blocks    ,    )




class 				UpperCAmelCase  (    snake_case_     ):




      def        lowercase__					(    self    :				List[Any]						)       ->   int:
            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      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							RegNetConfig(depths=[2, 7, 17, 1]							,			hidden_sizes=[8, 8, 8, 8]							,			groups_width=8						)
      if "in1k" in model_name:
            _lowerCAmelCase							  =							RegNetForImageClassification(lowerCAmelCase						)
      else:
            _lowerCAmelCase							  =							RegNetModel(lowerCAmelCase						)
      # create from model (with small weights)
      _lowerCAmelCase							  =							FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27							,			group_width=10_10							,			w_a=17_44							,			w_a=620.83							,			w_m=2.52						)						)						)

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

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

            pprint(dest_tracker.nameamodule						)
            _lowerCAmelCase							  =							Tracker(lowerCAmelCase						)
            _lowerCAmelCase							  =							src_tracker(lowerCAmelCase						).parametrized

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

      _lowerCAmelCase							  =							{}

      _lowerCAmelCase							  =							to_params_dict(lowerCAmelCase						)
      _lowerCAmelCase							  =							to_params_dict(lowerCAmelCase						)

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

      return from_to_ours_keys
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase = None							,			lowerCAmelCase = True						):





      """simple docstring"""


      _lowerCAmelCase							  =							"""imagenet-1k-id2label.json"""
      _lowerCAmelCase							  =							10_00

      _lowerCAmelCase							  =							"""huggingface/label-files"""
      _lowerCAmelCase							  =							num_labels
      _lowerCAmelCase							  =							json.load(open(cached_download(hf_hub_url(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							  =							{
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]							,			hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,			groups_width=10_10						),
          # finetuned on imagenet
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]							,			hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,			groups_width=10_10						),
      }

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

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

      _lowerCAmelCase							  =							get_from_to_our_keys(lowerCAmelCase						)

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

            _lowerCAmelCase							  =							{}

            _lowerCAmelCase							  =							list(from_state_dict.keys()						)
            _lowerCAmelCase							  =							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							  =							re.sub(lowerCAmelCase							,			""""""							,			lowerCAmelCase						)
                  # 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							  =							from_to_ours_keys[src_key]
                  # store the parameter with our key
                  _lowerCAmelCase							  =							from_state_dict[key]
                  not_used_keys.remove(lowerCAmelCase						)
            # check that all keys have been updated
            assert len(lowerCAmelCase						) == 0, f"Some keys where not used {','.join(lowerCAmelCase						)}"

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

            # save our state dict to disk
            torch.save(lowerCAmelCase							,			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							  =							names_to_config[model_name]
            _lowerCAmelCase							  =							RegNetModel
            if "in1k" in model_name:
                  _lowerCAmelCase							  =							RegNetForImageClassification
            _lowerCAmelCase							  =							our_model_func(lowerCAmelCase						)
            # 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							  =							our_model.state_dict().keys()
            PreTrainedModel._load_pretrained_model_low_mem(
                lowerCAmelCase							,			lowerCAmelCase							,			[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							  =							3_84
            # we can use the convnext one
            _lowerCAmelCase							  =							AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""							,			size=lowerCAmelCase						)
            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__       :     Dict          =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__       :     Dict          =parser.parse_args()

      A__       :     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)













'''simple docstring'''






import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


A__       :     Optional[Any]          =get_logger(__name__)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = None							,			):





      """simple docstring"""


      for i, state in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Model weights saved in {output_model_file}"						)
      # Optimizer states
      for i, opt in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							opt.state_dict()
            _lowerCAmelCase							  =							f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Optimizer state saved in {output_optimizer_file}"						)
      # Scheduler states
      for i, scheduler in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							scheduler.state_dict()
            _lowerCAmelCase							  =							f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Scheduler state saved in {output_scheduler_file}"						)
      # GradScaler state
      if scaler is not None:
            _lowerCAmelCase							  =							scaler.state_dict()
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            torch.save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Gradient scaler state saved in {output_scaler_file}"						)
      # Random number generator states
      _lowerCAmelCase							  =							{}
      _lowerCAmelCase							  =							f"{RNG_STATE_NAME}_{process_index}.pkl"
      _lowerCAmelCase							  =							random.getstate()
      _lowerCAmelCase							  =							np.random.get_state()
      _lowerCAmelCase							  =							torch.get_rng_state()
      if is_xpu_available():
            _lowerCAmelCase							  =							torch.xpu.get_rng_state_all()
      else:
            _lowerCAmelCase							  =							torch.cuda.get_rng_state_all()
      if is_tpu_available():
            _lowerCAmelCase							  =							xm.get_rng_state()
      _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
      torch.save(lowerCAmelCase							,			lowerCAmelCase						)
      logger.info(f"Random states saved in {output_states_file}"						)
      return output_dir
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase=None							,			lowerCAmelCase=None							,			**lowerCAmelCase							,			):





      """simple docstring"""


      if map_location not in [None, "cpu", "on_device"]:
            raise TypeError(
                """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""						)
      if map_location is None:
            _lowerCAmelCase							  =							"""cpu"""
      elif map_location == "on_device":
            _lowerCAmelCase							  =							PartialState().device
      # Model states
      for i, model in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            models[i].load_state_dict(torch.load(lowerCAmelCase							,			map_location=lowerCAmelCase						)							,			**lowerCAmelCase						)
      logger.info("""All model weights loaded successfully"""						)

      # Optimizer states
      for i, opt in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            _lowerCAmelCase							  =							torch.load(lowerCAmelCase							,			map_location=lowerCAmelCase						)
            optimizers[i].load_state_dict(lowerCAmelCase						)
      logger.info("""All optimizer states loaded successfully"""						)

      # Scheduler states
      for i, scheduler in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            scheduler.load_state_dict(torch.load(lowerCAmelCase						)						)
      logger.info("""All scheduler states loaded successfully"""						)

      # GradScaler state
      if scaler is not None:
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            scaler.load_state_dict(torch.load(lowerCAmelCase						)						)
            logger.info("""GradScaler state loaded successfully"""						)

      # Random states
      try:
            _lowerCAmelCase							  =							torch.load(os.path.join(lowerCAmelCase							,			f"{RNG_STATE_NAME}_{process_index}.pkl"						)						)
            random.setstate(states["""random_state"""]						)
            np.random.set_state(states["""numpy_random_seed"""]						)
            torch.set_rng_state(states["""torch_manual_seed"""]						)
            if is_xpu_available():
                  torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]						)
            else:
                  torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]						)
            if is_tpu_available():
                  xm.set_rng_state(states["""xm_seed"""]						)
            logger.info("""All random states loaded successfully"""						)
      except Exception:
            logger.info("""Could not load random states"""						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = 0						):





      """simple docstring"""


      _lowerCAmelCase							  =							Path(lowerCAmelCase						) / f"custom_checkpoint_{index}.pkl"
      logger.info(f"Saving the state of {get_pretty_name(lowerCAmelCase						)} to {save_location}"						)
      torch.save(obj.state_dict()							,			lowerCAmelCase						)





def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = 0						):





      """simple docstring"""


      _lowerCAmelCase							  =							f"{path}/custom_checkpoint_{index}.pkl"
      logger.info(f"Loading the state of {get_pretty_name(lowerCAmelCase						)} from {load_location}"						)
      obj.load_state_dict(torch.load(lowerCAmelCase							,			map_location="""cpu"""						)						)






