



"""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()
lowercase__						:							Dict        =      logging.get_logger()







@dataclass
class  UpperCamelCase__  :




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	field(default_factory=lowercase_    )
      _SCREAMING_SNAKE_CASE    							=	field(default_factory=lowercase_    )
      _SCREAMING_SNAKE_CASE    							=	field(default_factory=lowercase_    )



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]       ,     SCREAMING_SNAKE_CASE_							: Tensor       ,     SCREAMING_SNAKE_CASE_							: Tensor       ,     SCREAMING_SNAKE_CASE_							: str			):
        lowerCAmelCase_  :							List[Any]								=					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_  :							str								=					m



      def __call__( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: Tensor			):
        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 	SCREAMING_SNAKE_CASE__					( self							: str			):
        # 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    ):




      """simple docstring"""




      def __init__( self							: Any       ,     SCREAMING_SNAKE_CASE_							: nn.Module			):
        super().__init__()

        lowerCAmelCase_  :							List[Tuple[str, nn.Module]]								=					[]
        # - 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_  :							Dict								=					len(SCREAMING_SNAKE_CASE_			) + 1
          feature_blocks.append((F"res{block_index}", v)			)

        lowerCAmelCase_  :							Tuple								=					nn.ModuleDict(SCREAMING_SNAKE_CASE_			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: Tensor			):
        return get_trunk_forward_outputs(
            SCREAMING_SNAKE_CASE_       ,     out_feat_keys=SCREAMING_SNAKE_CASE_       ,     feature_blocks=self._feature_blocks       ,     )







class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: int			):
        return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]




def 							UpperCamelCase_       (       lowerCAmelCase__ :							str		)		->  Dict[str, str]:

  """simple docstring"""




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

  with torch.no_grad():
    lowerCAmelCase_  :							List[str]								=					from_model.eval()
    lowerCAmelCase_  :							Any								=					our_model.eval()

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

    pprint(dest_tracker.nameamodule		)
    lowerCAmelCase_  :							int								=					Tracker(lowerCAmelCase__		)
    lowerCAmelCase_  :							Any								=					src_tracker(lowerCAmelCase__		).parametrized

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

  lowerCAmelCase_  :							str								=					{}

  lowerCAmelCase_  :							Optional[Any]								=					to_params_dict(lowerCAmelCase__		)
  lowerCAmelCase_  :							Optional[int]								=					to_params_dict(lowerCAmelCase__		)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,  dst_state_dict.items()		):
    lowerCAmelCase_  :							Dict								=					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_  :							Dict								=					'classifier.1.weight'
    lowerCAmelCase_  :							List[str]								=					'classifier.1.bias'

  return from_to_ours_keys

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Path       ,  lowerCAmelCase__ :							str = None       ,  lowerCAmelCase__ :							bool = True		)		->  int:

  """simple docstring"""




  lowerCAmelCase_  :							int								=					'imagenet-1k-id2label.json'
  lowerCAmelCase_  :							Optional[Any]								=					1000

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

  lowerCAmelCase_  :							Optional[int]								=					idalabel
  lowerCAmelCase_  :							List[str]								=					{v: k for k, v in idalabel.items()}

  lowerCAmelCase_  :							List[str]								=					partial(lowerCAmelCase__       ,  num_labels=lowerCAmelCase__       ,  idalabel=lowerCAmelCase__       ,  labelaid=lowerCAmelCase__		)

  lowerCAmelCase_  :							List[str]								=					{
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,  hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,  groups_width=1010		),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,  hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,  groups_width=1010		),
  }

  # add seer weights logic
  def load_using_classy_vision(lowerCAmelCase__ :							str		) -> Tuple[Dict, Dict]:
    lowerCAmelCase_  :							Union[str, Any]								=					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_  :							Optional[int]								=					files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  lowerCAmelCase_  :							Union[str, Any]								=					{
      '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_  :							List[Any]								=					get_from_to_our_keys(lowerCAmelCase__		)

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

    lowerCAmelCase_  :							List[str]								=					{}

    lowerCAmelCase_  :							str								=					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_  :							str								=					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_  :							str								=					from_to_ours_keys[src_key]
      # store the parameter with our key
      lowerCAmelCase_  :							Optional[Any]								=					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_  :							int								=					names_to_config[model_name]
    lowerCAmelCase_  :							Optional[Any]								=					RegNetModel
    if "in1k" in model_name:
      lowerCAmelCase_  :							str								=					RegNetForImageClassification
    lowerCAmelCase_  :							Optional[int]								=					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_  :							List[Any]								=					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_  :							Any								=					384
    # we can use the convnext one
    lowerCAmelCase_  :							List[Any]								=					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__":
     lowercase__						:							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.""",
     )

     lowercase__						:							Dict        =      parser.parse_args()

     lowercase__						:							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
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
     print("""#################### AVAILABLE DEVICES ####################""")
     print(jax.devices())
     print("""###########################################################""")

     # setup for wandb sweep
     lowercase__						:							Any        =      Args()
     lowercase__						:							List[Any]        =      wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
     lowercase__						:							List[Any]        =      dict(logger.config)
     del wandb_args["batch_size"]
     lowercase__						:							List[str]        =      replace(args, **wandb_args)
     lowercase__						:							Any        =      args.base_dir + """-""" + wandb.run.id
     lowercase__						:							Tuple        =      replace(args, base_dir=base_dir)
     print(args)

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

     # drop extra batch for now
     lowercase__						:							Tuple        =      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
     lowercase__						:							Tuple        =      tr_dataset.shuffle().select(indices)
     lowercase__						:							Optional[int]        =      range(len(val_dataset) - len(val_dataset) % args.batch_size)
     lowercase__						:							str        =      val_dataset.shuffle().select(indices)

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """lr""": args.lr,
         """init_lr""": args.init_lr,
         """warmup_steps""": args.warmup_steps,
         """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
         """weight_decay""": args.weight_decay,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[Any]        =      Trainer(
         args=args,
         data_collator=data_collator,
         model_save_fn=model.save_pretrained,
         train_step_fn=train_step,
         val_step_fn=val_step,
         logger=logger,
         scheduler_fn=lr,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
     try:
          trainer.train(state, tr_dataset, val_dataset)
     except KeyboardInterrupt:
          print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

     print("""SAVING WEIGHTS IN `final-weights`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





