


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       _lowercase			:


       """simple docstring"""




       A__ 						=      42
       A__ 						=      field(default_factory=lowercase__)
       A__ 						=      field(default_factory=lowercase__)
       A__ 						=      field(default_factory=lowercase__)





       def 						lowerCAmelCase					(       self							:      Dict ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      Tensor ,				__lowerCamelCase							:      Tensor ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  len(list(m.modules()	)	) == 1 or isinstance(__lowerCamelCase ,				nn.Convad	) or isinstance(__lowerCamelCase ,				nn.BatchNormad	)
             if has_not_submodules:
                   self.traced.append(__lowerCamelCase	)
                   lowerCamelCase__		:    Tuple   =  m





       def __call__(       self							:      str ,				__lowerCamelCase							:      Tensor	):


             '''simple docstring'''



             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 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             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							:      Dict ,				__lowerCamelCase							:      nn.Module	):


             '''simple docstring'''



             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__		:    Optional[Any]   =  len(__lowerCamelCase	) + 1
                   feature_blocks.append((f"res{block_index}", v)	)

             lowerCamelCase__		:    List[str]   =  nn.ModuleDict(__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Any ,				__lowerCamelCase							:      Tensor	):


             '''simple docstring'''



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







class       _lowercase			(							lowercase__):


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''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 				lowercase_		(		_A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[int]   =  RegNetConfig(depths=[2, 7, 17, 1]   , hidden_sizes=[8, 8, 8, 8]   , groups_width=8						)
      if "in1k" in model_name:
            lowerCamelCase__		:    str   =  RegNetForImageClassification(_A						)
      else:
            lowerCamelCase__		:    Union[str, Any]   =  RegNetModel(_A						)
      # create from model (with small weights)
      lowerCamelCase__		:    Union[str, 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__		:    str   =  from_model.eval()
            lowerCamelCase__		:    Optional[int]   =  our_model.eval()

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

            pprint(dest_tracker.nameamodule						)
            lowerCamelCase__		:    Dict   =  Tracker(_A						)
            lowerCamelCase__		:    int   =  src_tracker(_A						).parametrized

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

      lowerCamelCase__		:    Dict   =  {}

      lowerCamelCase__		:    List[str]   =  to_params_dict(_A						)
      lowerCamelCase__		:    Any   =  to_params_dict(_A						)

      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__		:    Tuple   =  "classifier.1.weight"
            lowerCamelCase__		:    Tuple   =  "classifier.1.bias"

      return from_to_ours_keys

def 				lowercase_		(		_A      :   Path   , _A      :   str = None   , _A      :   bool = True						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[int]   =  "imagenet-1k-id2label.json"
      lowerCamelCase__		:    Any   =  1000

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

      lowerCamelCase__		:    str   =  idalabel
      lowerCamelCase__		:    Tuple   =  {v: k for k, v in idalabel.items()}

      lowerCamelCase__		:    Tuple   =  partial(_A   , num_labels=_A   , idalabel=_A   , labelaid=_A						)

      lowerCamelCase__		:    Union[str, 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(_A      :   str						) -> Tuple[Dict, Dict]:
            lowerCamelCase__		:    List[Any]   =  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
            lowerCamelCase__		:    Union[str, Any]   =  files["classy_state_dict"]["base_model"]["model"]
            return model_state_dict["trunk"], model_state_dict["heads"]

      lowerCamelCase__		:    List[Any]   =  {
          "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"   , ),
      }

      lowerCamelCase__		:    Dict   =  get_from_to_our_keys(_A						)

      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__		:    Tuple   =  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__		:    Union[str, Any]   =  {}

            lowerCamelCase__		:    Optional[int]   =  list(from_state_dict.keys()						)
            lowerCamelCase__		:    List[str]   =  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__		:    List[str]   =  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
                  lowerCamelCase__		:    Any   =  from_to_ours_keys[src_key]
                  # store the parameter with our key
                  lowerCamelCase__		:    int   =  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
            lowerCamelCase__		:    str   =  names_to_config[model_name]
            lowerCamelCase__		:    Dict   =  RegNetModel
            if "in1k" in model_name:
                  lowerCamelCase__		:    Optional[int]   =  RegNetForImageClassification
            lowerCamelCase__		:    Dict   =  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
            lowerCamelCase__		:    Optional[int]   =  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   , )
            lowerCamelCase__		:    Union[str, Any]   =  384
            # we can use the convnext one
            lowerCamelCase__		:    Optional[int]   =  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				:   List[str]		= 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				:   Tuple		= 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)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      "AutoFeatureExtractor"
       A__ 						=      "Speech2Text2Tokenizer"





       def __init__(       self							:      List[str] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str	):


             '''simple docstring'''



             super().__init__(__lowerCamelCase ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  self.feature_extractor
             lowerCamelCase__		:    Optional[int]   =  False





       def __call__(       self							:      List[str] ,				*__lowerCamelCase							:      Union[str, Any] ,				**__lowerCamelCase							:      List[Any]	):


             '''simple docstring'''



             if self._in_target_context_manager:
                   return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase	)

             if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."	)
                   lowerCamelCase__		:    List[Any]   =  kwargs.pop("raw_speech"	)
             else:
                   lowerCamelCase__		:    str   =  kwargs.pop("audio" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  kwargs.pop("sampling_rate" ,				__lowerCamelCase	)
             lowerCamelCase__		:    Union[str, Any]   =  kwargs.pop("text" ,				__lowerCamelCase	)
             if len(__lowerCamelCase	) > 0:
                   lowerCamelCase__		:    Dict   =  args[0]
                   lowerCamelCase__		:    int   =  args[1:]

             if audio is None and text is None:
                   raise ValueError("You need to specify either an `audio` or `text` input to process."	)

             if audio is not None:
                   lowerCamelCase__		:    str   =  self.feature_extractor(__lowerCamelCase ,				*__lowerCamelCase ,				sampling_rate=__lowerCamelCase ,				**__lowerCamelCase	)
             if text is not None:
                   lowerCamelCase__		:    Optional[int]   =  self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase	)

             if text is None:
                   return inputs
             elif audio is None:
                   return encodings
             else:
                   lowerCamelCase__		:    Optional[int]   =  encodings["input_ids"]
                   return inputs





       def 						lowerCAmelCase					(       self							:      Any ,				*__lowerCamelCase							:      Any ,				**__lowerCamelCase							:      Any	):


             '''simple docstring'''



             return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Dict ,				*__lowerCamelCase							:      int ,				**__lowerCamelCase							:      List[str]	):


             '''simple docstring'''



             return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase	)





       @contextmanager
       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             warnings.warn(
                 "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                 "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                 "your audio inputs, or in a separate call."	)
             lowerCamelCase__		:    int   =  True
             lowerCamelCase__		:    List[Any]   =  self.tokenizer
             yield
             lowerCamelCase__		:    Optional[int]   =  self.feature_extractor
             lowerCamelCase__		:    List[str]   =  False


