
'''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 			lowerCAmelCase__ :



  '''simple docstring'''






  _UpperCamelCase							=			42
  _UpperCamelCase							=			field(default_factory=UpperCamelCase							)
  _UpperCamelCase							=			field(default_factory=UpperCamelCase							)
  _UpperCamelCase							=			field(default_factory=UpperCamelCase							)

  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Tensor						,			lowerCAmelCase_	:			Tensor						,			lowerCAmelCase_	:			str     ) ->							int:
        snake_case_    							=				len(list(m.modules()     )     ) == 1 or isinstance(lowerCAmelCase_						,			nn.Convad     ) or isinstance(lowerCAmelCase_						,			nn.BatchNormad     )
        if has_not_submodules:
              self.traced.append(lowerCAmelCase_     )
              snake_case_    							=				m

  def __call__(self	:			int						,			lowerCAmelCase_	:			Tensor     ) ->							Optional[Any]:
        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	:			List[str]     ) ->							Any:
        # 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 			lowerCAmelCase__ (							nn.Module							):



  '''simple docstring'''






  def __init__(self	:			int						,			lowerCAmelCase_	:			nn.Module     ) ->							int:
        super().__init__()

        snake_case_    							=				[]
        # - 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}'''
              snake_case_    							=				len(lowerCAmelCase_     ) + 1
              feature_blocks.append((f'''res{block_index}''', v)     )

        snake_case_    							=				nn.ModuleDict(lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Tensor     ) ->							int:
        return get_trunk_forward_outputs(
            lowerCAmelCase_						,			out_feat_keys=lowerCAmelCase_						,			feature_blocks=self._feature_blocks						,			)



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			str     ) ->							Tuple:
        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      ):
      snake_case_    							=				RegNetConfig(depths=[2, 7, 17, 1]			,       hidden_sizes=[8, 8, 8, 8]			,       groups_width=8      )
      if "in1k" in model_name:
            snake_case_    							=				RegNetForImageClassification(_a      )
      else:
            snake_case_    							=				RegNetModel(_a      )
      # create from model (with small weights)
      snake_case_    							=				FakeRegNetVisslWrapper(
          RegNet(FakeRegNetParams(depth=27			,       group_width=1_010			,       w_a=1_744			,       w_a=6_2_0.8_3			,       w_m=2.5_2      )      )      )

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

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

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

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

      snake_case_    							=				{}

      snake_case_    							=				to_params_dict(_a      )
      snake_case_    							=				to_params_dict(_a      )

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

      return from_to_ours_keys
def 	snake_case_							(				_a  :				Path			,       _a  :				str = None			,       _a  :				bool = True      ):
      snake_case_    							=				"imagenet-1k-id2label.json"
      snake_case_    							=				1_000

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

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

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

      snake_case_    							=				{
          "regnet-y-10b-seer": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]			,       hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,       groups_width=1_010      ),
          # finetuned on imagenet
          "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]			,       hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,       groups_width=1_010      ),
      }

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

      snake_case_    							=				{
          "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"			,       ),
      }

      snake_case_    							=				get_from_to_our_keys(_a      )

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

            snake_case_    							=				{}

            snake_case_    							=				list(from_state_dict.keys()      )
            snake_case_    							=				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
                  snake_case_    							=				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
                  snake_case_    							=				from_to_ours_keys[src_key]
                  # store the parameter with our key
                  snake_case_    							=				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
            snake_case_    							=				names_to_config[model_name]
            snake_case_    							=				RegNetModel
            if "in1k" in model_name:
                  snake_case_    							=				RegNetForImageClassification
            snake_case_    							=				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
            snake_case_    							=				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			,       )
            snake_case_    							=				384
            # we can use the convnext one
            snake_case_    							=				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'''





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''      )

      if hf_config.use_weighted_layer_sum:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


if __name__ == "__main__":
     A							     = argparse.ArgumentParser()
     parser.add_argument(
         "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
     )
     parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
     parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
     A							     = parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

