



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					:
				__lowerCamelCase					=							42
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_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		,							_snake_case						):



									"""simple docstring"""





									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 				snake_case  (				self						):



									"""simple docstring"""





									return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}





class    __lowerCAmelCase					(	nn.Module			):




				def __init__(				self		,							_snake_case						):



									"""simple docstring"""





									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 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





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





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def 				snake_case  (				self						):



									"""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 						_UpperCAmelCase							( snake_case     ):





					"""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(snake_case     )
					else:
										_lowerCAmelCase         =       RegNetModel(snake_case     )
					# 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(snake_case     )
										_lowerCAmelCase         =       dest_tracker(snake_case     ).parametrized

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

					# convert the keys -> module dict to keys -> params
					def to_params_dict(snake_case     ):
										_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(snake_case     )
					_lowerCAmelCase         =       to_params_dict(snake_case     )

					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							( snake_case      ,		snake_case = None      ,		snake_case = 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(snake_case      ,		snake_case      ,		repo_type="""dataset"""     )     )      ,		"""r"""     )     )
					_lowerCAmelCase         =       {int(snake_case     ): v for k, v in idalabel.items()}

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

					_lowerCAmelCase         =       partial(snake_case      ,		num_labels=snake_case      ,		idalabel=snake_case      ,		labelaid=snake_case     )

					_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(snake_case     ) -> Tuple[Dict, Dict]:
										_lowerCAmelCase         =       torch.hub.load_state_dict_from_url(snake_case      ,		model_dir=str(snake_case     )      ,		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(
					        snake_case      ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""      ,		),
					    """regnet-y-10b-seer-in1k""": partial(
					        snake_case      ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""      ,		),
					}

					_lowerCAmelCase         =       get_from_to_our_keys(snake_case     )

					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(snake_case      ,		""""""      ,		snake_case     )
															# 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(snake_case     )
										# check that all keys have been updated
										assert len(snake_case     ) == 0, F'Some keys where not used {",".join(snake_case     )}'

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

										# save our state dict to disk
										torch.save(snake_case      ,		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(snake_case     )
										# 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(
										    snake_case      ,		snake_case      ,		[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=snake_case     )
										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 saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase_			:			List[str]    	=   logging.get_logger(__name__)

lowerCAmelCase_			:			Optional[Any]    	=   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}


def __a			(						__lowerCamelCase      :    Dict			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    int			,		__lowerCamelCase      :    Union[str, Any]					)						->				Optional[Any]:
      '''simple docstring'''


      for attribute in key.split("."					):
            lowercase_     			=   getattr(__lowerCamelCase			,		__lowerCamelCase					)

      if weight_type is not None:
            lowercase_     			=   getattr(__lowerCamelCase			,		__lowerCamelCase					).shape
      else:
            lowercase_     			=   hf_pointer.shape

      assert hf_shape == value.shape, (
          f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
          f' {value.shape} for {full_name}'
      )

      if weight_type == "weight":
            lowercase_     			=   value
      elif weight_type == "weight_g":
            lowercase_     			=   value
      elif weight_type == "weight_v":
            lowercase_     			=   value
      elif weight_type == "bias":
            lowercase_     			=   value
      else:
            lowercase_     			=   value

      logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'					)


def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Optional[int]					)						->				List[Any]:
      '''simple docstring'''


      lowercase_     			=   []
      lowercase_     			=   fairseq_model.state_dict()

      lowercase_     			=   hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowercase_     			=   False
            if "conv_layers" in name:
                  load_conv_layer(
                      __lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		hf_model.config.feat_extract_norm == "group"			,		)
                  lowercase_     			=   True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowercase_     			=   mapped_key

                        if key in name:
                              lowercase_     			=   True
                              if "*" in mapped_key:
                                    lowercase_     			=   name.split(__lowerCamelCase					)[0].split("."					)[-2]
                                    lowercase_     			=   mapped_key.replace("*"			,		__lowerCamelCase					)
                              if "weight_g" in name:
                                    lowercase_     			=   "weight_g"
                              elif "weight_v" in name:
                                    lowercase_     			=   "weight_v"
                              elif "weight" in name:
                                    lowercase_     			=   "weight"
                              elif "bias" in name:
                                    lowercase_     			=   "bias"
                              else:
                                    lowercase_     			=   None
                              set_recursively(__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase					)
                        continue
            if not is_used:
                  unused_weights.append(__lowerCamelCase					)

      logger.warning(f'Unused weights: {unused_weights}'					)


def __a			(						__lowerCamelCase      :    Any			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Optional[Any]					)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   full_name.split("conv_layers."					)[-1]
      lowercase_     			=   name.split("."					)
      lowercase_     			=   int(items[0]					)
      lowercase_     			=   int(items[1]					)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                      " found."
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f'{full_name} has size {value.shape}, but'
                      f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
      else:
            unused_weights.append(__lowerCamelCase					)


def __a			(						__lowerCamelCase      :    Optional[int]					)						->				Dict:
      '''simple docstring'''


      lowercase_     			=   HubertConfig()
      lowercase_     			=   model.config

      lowercase_     			=   fs_config.activation_dropout
      lowercase_     			=   False
      lowercase_     			=   fs_config.attention_dropout
      lowercase_     			=   False
      lowercase_     			=   eval(fs_config.extractor_conv_feature_layers					)
      lowercase_     			=   [x[0] for x in conv_layers]
      lowercase_     			=   [x[1] for x in conv_layers]
      lowercase_     			=   [x[2] for x in conv_layers]
      lowercase_     			=   "gelu"
      lowercase_     			=   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
      lowercase_     			=   False
      lowercase_     			=   0.0
      lowercase_     			=   0.0
      lowercase_     			=   fs_config.activation_fn
      lowercase_     			=   fs_config.dropout
      lowercase_     			=   fs_config.encoder_embed_dim
      lowercase_     			=   0.0_2
      lowercase_     			=   fs_config.encoder_ffn_embed_dim
      lowercase_     			=   1e-5
      lowercase_     			=   0.0
      lowercase_     			=   fs_config.encoder_attention_heads
      lowercase_     			=   fs_config.conv_pos_groups
      lowercase_     			=   fs_config.conv_pos
      lowercase_     			=   len(__lowerCamelCase					)
      lowercase_     			=   fs_config.encoder_layers

      return config


@torch.no_grad()
def __a			(						__lowerCamelCase      :    Optional[Any]			,		__lowerCamelCase      :    Any=None					)						->				Any:
      '''simple docstring'''


      lowercase_     			=   distilhubert().model.model

      if config_path is not None:
            lowercase_     			=   HubertConfig.from_pretrained(__lowerCamelCase					)
      else:
            lowercase_     			=   convert_config(__lowerCamelCase					)
      lowercase_     			=   model.eval()

      lowercase_     			=   WavaVecaFeatureExtractor(
          feature_size=1			,		sampling_rate=16_000			,		padding_value=0			,		do_normalize=__lowerCamelCase			,		return_attention_mask=__lowerCamelCase			,		)
      lowercase_     			=   HubertModel(__lowerCamelCase					)

      recursively_load_weights(__lowerCamelCase			,		__lowerCamelCase					)

      feature_extractor.save_pretrained(__lowerCamelCase					)
      hf_model.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
       lowerCAmelCase_			:			str    	=   argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_			:			str    	=   parser.parse_args()
       convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






