import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
snake_case__				= logging.get_logger(__name__)

snake_case__				= {
    '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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
snake_case__				= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      for attribute in key.split('''.'''		):
            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		)

      if weight_type is not None:
            _lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		).shape
      else:
            _lowerCamelCase			    =				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":
            _lowerCamelCase			    =				value
      elif weight_type == "weight_g":
            _lowerCamelCase			    =				value
      elif weight_type == "weight_v":
            _lowerCamelCase			    =				value
      elif weight_type == "bias":
            _lowerCamelCase			    =				value
      else:
            _lowerCamelCase			    =				value

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				fairseq_model.state_dict()

      _lowerCamelCase			    =				hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCamelCase			    =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						hf_model.config.feat_extract_norm == '''group''' ,						)
                  _lowerCamelCase			    =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
                              _lowerCamelCase			    =				True
                              if "*" in mapped_key:
                                    _lowerCamelCase			    =				name.split(__UpperCAmelCase		)[0].split('''.'''		)[-2]
                                    _lowerCamelCase			    =				mapped_key.replace('''*''' ,						__UpperCAmelCase		)
                              if "weight_g" in name:
                                    _lowerCamelCase			    =				'''weight_g'''
                              elif "weight_v" in name:
                                    _lowerCamelCase			    =				'''weight_v'''
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    _lowerCamelCase			    =				'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _lowerCamelCase			    =				'''weight'''
                              else:
                                    _lowerCamelCase			    =				None

                              set_recursively(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(__UpperCAmelCase		)

      logger.warning(F'Unused weights: {unused_weights}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				full_name.split('''conv_layers.'''		)[-1]
      _lowerCamelCase			    =				name.split('''.'''		)
      _lowerCamelCase			    =				int(items[0]		)
      _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				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."
                  )
                  _lowerCamelCase			    =				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.'
                  )
                  _lowerCamelCase			    =				value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(__UpperCAmelCase		)
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				torch.load(__UpperCAmelCase		)
      _lowerCamelCase			    =				WavLMConfigOrig(checkpoint['''cfg''']		)
      _lowerCamelCase			    =				WavLMOrig(__UpperCAmelCase		)
      model.load_state_dict(checkpoint['''model''']		)
      model.eval()

      if config_path is not None:
            _lowerCamelCase			    =				WavLMConfig.from_pretrained(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				WavLMConfig()

      _lowerCamelCase			    =				WavLMModel(__UpperCAmelCase		)

      recursively_load_weights(__UpperCAmelCase ,						__UpperCAmelCase		)

      hf_wavlm.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     snake_case__				= parser.parse_args()
     convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__				= logging.get_logger()



@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				len(list(m.modules()						)						) == 1 or isinstance(A_				,				nn.Convad						) or isinstance(A_				,				nn.BatchNormad						)
           if has_not_submodules:
                 self.traced.append(A_						)



     def __call__(					self				,				A_						) ->				Tuple:






           """simple docstring"""
           for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook						)						)
           self.module(A_						)
           [x.remove() for x in self.handles]
           return self





     @property
     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           # check the len of the state_dict keys to see if we have learnable params
           return list(filter(lambda A_						: len(list(x.state_dict().keys()						)						) > 0				,				self.traced						)						)





@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  42
     A_										=  0
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def __call__(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				Tracker(self.dest						)(A_						).parametrized
           _lowerCamelCase			    =				Tracker(self.src						)(A_						).parametrized

           _lowerCamelCase			    =				list(filter(lambda A_						: type(A_						) not in self.src_skip				,				A_						)						)
           _lowerCamelCase			    =				list(filter(lambda A_						: type(A_						) not in self.dest_skip				,				A_						)						)

           if len(A_						) != len(A_						):
                 raise Exception(
                     F'Numbers of operations are different. Source module has {len(A_						)} operations while'
                     F' destination module has {len(A_						)}.'						)

           for dest_m, src_m in zip(A_				,				A_						):
                 dest_m.load_state_dict(src_m.state_dict()						)
                 if self.verbose == 1:
                       print(F'Transfered from={src_m} to={dest_m}'						)





def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      print(F'Converting {name}...'		)
      with torch.no_grad():
            _lowerCamelCase			    =				timm.create_model(__UpperCAmelCase ,						pretrained=__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ResNetForImageClassification(__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ModuleTransfer(src=__UpperCAmelCase ,						dest=__UpperCAmelCase		)
            _lowerCamelCase			    =				torch.randn((1, 3, 224, 224)		)
            module_transfer(__UpperCAmelCase		)

      assert torch.allclose(from_model(__UpperCAmelCase		) ,						our_model(__UpperCAmelCase		).logits		), "The model logits don't match the original one."

      _lowerCamelCase			    =				F'resnet{"-".join(name.split("resnet"		)		)}'
      print(__UpperCAmelCase		)

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name ,						commit_message='''Add model''' ,						use_temp_dir=__UpperCAmelCase ,						)

            # we can use the convnext one
            _lowerCamelCase			    =				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		)
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name ,						commit_message='''Add image processor''' ,						use_temp_dir=__UpperCAmelCase ,						)

            print(F'Pushed {checkpoint_name}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase = None ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
      _lowerCamelCase			    =				1000
      _lowerCamelCase			    =				(1, num_labels)

      _lowerCamelCase			    =				'''huggingface/label-files'''
      _lowerCamelCase			    =				num_labels
      _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
      _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}

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

      _lowerCamelCase			    =				partial(__UpperCAmelCase ,						num_labels=__UpperCAmelCase ,						idalabel=__UpperCAmelCase ,						labelaid=__UpperCAmelCase		)

      _lowerCamelCase			    =				{
          '''resnet18''': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2] ,						hidden_sizes=[64, 128, 256, 512] ,						layer_type='''basic'''		),
          '''resnet26''': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet34''': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3] ,						hidden_sizes=[64, 128, 256, 512] ,						layer_type='''basic'''		),
          '''resnet50''': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet101''': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
          '''resnet152''': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3] ,						hidden_sizes=[256, 512, 1024, 2048] ,						layer_type='''bottleneck'''		),
      }

      if model_name:
            convert_weight_and_push(__UpperCAmelCase ,						names_to_config[model_name] ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      return config, expected_shape


if __name__ == "__main__":
     snake_case__				= 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 resnet* architecture,'
             ' currently: resnet18,26,34,50,101,152. 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.',
     )

     snake_case__				= parser.parse_args()
     snake_case__				= 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)