
'''simple docstring'''







import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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







@dataclass
class 		snake_case_       :



   """simple docstring"""





   __lowerCAmelCase	:							nn.Module
   __lowerCAmelCase	:							List[nn.Module]								=field(default_factory=A__						)
   __lowerCAmelCase	:							list								=field(default_factory=A__						)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      len(list(m.modules())) == 1 or isinstance(UpperCamelCase			,   nn.Convad) or isinstance(UpperCamelCase			,   nn.BatchNormad)
          if has_not_submodules:
                 if not isinstance(UpperCamelCase			,   UpperCamelCase):
                        self.traced.append(UpperCamelCase)







   def __call__( self			,   UpperCamelCase):
          for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook))
          self.module(UpperCamelCase)
          [x.remove() for x in self.handles]
          return self







   @property
   def     __UpperCAmelCase      ( self):
          # check the len of the state_dict keys to see if we have learnable params
          return list(filter(lambda UpperCamelCase: len(list(x.state_dict().keys())) > 0			,   self.traced))







@dataclass
class 		snake_case_       :



   """simple docstring"""





   __lowerCAmelCase	:							nn.Module
   __lowerCAmelCase	:							nn.Module
   __lowerCAmelCase	:							int								=0
   __lowerCAmelCase	:							List								=field(default_factory=A__						)
   __lowerCAmelCase	:							List								=field(default_factory=A__						)







   def __call__( self			,   UpperCamelCase):
          lowerCamelCase__				   =      Tracker(self.dest)(UpperCamelCase).parametrized
          lowerCamelCase__				   =      Tracker(self.src)(UpperCamelCase).parametrized

          lowerCamelCase__				   =      list(filter(lambda UpperCamelCase: type(UpperCamelCase) not in self.src_skip			,   UpperCamelCase))
          lowerCamelCase__				   =      list(filter(lambda UpperCamelCase: type(UpperCamelCase) not in self.dest_skip			,   UpperCamelCase))

          if len(UpperCamelCase) != len(UpperCamelCase):
                 raise Exception(
                     f"""Numbers of operations are different. Source module has {len(UpperCamelCase)} operations while"""
                     f""" destination module has {len(UpperCamelCase)}.""")

          for dest_m, src_m in zip(UpperCamelCase			,   UpperCamelCase):
                 dest_m.load_state_dict(src_m.state_dict())
                 if self.verbose == 1:
                        print(f"""Transfered from={src_m} to={dest_m}""")







def   lowerCAmelCase(  a__    :  nn.Module      , a__    :  nn.Module      ):







       '''simple docstring'''






       lowerCamelCase__				   =      from_model.state_dict()
       lowerCamelCase__				   =      our_model.state_dict()
       lowerCamelCase__				   =      our_model.config
       lowerCamelCase__				   =      []
       for stage_idx in range(len(config.hidden_sizes      )      ):
              for block_id in range(config.depths[stage_idx]      ):
                     lowerCamelCase__				   =      f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
                     lowerCamelCase__				   =      f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

                     all_keys.append((from_key, to_key)      )
                     lowerCamelCase__				   =      f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
                     lowerCamelCase__				   =      f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

                     all_keys.append((from_key, to_key)      )

       for from_key, to_key in all_keys:
              lowerCamelCase__				   =      from_state_dict.pop(a__      )

       our_model.load_state_dict(a__      )
       return our_model







def   lowerCAmelCase(  a__    :  str      , a__    :  VanConfig      , a__    :  str      , a__    :  nn.Module      , a__    :  Path      , a__    :  bool = True      , ):







       '''simple docstring'''






       print(f"""Downloading weights for {name}..."""      )
       lowerCamelCase__				   =      cached_download(a__      )
       print(f"""Converting {name}..."""      )
       lowerCamelCase__				   =      torch.load(a__      )["state_dict"]
       from_model.load_state_dict(a__      )
       from_model.eval()
       with torch.no_grad():
              lowerCamelCase__				   =      VanForImageClassification(a__      ).eval()
              lowerCamelCase__				   =      ModuleTransfer(src=a__      , dest=a__      )
              lowerCamelCase__				   =      torch.randn((1, 3, 224, 224)      )
              module_transfer(a__      )
              lowerCamelCase__				   =      copy_parameters(a__      , a__      )

       if not torch.allclose(from_model(a__      )      , our_model(a__      ).logits      ):
              raise ValueError("The model logits don't match the original one."      )

       lowerCamelCase__				   =      name
       print(a__      )

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

              # 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=a__      , )

              print(f"""Pushed {checkpoint_name}"""      )







def   lowerCAmelCase(  a__    :  Path      , a__    :  str = None      , a__    :  bool = True      ):







       '''simple docstring'''






       lowerCamelCase__				   =      "imagenet-1k-id2label.json"
       lowerCamelCase__				   =      1000

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

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

       lowerCamelCase__				   =      partial(a__      , num_labels=a__      , idalabel=a__      , labelaid=a__      )

       lowerCamelCase__				   =      {
           "van-tiny": ImageNetPreTrainedConfig(
               hidden_sizes=[32, 64, 160, 256]      , depths=[3, 3, 5, 2]      , mlp_ratios=[8, 8, 4, 4]      , ),
           "van-small": ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]      , depths=[2, 2, 4, 2]      , mlp_ratios=[8, 8, 4, 4]      , ),
           "van-base": ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]      , depths=[3, 3, 12, 3]      , mlp_ratios=[8, 8, 4, 4]      , ),
           "van-large": ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]      , depths=[3, 5, 27, 3]      , mlp_ratios=[8, 8, 4, 4]      , ),
       }

       lowerCamelCase__				   =      {
           "van-tiny": van_tiny,
           "van-small": van_small,
           "van-base": van_base,
           "van-large": van_large,
       }

       lowerCamelCase__				   =      {
           "van-tiny": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
           ),
           "van-small": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
           ),
           "van-base": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
           ),
           "van-large": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
           ),
       }

       if model_name:
              convert_weight_and_push(
                  a__      , names_to_config[model_name]      , checkpoint=names_to_original_checkpoints[model_name]      , from_model=names_to_original_models[model_name]()      , save_directory=a__      , push_to_hub=a__      , )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         a__      , a__      , checkpoint=names_to_original_checkpoints[model_name]      , from_model=names_to_original_models[model_name]()      , save_directory=a__      , push_to_hub=a__      , )


if __name__ == "__main__":
    lowerCAmelCase_      =   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: van-tiny/small/base/large. 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(
        "--van_dir",
        required=True,
        type=Path,
        help=(
            "A path to VAN's original implementation directory. You can download from here:"
            " https://github.com/Visual-Attention-Network/VAN-Classification"
        ),
    )
    parser.add_argument(
        "--push_to_hub",
        default=True,
        type=bool,
        required=False,
        help="If True, push model and image processor to the hub.",
    )

    lowerCAmelCase_      =   parser.parse_args()
    lowerCAmelCase_      =   args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    lowerCAmelCase_      =   args.van_dir
    # append the path to the parents to maskformer dir
    sys.path.append(str(van_dir.parent))
    from van.models.van import van_base, van_large, van_small, van_tiny

    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase_      =   {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  List[str]      , a__    :  Optional[int]      , a__    :  Optional[Any]      , a__    :  List[Any]      ):







       '''simple docstring'''






       for attribute in key.split("."      ):
              lowerCamelCase__				   =      getattr(a__      , a__      )

       if weight_type is not None:
              lowerCamelCase__				   =      getattr(a__      , a__      ).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   lowerCAmelCase(  a__    :  Any      , a__    :  List[str]      , a__    :  str      ):







       '''simple docstring'''






       lowerCamelCase__				   =      []
       lowerCamelCase__				   =      fairseq_model.state_dict()

       lowerCamelCase__				   =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCamelCase__				   =      False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__      , a__      , a__      , a__      , hf_model.config.feat_extract_norm == "group"      , )
                     lowerCamelCase__				   =      True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowerCamelCase__				   =      "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                            if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                                   lowerCamelCase__				   =      True
                                   if "*" in mapped_key:
                                          lowerCamelCase__				   =      name.split(a__      )[0].split("."      )[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          lowerCamelCase__				   =      mapped_key.replace("*"      , a__      )
                                   if "weight_g" in name:
                                          lowerCamelCase__				   =      "weight_g"
                                   elif "weight_v" in name:
                                          lowerCamelCase__				   =      "weight_v"
                                   elif "weight" in name:
                                          lowerCamelCase__				   =      "weight"
                                   elif "bias" in name:
                                          lowerCamelCase__				   =      "bias"
                                   else:
                                          lowerCamelCase__				   =      None
                                   set_recursively(a__      , a__      , a__      , a__      , a__      )
                            continue
              if not is_used:
                     unused_weights.append(a__      )

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







def   lowerCAmelCase(  a__    :  Optional[Any]      , a__    :  Dict      , a__    :  List[Any]      , a__    :  Union[str, Any]      , a__    :  List[str]      ):







       '''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(a__      )







def   lowerCAmelCase(  a__    :  Optional[int]      , a__    :  Tuple      ):







       '''simple docstring'''






       lowerCamelCase__				   =      SEWDConfig()
       if is_finetuned:
              lowerCamelCase__				   =      model.wav_encoder.wav_model.cfg
       else:
              lowerCamelCase__				   =      model.cfg

       lowerCamelCase__				   =      fs_config.conv_bias
       lowerCamelCase__				   =      eval(fs_config.conv_feature_layers      )
       lowerCamelCase__				   =      [x[0] for x in conv_layers]
       lowerCamelCase__				   =      [x[1] for x in conv_layers]
       lowerCamelCase__				   =      [x[2] for x in conv_layers]
       lowerCamelCase__				   =      "gelu"
       lowerCamelCase__				   =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
       lowerCamelCase__				   =      0.0
       lowerCamelCase__				   =      fs_config.activation_fn.name
       lowerCamelCase__				   =      fs_config.encoder_embed_dim
       lowerCamelCase__				   =      0.0_2
       lowerCamelCase__				   =      fs_config.encoder_ffn_embed_dim
       lowerCamelCase__				   =      1E-5
       lowerCamelCase__				   =      fs_config.encoder_layerdrop
       lowerCamelCase__				   =      fs_config.encoder_attention_heads
       lowerCamelCase__				   =      fs_config.conv_pos_groups
       lowerCamelCase__				   =      fs_config.conv_pos
       lowerCamelCase__				   =      len(a__      )
       lowerCamelCase__				   =      fs_config.encoder_layers
       lowerCamelCase__				   =      fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       lowerCamelCase__				   =      fs_config.max_position_embeddings
       lowerCamelCase__				   =      fs_config.position_buckets
       lowerCamelCase__				   =      fs_config.share_att_key
       lowerCamelCase__				   =      fs_config.relative_attention
       lowerCamelCase__				   =      fs_config.position_biased_input
       lowerCamelCase__				   =      tuple(fs_config.pos_att_type.split("|"      )      )
       lowerCamelCase__				   =      fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              lowerCamelCase__				   =      model.cfg
              lowerCamelCase__				   =      fs_config.final_dropout
              lowerCamelCase__				   =      fs_config.layerdrop
       lowerCamelCase__				   =      fs_config.activation_dropout
       lowerCamelCase__				   =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowerCamelCase__				   =      fs_config.attention_dropout
       lowerCamelCase__				   =      fs_config.dropout_input
       lowerCamelCase__				   =      fs_config.dropout
       lowerCamelCase__				   =      fs_config.mask_channel_length
       lowerCamelCase__				   =      fs_config.mask_channel_prob
       lowerCamelCase__				   =      fs_config.mask_length
       lowerCamelCase__				   =      fs_config.mask_prob

       lowerCamelCase__				   =      "Wav2Vec2FeatureExtractor"
       lowerCamelCase__				   =      "Wav2Vec2CTCTokenizer"

       return config







@torch.no_grad()
def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  int      , a__    :  Any=None      , a__    :  Any=None      , a__    :  Any=True      ):







       '''simple docstring'''






       if is_finetuned:
              lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]      , arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
       else:
              lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

       if config_path is not None:
              lowerCamelCase__				   =      SEWDConfig.from_pretrained(a__      )
       else:
              lowerCamelCase__				   =      convert_config(model[0]      , a__      )
       lowerCamelCase__				   =      model[0].eval()

       lowerCamelCase__				   =      True if config.feat_extract_norm == "layer" else False
       lowerCamelCase__				   =      WavaVecaFeatureExtractor(
           feature_size=1      , sampling_rate=1_6000      , padding_value=0      , do_normalize=a__      , return_attention_mask=a__      , )

       if is_finetuned:
              if dict_path:
                     lowerCamelCase__				   =      Dictionary.load(a__      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCamelCase__				   =      target_dict.pad_index
                     lowerCamelCase__				   =      target_dict.bos_index
                     lowerCamelCase__				   =      target_dict.pad_index
                     lowerCamelCase__				   =      target_dict.bos_index
                     lowerCamelCase__				   =      target_dict.eos_index
                     lowerCamelCase__				   =      len(target_dict.symbols      )
                     lowerCamelCase__				   =      os.path.join(a__      , "vocab.json"      )
                     if not os.path.isdir(a__      ):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a__      )      )
                            return
                     os.makedirs(a__      , exist_ok=a__      )
                     with open(a__      , "w"      , encoding="utf-8"      ) as vocab_handle:
                            json.dump(target_dict.indices      , a__      )
                     lowerCamelCase__				   =      WavaVecaCTCTokenizer(
                         a__      , unk_token=target_dict.unk_word      , pad_token=target_dict.pad_word      , bos_token=target_dict.bos_word      , eos_token=target_dict.eos_word      , word_delimiter_token="|"      , do_lower_case=a__      , )
                     lowerCamelCase__				   =      WavaVecaProcessor(feature_extractor=a__      , tokenizer=a__      )
                     processor.save_pretrained(a__      )

              lowerCamelCase__				   =      SEWDForCTC(a__      )
       else:
              lowerCamelCase__				   =      SEWDModel(a__      )
              feature_extractor.save_pretrained(a__      )

       recursively_load_weights(a__      , a__      , a__      )

       hf_model.save_pretrained(a__      )


if __name__ == "__main__":
    lowerCAmelCase_      =   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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    lowerCAmelCase_      =   parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )

