'''simple docstring'''


import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  print("""Loading config file..."""						)

  def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_="" ,	SCREAMING_SNAKE_CASE_="."						):
    lowercase_     =       []
    for k, v in d.items():
      lowercase_     =       parent_key + sep + k if parent_key else k
      if isinstance(SCREAMING_SNAKE_CASE_ ,	collections.abc.MutableMapping						):
        items.extend(flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	sep=SCREAMING_SNAKE_CASE_						).items()						)
      else:
        items.append((new_key, v)						)
    return dict(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       argparse.Namespace()
  with open(SCREAMING_SNAKE_CASE_ ,	"""r"""						) as yaml_file:
    try:
      lowercase_     =       yaml.load(SCREAMING_SNAKE_CASE_ ,	Loader=yaml.FullLoader						)

      lowercase_     =       flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_						)
      for k, v in flat_cfg.items():
        setattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    except yaml.YAMLError as exc:
      logger.error("""Error while loading config file: {}. Error message: {}""".format(SCREAMING_SNAKE_CASE_ ,	str(SCREAMING_SNAKE_CASE_						)						)						)
  return config

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       MobileViTVaConfig()

  lowercase_     =       False

  # dataset
  if task_name.startswith("""imagenet1k_"""						):
    lowercase_     =       10_00
    if int(task_name.strip().split("""_"""						)[-1]						) == 3_84:
      lowercase_     =       3_84
    else:
      lowercase_     =       2_56
    lowercase_     =       """imagenet-1k-id2label.json"""
  elif task_name.startswith("""imagenet21k_to_1k_"""						):
    lowercase_     =       2_10_00
    if int(task_name.strip().split("""_"""						)[-1]						) == 3_84:
      lowercase_     =       3_84
    else:
      lowercase_     =       2_56
    lowercase_     =       """imagenet-22k-id2label.json"""
  elif task_name.startswith("""ade20k_"""						):
    lowercase_     =       1_51
    lowercase_     =       5_12
    lowercase_     =       """ade20k-id2label.json"""
    lowercase_     =       True
  elif task_name.startswith("""voc_"""						):
    lowercase_     =       21
    lowercase_     =       5_12
    lowercase_     =       """pascal-voc-id2label.json"""
    lowercase_     =       True

  # orig_config
  lowercase_     =       load_orig_config_file(SCREAMING_SNAKE_CASE_						)
  assert getattr(SCREAMING_SNAKE_CASE_ ,	"""model.classification.name""" ,	-1						) == "mobilevit_v2", "Invalid model"
  lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	"""model.classification.mitv2.width_multiplier""" ,	1.0						)
  assert (
      getattr(SCREAMING_SNAKE_CASE_ ,	"""model.classification.mitv2.attn_norm_layer""" ,	-1						) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	"""model.classification.activation.name""" ,	"""swish"""						)
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	"""model.segmentation.output_stride""" ,	16						)
    if "_deeplabv3" in task_name:
      lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	"""model.segmentation.deeplabv3.aspp_rates""" ,	[12, 24, 36]						)
      lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	"""model.segmentation.deeplabv3.aspp_out_channels""" ,	5_12						)
      lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	"""model.segmentation.deeplabv3.aspp_dropout""" ,	0.1						)

    # id2label
  lowercase_     =       """huggingface/label-files"""
  lowercase_     =       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	repo_type="""dataset"""						) ,	"""r"""						)						)
  lowercase_     =       {int(SCREAMING_SNAKE_CASE_						): v for k, v in idalabel.items()}
  lowercase_     =       idalabel
  lowercase_     =       {v: k for k, v in idalabel.items()}

  return config

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       dct.pop(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       val

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=False						)			->Dict:
  if base_model:
    lowercase_     =       """"""
  else:
    lowercase_     =       """mobilevitv2."""

  lowercase_     =       []
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      lowercase_     =       k[8:]
    else:
      lowercase_     =       k

    if ".block." in k:
      lowercase_     =       k_new.replace(""".block.""" ,	"""."""						)
    if ".conv." in k:
      lowercase_     =       k_new.replace(""".conv.""" ,	""".convolution."""						)
    if ".norm." in k:
      lowercase_     =       k_new.replace(""".norm.""" ,	""".normalization."""						)

    if "conv_1." in k:
      lowercase_     =       k_new.replace("""conv_1.""" ,	f"""{model_prefix}conv_stem."""						)
    for i in [1, 2]:
      if f"""layer_{i}.""" in k:
        lowercase_     =       k_new.replace(f"""layer_{i}.""" ,	f"""{model_prefix}encoder.layer.{i-1}.layer."""						)
    if ".exp_1x1." in k:
      lowercase_     =       k_new.replace(""".exp_1x1.""" ,	""".expand_1x1."""						)
    if ".red_1x1." in k:
      lowercase_     =       k_new.replace(""".red_1x1.""" ,	""".reduce_1x1."""						)

    for i in [3, 4, 5]:
      if f"""layer_{i}.0.""" in k:
        lowercase_     =       k_new.replace(f"""layer_{i}.0.""" ,	f"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""						)
      if f"""layer_{i}.1.local_rep.0.""" in k:
        lowercase_     =       k_new.replace(f"""layer_{i}.1.local_rep.0.""" ,	f"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""						)
      if f"""layer_{i}.1.local_rep.1.""" in k:
        lowercase_     =       k_new.replace(f"""layer_{i}.1.local_rep.1.""" ,	f"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""						)

    for i in [3, 4, 5]:
      if i == 3:
        lowercase_     =       [0, 1]
      elif i == 4:
        lowercase_     =       [0, 1, 2, 3]
      elif i == 5:
        lowercase_     =       [0, 1, 2]

      for j in j_in:
        if f"""layer_{i}.1.global_rep.{j}.""" in k:
          lowercase_     =       k_new.replace(
              f"""layer_{i}.1.global_rep.{j}.""" ,	f"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""						)
      if f"""layer_{i}.1.global_rep.{j+1}.""" in k:
        lowercase_     =       k_new.replace(
            f"""layer_{i}.1.global_rep.{j+1}.""" ,	f"""{model_prefix}encoder.layer.{i-1}.layernorm."""						)

      if f"""layer_{i}.1.conv_proj.""" in k:
        lowercase_     =       k_new.replace(f"""layer_{i}.1.conv_proj.""" ,	f"""{model_prefix}encoder.layer.{i-1}.conv_projection."""						)

    if "pre_norm_attn.0." in k:
      lowercase_     =       k_new.replace("""pre_norm_attn.0.""" ,	"""layernorm_before."""						)
    if "pre_norm_attn.1." in k:
      lowercase_     =       k_new.replace("""pre_norm_attn.1.""" ,	"""attention."""						)
    if "pre_norm_ffn.0." in k:
      lowercase_     =       k_new.replace("""pre_norm_ffn.0.""" ,	"""layernorm_after."""						)
    if "pre_norm_ffn.1." in k:
      lowercase_     =       k_new.replace("""pre_norm_ffn.1.""" ,	"""ffn.conv1."""						)
    if "pre_norm_ffn.3." in k:
      lowercase_     =       k_new.replace("""pre_norm_ffn.3.""" ,	"""ffn.conv2."""						)

    if "classifier.1." in k:
      lowercase_     =       k_new.replace("""classifier.1.""" ,	"""classifier."""						)

    if "seg_head." in k:
      lowercase_     =       k_new.replace("""seg_head.""" ,	"""segmentation_head."""						)
    if ".aspp_layer." in k:
      lowercase_     =       k_new.replace(""".aspp_layer.""" ,	"""."""						)
    if ".aspp_pool." in k:
      lowercase_     =       k_new.replace(""".aspp_pool.""" ,	"""."""						)

    rename_keys.append((k, k_new)						)
  return rename_keys

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       []
  for k in state_dict.keys():
    if k.startswith("""seg_head.aux_head."""						):
      keys_to_ignore.append(SCREAMING_SNAKE_CASE_						)
  for k in keys_to_ignore:
    state_dict.pop(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

def   A_      (       )			->str:
  lowercase_     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
  lowercase_     =       Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,	stream=SCREAMING_SNAKE_CASE_						).raw						)
  return im

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       get_mobilevitva_config(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # load original state_dict
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)

  # load huggingface model
  if task_name.startswith("""ade20k_"""						) or task_name.startswith("""voc_"""						):
    lowercase_     =       MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_						).eval()
    lowercase_     =       False
  else:
    lowercase_     =       MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_						).eval()
    lowercase_     =       False

  # remove and rename some keys of load the original model
  lowercase_     =       checkpoint
  remove_unused_keys(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       create_rename_keys(SCREAMING_SNAKE_CASE_ ,	base_model=SCREAMING_SNAKE_CASE_						)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  # load modified state_dict
  model.load_state_dict(SCREAMING_SNAKE_CASE_						)

  # Check outputs on an image, prepared by MobileViTImageProcessor
  lowercase_     =       MobileViTImageProcessor(crop_size=config.image_size ,	size=config.image_size + 32						)
  lowercase_     =       image_processor(images=prepare_img() ,	return_tensors="""pt"""						)
  lowercase_     =       model(**SCREAMING_SNAKE_CASE_						)

  # verify classification model
  if task_name.startswith("""imagenet"""						):
    lowercase_     =       outputs.logits
    lowercase_     =       logits.argmax(-1						).item()
    print("""Predicted class:""" ,	model.config.idalabel[predicted_class_idx]						)
    if task_name.startswith("""imagenet1k_256"""						) and config.width_multiplier == 1.0:
      # expected_logits for base variant
      lowercase_     =       torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]						)
      assert torch.allclose(logits[0, :3] ,	SCREAMING_SNAKE_CASE_ ,	atol=1e-4						)

  Path(SCREAMING_SNAKE_CASE_						).mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
  print(f"""Saving model {task_name} to {pytorch_dump_folder_path}"""						)
  model.save_pretrained(SCREAMING_SNAKE_CASE_						)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--task""",
           default="""imagenet1k_256""",
           type=str,
           help=(
               """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
               """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
           ),
           choices=[
               """imagenet1k_256""",
               """imagenet1k_384""",
               """imagenet21k_to_1k_256""",
               """imagenet21k_to_1k_384""",
               """ade20k_deeplabv3""",
               """voc_deeplabv3""",
           ],
       )

       parser.add_argument(
           """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
       )
       parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
       parser.add_argument(
           """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
       )

       __snake_case						  =		parser.parse_args()
       convert_mobilevitva_checkpoint(
           args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
       )


'''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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__snake_case						  =		{
    """post_extract_proj""": """feature_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.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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  for attribute in key.split("""."""						):
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  if weight_type is not None:
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						).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_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       []
  lowercase_     =       fairseq_model.state_dict()

  lowercase_     =       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    lowercase_     =       False
    if "conv_layers" in name:
      load_conv_layer(
          SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	hf_model.config.feat_extract_norm == """group""" ,	)
      lowercase_     =       True
    else:
      for key, mapped_key in MAPPING.items():
        lowercase_     =       """sew.""" + 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]:
          lowercase_     =       True
          if "*" in mapped_key:
            lowercase_     =       name.split(SCREAMING_SNAKE_CASE_						)[0].split("""."""						)[-2]
            lowercase_     =       mapped_key.replace("""*""" ,	SCREAMING_SNAKE_CASE_						)
          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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
        continue
    if not is_used:
      unused_weights.append(SCREAMING_SNAKE_CASE_						)

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  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(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       SEWConfig()
  if is_finetuned:
    lowercase_     =       model.wav_encoder.wav_model.cfg
  else:
    lowercase_     =       model.cfg

  lowercase_     =       fs_config.conv_bias
  lowercase_     =       eval(fs_config.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_     =       0.0
  lowercase_     =       fs_config.activation_fn.name
  lowercase_     =       fs_config.encoder_embed_dim
  lowercase_     =       0.02
  lowercase_     =       fs_config.encoder_ffn_embed_dim
  lowercase_     =       1e-5
  lowercase_     =       fs_config.encoder_layerdrop
  lowercase_     =       fs_config.encoder_attention_heads
  lowercase_     =       fs_config.conv_pos_groups
  lowercase_     =       fs_config.conv_pos
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       fs_config.encoder_layers
  lowercase_     =       fs_config.squeeze_factor

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

  lowercase_     =       """Wav2Vec2FeatureExtractor"""
  lowercase_     =       """Wav2Vec2CTCTokenizer"""

  return config

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=True						)			->Optional[Any]:

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

  if config_path is not None:
    lowercase_     =       SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       convert_config(model[0] ,	SCREAMING_SNAKE_CASE_						)
  lowercase_     =       model[0].eval()

  lowercase_     =       True if config.feat_extract_norm == """layer""" else False
  lowercase_     =       WavaVecaFeatureExtractor(
      feature_size=1 ,	sampling_rate=1_60_00 ,	padding_value=0 ,	do_normalize=SCREAMING_SNAKE_CASE_ ,	return_attention_mask=SCREAMING_SNAKE_CASE_ ,	)

  if is_finetuned:
    if dict_path:
      lowercase_     =       Dictionary.load(SCREAMING_SNAKE_CASE_						)

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

    lowercase_     =       SEWForCTC(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       SEWModel(SCREAMING_SNAKE_CASE_						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)

  recursively_load_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)


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("""--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"""
       )
       __snake_case						  =		parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )

