





import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict  )   ->    Optional[int]:
  _lowercase						    =			MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    _lowercase						    =			[144, 192, 240]
    _lowercase						    =			[16, 32, 64, 96, 128, 160, 640]
  elif "mobilevit_xs" in mobilevit_name:
    _lowercase						    =			[96, 120, 144]
    _lowercase						    =			[16, 32, 48, 64, 80, 96, 384]
  elif "mobilevit_xxs" in mobilevit_name:
    _lowercase						    =			[64, 80, 96]
    _lowercase						    =			[16, 16, 24, 48, 64, 80, 320]
    _lowercase						    =			0.05
    _lowercase						    =			2.0

  if mobilevit_name.startswith('deeplabv3_'  ):
    _lowercase						    =			512
    _lowercase						    =			16
    _lowercase						    =			21
    _lowercase						    =			'pascal-voc-id2label.json'
  else:
    _lowercase						    =			1000
    _lowercase						    =			'imagenet-1k-id2label.json'

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

  return config

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Dict=False  )   ->    List[str]:
  for i in range(1      ,				6  ):
    if F"""layer_{i}.""" in name:
      _lowercase						    =			name.replace(F"""layer_{i}."""      ,				F"""encoder.layer.{i - 1}."""  )

  if "conv_1." in name:
    _lowercase						    =			name.replace('conv_1.'      ,				'conv_stem.'  )
  if ".block." in name:
    _lowercase						    =			name.replace('.block.'      ,				'.'  )

  if "exp_1x1" in name:
    _lowercase						    =			name.replace('exp_1x1'      ,				'expand_1x1'  )
  if "red_1x1" in name:
    _lowercase						    =			name.replace('red_1x1'      ,				'reduce_1x1'  )
  if ".local_rep.conv_3x3." in name:
    _lowercase						    =			name.replace('.local_rep.conv_3x3.'      ,				'.conv_kxk.'  )
  if ".local_rep.conv_1x1." in name:
    _lowercase						    =			name.replace('.local_rep.conv_1x1.'      ,				'.conv_1x1.'  )
  if ".norm." in name:
    _lowercase						    =			name.replace('.norm.'      ,				'.normalization.'  )
  if ".conv." in name:
    _lowercase						    =			name.replace('.conv.'      ,				'.convolution.'  )
  if ".conv_proj." in name:
    _lowercase						    =			name.replace('.conv_proj.'      ,				'.conv_projection.'  )

  for i in range(0      ,				2  ):
    for j in range(0      ,				4  ):
      if F""".{i}.{j}.""" in name:
        _lowercase						    =			name.replace(F""".{i}.{j}."""      ,				F""".{i}.layer.{j}."""  )

  for i in range(2      ,				6  ):
    for j in range(0      ,				4  ):
      if F""".{i}.{j}.""" in name:
        _lowercase						    =			name.replace(F""".{i}.{j}."""      ,				F""".{i}."""  )
        if "expand_1x1" in name:
          _lowercase						    =			name.replace('expand_1x1'      ,				'downsampling_layer.expand_1x1'  )
        if "conv_3x3" in name:
          _lowercase						    =			name.replace('conv_3x3'      ,				'downsampling_layer.conv_3x3'  )
        if "reduce_1x1" in name:
          _lowercase						    =			name.replace('reduce_1x1'      ,				'downsampling_layer.reduce_1x1'  )

  for i in range(2      ,				5  ):
    if F""".global_rep.{i}.weight""" in name:
      _lowercase						    =			name.replace(F""".global_rep.{i}.weight"""      ,				'.layernorm.weight'  )
    if F""".global_rep.{i}.bias""" in name:
      _lowercase						    =			name.replace(F""".global_rep.{i}.bias"""      ,				'.layernorm.bias'  )

  if ".global_rep." in name:
    _lowercase						    =			name.replace('.global_rep.'      ,				'.transformer.'  )
  if ".pre_norm_mha.0." in name:
    _lowercase						    =			name.replace('.pre_norm_mha.0.'      ,				'.layernorm_before.'  )
  if ".pre_norm_mha.1.out_proj." in name:
    _lowercase						    =			name.replace('.pre_norm_mha.1.out_proj.'      ,				'.attention.output.dense.'  )
  if ".pre_norm_ffn.0." in name:
    _lowercase						    =			name.replace('.pre_norm_ffn.0.'      ,				'.layernorm_after.'  )
  if ".pre_norm_ffn.1." in name:
    _lowercase						    =			name.replace('.pre_norm_ffn.1.'      ,				'.intermediate.dense.'  )
  if ".pre_norm_ffn.4." in name:
    _lowercase						    =			name.replace('.pre_norm_ffn.4.'      ,				'.output.dense.'  )
  if ".transformer." in name:
    _lowercase						    =			name.replace('.transformer.'      ,				'.transformer.layer.'  )

  if ".aspp_layer." in name:
    _lowercase						    =			name.replace('.aspp_layer.'      ,				'.'  )
  if ".aspp_pool." in name:
    _lowercase						    =			name.replace('.aspp_pool.'      ,				'.'  )
  if "seg_head." in name:
    _lowercase						    =			name.replace('seg_head.'      ,				'segmentation_head.'  )
  if "segmentation_head.classifier.classifier." in name:
    _lowercase						    =			name.replace('segmentation_head.classifier.classifier.'      ,				'segmentation_head.classifier.'  )

  if "classifier.fc." in name:
    _lowercase						    =			name.replace('classifier.fc.'      ,				'classifier.'  )
  elif (not base_model) and ("segmentation_head." not in name):
    _lowercase						    =			'mobilevit.' + name

  return name

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Dict      ,				snake_case__	:Tuple=False  )   ->    Optional[int]:
  if base_model:
    _lowercase						    =			''
  else:
    _lowercase						    =			'mobilevit.'

  for key in orig_state_dict.copy().keys():
    _lowercase						    =			orig_state_dict.pop(snake_case__  )

    if key[:8] == "encoder.":
      _lowercase						    =			key[8:]

    if "qkv" in key:
      _lowercase						    =			key.split('.'  )
      _lowercase						    =			int(key_split[0][6:]  ) - 1
      _lowercase						    =			int(key_split[3]  )
      _lowercase						    =			model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""  )
      _lowercase						    =			layer.transformer.layer[transformer_num].attention.attention.all_head_size
      _lowercase						    =			(
          F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
      )
      if "weight" in key:
        _lowercase						    =			val[:dim, :]
        _lowercase						    =			val[dim : dim * 2, :]
        _lowercase						    =			val[-dim:, :]
      else:
        _lowercase						    =			val[:dim]
        _lowercase						    =			val[dim : dim * 2]
        _lowercase						    =			val[-dim:]
    else:
      _lowercase						    =			val

  return orig_state_dict

def 				SCREAMING_SNAKE_CASE__							(	)   ->    Tuple:
  _lowercase						    =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase						    =			Image.open(requests.get(snake_case__      ,				stream=snake_case__  ).raw  )
  return im

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Tuple      ,				snake_case__	:List[str]      ,				snake_case__	:Tuple=False  )   ->    Optional[Any]:
  _lowercase						    =			get_mobilevit_config(snake_case__  )

  # load original state_dict
  _lowercase						    =			torch.load(snake_case__      ,				map_location='cpu'  )

  # load 🤗 model
  if mobilevit_name.startswith('deeplabv3_'  ):
    _lowercase						    =			MobileViTForSemanticSegmentation(snake_case__  ).eval()
  else:
    _lowercase						    =			MobileViTForImageClassification(snake_case__  ).eval()

  _lowercase						    =			convert_state_dict(snake_case__      ,				snake_case__  )
  model.load_state_dict(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(**snake_case__  )
  _lowercase						    =			outputs.logits

  if mobilevit_name.startswith('deeplabv3_'  ):
    assert logits.shape == (1, 21, 32, 32)

    if mobilevit_name == "deeplabv3_mobilevit_s":
      _lowercase						    =			torch.tensor(
          [
              [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
              [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
              [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
          ]  )
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      _lowercase						    =			torch.tensor(
          [
              [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
              [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
              [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
          ]  )
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      _lowercase						    =			torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
              [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
          ]  )
    else:
      raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""  )

    assert torch.allclose(logits[0, :3, :3, :3]      ,				snake_case__      ,				atol=1E-4  )
  else:
    assert logits.shape == (1, 1000)

    if mobilevit_name == "mobilevit_s":
      _lowercase						    =			torch.tensor([-0.9866, 0.2392, -1.1241]  )
    elif mobilevit_name == "mobilevit_xs":
      _lowercase						    =			torch.tensor([-2.4761, -0.9399, -1.9587]  )
    elif mobilevit_name == "mobilevit_xxs":
      _lowercase						    =			torch.tensor([-1.9364, -1.2327, -0.4653]  )
    else:
      raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""  )

    assert torch.allclose(logits[0, :3]      ,				snake_case__      ,				atol=1E-4  )

  Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
  print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""  )
  model.save_pretrained(snake_case__  )
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
  image_processor.save_pretrained(snake_case__  )

  if push_to_hub:
    _lowercase						    =			{
        'mobilevit_s': 'mobilevit-small',
        'mobilevit_xs': 'mobilevit-x-small',
        'mobilevit_xxs': 'mobilevit-xx-small',
        'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
        'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
        'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
    }

    print('Pushing to the hub...'  )
    _lowercase						    =			model_mapping[mobilevit_name]
    image_processor.push_to_hub(snake_case__      ,				organization='apple'  )
    model.push_to_hub(snake_case__      ,				organization='apple'  )


if __name__ == "__main__":
      snake_case								=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--mobilevit_name""",
          default="""mobilevit_s""",
          type=str,
          help=(
              """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
              """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
          ),
      )
      parser.add_argument(
          """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      snake_case								=							parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a__   :					List[str] 						=      logging.get_logger("transformers.models.encodec")

a__   :					Optional[Any] 						=      {
    "quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
    "quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
    "quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
    "quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
a__   :					int 						=      {
    "encoder.model.0.conv.conv": "encoder.layers.0.conv",
    "encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
    "encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
    "encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
    "encoder.model.3.conv.conv": "encoder.layers.3.conv",
    "encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
    "encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
    "encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
    "encoder.model.6.conv.conv": "encoder.layers.6.conv",
    "encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
    "encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
    "encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
    "encoder.model.9.conv.conv": "encoder.layers.9.conv",
    "encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
    "encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
    "encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
    "encoder.model.12.conv.conv": "encoder.layers.12.conv",
    "encoder.model.13.lstm": "encoder.layers.13.lstm",
    "encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
a__   :					int 						=      {
    "encoder.model.0.conv.norm": "encoder.layers.0.norm",
    "encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
    "encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
    "encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
    "encoder.model.3.conv.norm": "encoder.layers.3.norm",
    "encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
    "encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
    "encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
    "encoder.model.6.conv.norm": "encoder.layers.6.norm",
    "encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
    "encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
    "encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
    "encoder.model.9.conv.norm": "encoder.layers.9.norm",
    "encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
    "encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
    "encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
    "encoder.model.12.conv.norm": "encoder.layers.12.norm",
    "encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
a__   :					Dict 						=      {
    "decoder.model.0.conv.conv": "decoder.layers.0.conv",
    "decoder.model.1.lstm": "decoder.layers.1.lstm",
    "decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
    "decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
    "decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
    "decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
    "decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
    "decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
    "decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
    "decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
    "decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
    "decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
    "decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
    "decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
    "decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
    "decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
    "decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
    "decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
    "decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
a__   :					Any 						=      {
    "decoder.model.0.conv.norm": "decoder.layers.0.norm",
    "decoder.model.3.convtr.norm": "decoder.layers.3.norm",
    "decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
    "decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
    "decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
    "decoder.model.6.convtr.norm": "decoder.layers.6.norm",
    "decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
    "decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
    "decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
    "decoder.model.9.convtr.norm": "decoder.layers.9.norm",
    "decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
    "decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
    "decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
    "decoder.model.12.convtr.norm": "decoder.layers.12.norm",
    "decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
    "decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
    "decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
    "decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
a__   :					Tuple 						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__   :					List[str] 						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__   :					str 						=      []
a__   :					str 						=      []

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__						):
      for attribute in key.split('.'						):
            lowercase__  =	getattr(A__    ,	A__						)

      if weight_type is not None:
            lowercase__  =	getattr(A__    ,	A__						).shape
      else:
            lowercase__  =	hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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
      elif weight_type == "running_mean":
            lowercase__  =	value
      elif weight_type == "running_var":
            lowercase__  =	value
      elif weight_type == "num_batches_tracked":
            lowercase__  =	value
      elif weight_type == "weight_ih_l0":
            lowercase__  =	value
      elif weight_type == "weight_hh_l0":
            lowercase__  =	value
      elif weight_type == "bias_ih_l0":
            lowercase__  =	value
      elif weight_type == "bias_hh_l0":
            lowercase__  =	value
      elif weight_type == "weight_ih_l1":
            lowercase__  =	value
      elif weight_type == "weight_hh_l1":
            lowercase__  =	value
      elif weight_type == "bias_ih_l1":
            lowercase__  =	value
      elif weight_type == "bias_hh_l1":
            lowercase__  =	value
      else:
            lowercase__  =	value

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

def 	_lowerCAmelCase (    A__    ,	A__						):
      for key in ignore_keys:
            if key.endswith('.*'						):
                  if name.startswith(key[:-1]						):
                        return True
            elif ".*." in key:
                  lowercase__,						lowercase__  =	key.split('.*.'						)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowercase__  =	MAPPING_24K
      elif model_name == "encodec_48khz":
            lowercase__  =	MAPPING_48K
      else:
            raise ValueError(F'''Unsupported model: {model_name}'''						)

      for name, value in orig_dict.items():
            if should_ignore(A__    ,	A__						):
                  logger.info(F'''{name} was ignored'''						)
                  continue

            lowercase__  =	False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowercase__,						lowercase__  =	key.split('.*.'						)
                        if prefix in name and suffix in name:
                              lowercase__  =	suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith('embed'						) and name.endswith('embed_avg'						):
                              continue

                        lowercase__  =	True
                        if "*" in mapped_key:
                              lowercase__  =	name.split(A__						)[0].split('.'						)[-2]
                              lowercase__  =	mapped_key.replace('*'    ,	A__						)
                        if "weight_g" in name:
                              lowercase__  =	'weight_g'
                        elif "weight_v" in name:
                              lowercase__  =	'weight_v'
                        elif "weight_ih_l0" in name:
                              lowercase__  =	'weight_ih_l0'
                        elif "weight_hh_l0" in name:
                              lowercase__  =	'weight_hh_l0'
                        elif "bias_ih_l0" in name:
                              lowercase__  =	'bias_ih_l0'
                        elif "bias_hh_l0" in name:
                              lowercase__  =	'bias_hh_l0'
                        elif "weight_ih_l1" in name:
                              lowercase__  =	'weight_ih_l1'
                        elif "weight_hh_l1" in name:
                              lowercase__  =	'weight_hh_l1'
                        elif "bias_ih_l1" in name:
                              lowercase__  =	'bias_ih_l1'
                        elif "bias_hh_l1" in name:
                              lowercase__  =	'bias_hh_l1'
                        elif "bias" in name:
                              lowercase__  =	'bias'
                        elif "weight" in name:
                              lowercase__  =	'weight'
                        elif "running_mean" in name:
                              lowercase__  =	'running_mean'
                        elif "running_var" in name:
                              lowercase__  =	'running_var'
                        elif "num_batches_tracked" in name:
                              lowercase__  =	'num_batches_tracked'
                        else:
                              lowercase__  =	None
                        set_recursively(A__    ,	A__    ,	A__    ,	A__    ,	A__						)
                  continue
            if not is_used:
                  unused_weights.append(A__						)

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

@torch.no_grad()
def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__=None    ,	A__=None    ,	):
      if config_path is not None:
            lowercase__  =	EncodecConfig.from_pretrained(A__						)
      else:
            lowercase__  =	EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowercase__  =	[8, 5, 4, 4]
            lowercase__  =	[2.2]
            lowercase__  =	64
            lowercase__  =	32_000
            lowercase__  =	2_048
            lowercase__  =	False
            lowercase__  =	False
            lowercase__  =	False
      elif model_name == "encodec_48khz":
            lowercase__  =	[8, 5, 4, 2]
            lowercase__  =	[3.0, 6.0, 12.0, 24.0]
            lowercase__  =	48_000
            lowercase__  =	2
            lowercase__  =	False
            lowercase__  =	'time_group_norm'
            lowercase__  =	True
            lowercase__  =	1.0
            lowercase__  =	0.01
      else:
            raise ValueError(F'''Unknown model name: {model_name}'''						)

      lowercase__  =	EncodecModel(A__						)

      lowercase__  =	EncodecFeatureExtractor(
          feature_size=config.audio_channels    ,	sampling_rate=config.sampling_rate    ,	chunk_length_s=config.chunk_length_s    ,	overlap=config.overlap    ,	)
      feature_extractor.save_pretrained(A__						)

      lowercase__  =	torch.load(A__						)
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            lowercase__  =	original_checkpoint['best_state']
      recursively_load_weights(A__    ,	A__    ,	A__						)
      model.save_pretrained(A__						)

      if repo_id:
            print('Pushing to the hub...'						)
            feature_extractor.push_to_hub(A__						)
            model.push_to_hub(A__						)


if __name__ == "__main__":
       a__   :					Optional[int] 						=      argparse.ArgumentParser()
       parser.add_argument(
           "--model",
           default="encodec_24khz",
           type=str,
           help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
       )
       parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
       )

       a__   :					Optional[int] 						=      parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )
