

import argparse
import os
import re


a_     :    str     		=   "src/transformers"

# Pattern that looks at the indentation in a line.
a_     :    Dict     		=   re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
a_     :    Optional[Any]     		=   re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a_     :    int     		=   re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
a_     :    Any     		=   re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a_     :    Optional[Any]     		=   re.compile(R"\[([^\]]+)\]")


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      _re_indent.search(_UpperCamelCase						)
 return "" if search is None else search.groups()[0]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Tuple=""		,				_UpperCamelCase		:	Tuple=None		,				_UpperCamelCase		:	Tuple=None						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      0
 SCREAMING_SNAKE_CASE		    =      code.split('\n'						)
 if start_prompt is not None:
  while not lines[index].startswith(_UpperCamelCase						):
   index += 1
  SCREAMING_SNAKE_CASE		    =      ['\n'.join(lines[:index]						)]
 else:
  SCREAMING_SNAKE_CASE		    =      []

 # We split into blocks until we get to the `end_prompt` (or the end of the block).
 SCREAMING_SNAKE_CASE		    =      [lines[index]]
 index += 1
 while index < len(_UpperCamelCase						) and (end_prompt is None or not lines[index].startswith(_UpperCamelCase						)):
  if len(lines[index]						) > 0 and get_indent(lines[index]						) == indent_level:
   if len(_UpperCamelCase						) > 0 and get_indent(current_block[-1]						).startswith(indent_level + ' '						):
    current_block.append(lines[index]						)
    blocks.append('\n'.join(_UpperCamelCase						)						)
    if index < len(_UpperCamelCase						) - 1:
     SCREAMING_SNAKE_CASE		    =      [lines[index + 1]]
     index += 1
    else:
     SCREAMING_SNAKE_CASE		    =      []
   else:
    blocks.append('\n'.join(_UpperCamelCase						)						)
    SCREAMING_SNAKE_CASE		    =      [lines[index]]
  else:
   current_block.append(lines[index]						)
  index += 1

 # Adds current block if it's nonempty.
 if len(_UpperCamelCase						) > 0:
  blocks.append('\n'.join(_UpperCamelCase						)						)

 # Add final block after end_prompt if provided.
 if end_prompt is not None and index < len(_UpperCamelCase						):
  blocks.append('\n'.join(lines[index:]						)						)

 return blocks


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any						)		->				Tuple:
 '''simple docstring'''


 def _inner(_UpperCamelCase		:	int						):
  return key(_UpperCamelCase						).lower().replace('_'		,				''						)

 return _inner


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple=None						)		->				Any:
 '''simple docstring'''


 def noop(_UpperCamelCase		:	int						):
  return x

 if key is None:
  SCREAMING_SNAKE_CASE		    =      noop
 # Constants are all uppercase, they go first.
 SCREAMING_SNAKE_CASE		    =      [obj for obj in objects if key(_UpperCamelCase						).isupper()]
 # Classes are not all uppercase but start with a capital, they go second.
 SCREAMING_SNAKE_CASE		    =      [obj for obj in objects if key(_UpperCamelCase						)[0].isupper() and not key(_UpperCamelCase						).isupper()]
 # Functions begin with a lowercase, they go last.
 SCREAMING_SNAKE_CASE		    =      [obj for obj in objects if not key(_UpperCamelCase						)[0].isupper()]

 SCREAMING_SNAKE_CASE		    =      ignore_underscore(_UpperCamelCase						)
 return sorted(_UpperCamelCase		,				key=_UpperCamelCase						) + sorted(_UpperCamelCase		,				key=_UpperCamelCase						) + sorted(_UpperCamelCase		,				key=_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]						)		->				Any:
 '''simple docstring'''


 def _replace(_UpperCamelCase		:	Optional[int]						):
  SCREAMING_SNAKE_CASE		    =      match.groups()[0]
  if "," not in imports:
   return f"""[{imports}]"""
  SCREAMING_SNAKE_CASE		    =      [part.strip().replace('"'		,				''						) for part in imports.split(','						)]
  # We will have a final empty element if the line finished with a comma.
  if len(keys[-1]						) == 0:
   SCREAMING_SNAKE_CASE		    =      keys[:-1]
  return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(_UpperCamelCase						)]						) + "]"

 SCREAMING_SNAKE_CASE		    =      import_statement.split('\n'						)
 if len(_UpperCamelCase						) > 3:
  # Here we have to sort internal imports that are on several lines (one per name):
  # key: [
  #     "object1",
  #     "object2",
  #     ...
  # ]

  # We may have to ignore one or two lines on each side.
  SCREAMING_SNAKE_CASE		    =      2 if lines[1].strip() == '[' else 1
  SCREAMING_SNAKE_CASE		    =      [(i, _re_strip_line.search(_UpperCamelCase						).groups()[0]) for i, line in enumerate(lines[idx:-idx]						)]
  SCREAMING_SNAKE_CASE		    =      sort_objects(_UpperCamelCase		,				key=lambda _UpperCamelCase						: x[1]						)
  SCREAMING_SNAKE_CASE		    =      [lines[x[0] + idx] for x in sorted_indices]
  return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]						)
 elif len(_UpperCamelCase						) == 3:
  # Here we have to sort internal imports that are on one separate line:
  # key: [
  #     "object1", "object2", ...
  # ]
  if _re_bracket_content.search(lines[1]						) is not None:
   SCREAMING_SNAKE_CASE		    =      _re_bracket_content.sub(_replace		,				lines[1]						)
  else:
   SCREAMING_SNAKE_CASE		    =      [part.strip().replace('"'		,				''						) for part in lines[1].split(','						)]
   # We will have a final empty element if the line finished with a comma.
   if len(keys[-1]						) == 0:
    SCREAMING_SNAKE_CASE		    =      keys[:-1]
   SCREAMING_SNAKE_CASE		    =      get_indent(lines[1]						) + ', '.join([f"""\"{k}\"""" for k in sort_objects(_UpperCamelCase						)]						)
  return "\n".join(_UpperCamelCase						)
 else:
  # Finally we have to deal with imports fitting on one line
  SCREAMING_SNAKE_CASE		    =      _re_bracket_content.sub(_replace		,				_UpperCamelCase						)
  return import_statement


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Dict=True						)		->				List[Any]:
 '''simple docstring'''


 with open(_UpperCamelCase		,				encoding='utf-8'						) as f:
  SCREAMING_SNAKE_CASE		    =      f.read()

 if "_import_structure" not in code:
  return

 # Blocks of indent level 0
 SCREAMING_SNAKE_CASE		    =      split_code_in_indented_blocks(
     _UpperCamelCase		,				start_prompt='_import_structure = {'		,				end_prompt='if TYPE_CHECKING:'						)

 # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
 for block_idx in range(1		,				len(_UpperCamelCase						) - 1						):
  # Check if the block contains some `_import_structure`s thingy to sort.
  SCREAMING_SNAKE_CASE		    =      main_blocks[block_idx]
  SCREAMING_SNAKE_CASE		    =      block.split('\n'						)

  # Get to the start of the imports.
  SCREAMING_SNAKE_CASE		    =      0
  while line_idx < len(_UpperCamelCase						) and "_import_structure" not in block_lines[line_idx]:
   # Skip dummy import blocks
   if "import dummy" in block_lines[line_idx]:
    SCREAMING_SNAKE_CASE		    =      len(_UpperCamelCase						)
   else:
    line_idx += 1
  if line_idx >= len(_UpperCamelCase						):
   continue

  # Ignore beginning and last line: they don't contain anything.
  SCREAMING_SNAKE_CASE		    =      '\n'.join(block_lines[line_idx:-1]						)
  SCREAMING_SNAKE_CASE		    =      get_indent(block_lines[1]						)
  # Slit the internal block into blocks of indent level 1.
  SCREAMING_SNAKE_CASE		    =      split_code_in_indented_blocks(_UpperCamelCase		,				indent_level=_UpperCamelCase						)
  # We have two categories of import key: list or _import_structure[key].append/extend
  SCREAMING_SNAKE_CASE		    =      _re_direct_key if '_import_structure = {' in block_lines[0] else _re_indirect_key
  # Grab the keys, but there is a trap: some lines are empty or just comments.
  SCREAMING_SNAKE_CASE		    =      [(pattern.search(_UpperCamelCase						).groups()[0] if pattern.search(_UpperCamelCase						) is not None else None) for b in internal_blocks]
  # We only sort the lines with a key.
  SCREAMING_SNAKE_CASE		    =      [(i, key) for i, key in enumerate(_UpperCamelCase						) if key is not None]
  SCREAMING_SNAKE_CASE		    =      [x[0] for x in sorted(_UpperCamelCase		,				key=lambda _UpperCamelCase						: x[1]						)]

  # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
  SCREAMING_SNAKE_CASE		    =      0
  SCREAMING_SNAKE_CASE		    =      []
  for i in range(len(_UpperCamelCase						)						):
   if keys[i] is None:
    reorderded_blocks.append(internal_blocks[i]						)
   else:
    SCREAMING_SNAKE_CASE		    =      sort_objects_in_import(internal_blocks[sorted_indices[count]]						)
    reorderded_blocks.append(_UpperCamelCase						)
    count += 1

        # And we put our main block back together with its first and last line.
  SCREAMING_SNAKE_CASE		    =      '\n'.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]						)

 if code != "\n".join(_UpperCamelCase						):
  if check_only:
   return True
  else:
   print(f"""Overwriting {file}."""						)
   with open(_UpperCamelCase		,				'w'		,				encoding='utf-8'						) as f:
    f.write('\n'.join(_UpperCamelCase						)						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict=True						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 for root, _, files in os.walk(_UpperCamelCase						):
  if "__init__.py" in files:
   SCREAMING_SNAKE_CASE		    =      sort_imports(os.path.join(_UpperCamelCase		,				'__init__.py'						)		,				check_only=_UpperCamelCase						)
   if result:
    SCREAMING_SNAKE_CASE		    =      [os.path.join(_UpperCamelCase		,				'__init__.py'						)]
 if len(_UpperCamelCase						) > 0:
  raise ValueError(f"""Would overwrite {len(_UpperCamelCase						)} files, run `make style`."""						)


if __name__ == "__main__":
      a_     :    Any     		=   argparse.ArgumentParser()
      parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
      a_     :    Tuple     		=   parser.parse_args()

      sort_imports_in_all_inits(check_only=args.check_only)




import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_     :    Dict     		=   logging.get_logger(__name__)

a_     :    List[str]     		=   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
a_     :    Union[str, Any]     		=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


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


 for attribute in key.split('.'						):
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						)

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

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[Any]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      fairseq_model.state_dict()

 SCREAMING_SNAKE_CASE		    =      hf_model.feature_extractor
 SCREAMING_SNAKE_CASE		    =      hf_model.adapter

 for name, value in fairseq_dict.items():
  SCREAMING_SNAKE_CASE		    =      False
  if "conv_layers" in name:
   load_conv_layer(
       _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				hf_model.config.feat_extract_norm == 'group'		,				)
   SCREAMING_SNAKE_CASE		    =      True
  elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']						):
   load_adapter(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
     SCREAMING_SNAKE_CASE		    =      True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE		    =      name.split(_UpperCamelCase						)[0].split('.'						)[-2]
      SCREAMING_SNAKE_CASE		    =      mapped_key.replace('*'		,				_UpperCamelCase						)
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_g'
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_v'
     elif "bias" in name:
      SCREAMING_SNAKE_CASE		    =      'bias'
     elif "weight" in name:
      SCREAMING_SNAKE_CASE		    =      'weight'
     else:
      SCREAMING_SNAKE_CASE		    =      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 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('conv_layers.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)
 SCREAMING_SNAKE_CASE		    =      int(items[0]						)
 SCREAMING_SNAKE_CASE		    =      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."""
   )
   SCREAMING_SNAKE_CASE		    =      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."""
   )
   SCREAMING_SNAKE_CASE		    =      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."
   )
   SCREAMING_SNAKE_CASE		    =      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."""
   )
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Union[str, Any]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('adaptor.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)

 if items[1].isdigit():
  SCREAMING_SNAKE_CASE		    =      int(items[1]						)
 else:
  SCREAMING_SNAKE_CASE		    =      None

 if "adaptor" not in full_name:
  if "proj_ln" in full_name:
   # has to be layer norm
   if "bias" in name:
    assert (
        value.shape == adapter.proj_layer_norm.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj_layer_norm.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
  else:
   # has to be projection layer
   if "bias" in name:
    assert (
        value.shape == adapter.proj.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""						)
 elif isinstance(_UpperCamelCase		,				_UpperCamelCase						):
  if "bias" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.bias.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
  elif "weight" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.weight.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      emb.weight.shape
 SCREAMING_SNAKE_CASE		    =      nn.Linear(_UpperCamelCase		,				_UpperCamelCase		,				bias=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      emb.weight.data
 return lin_layer


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      WavaVecaConfig.from_pretrained(
     _UpperCamelCase		,				add_adapter=_UpperCamelCase		,				adapter_stride=_UpperCamelCase		,				adapter_kernel_size=_UpperCamelCase		,				use_auth_token=_UpperCamelCase		,				output_hidden_size=_UpperCamelCase		,				)
 SCREAMING_SNAKE_CASE		    =      MBartConfig.from_pretrained(_UpperCamelCase						)

 # load model
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]		,				arg_overrides={
         'config_yaml': config_yaml_path,
         'data': '/'.join(dict_path.split('/'						)[:-1]						),
         'w2v_path': checkpoint_path,
         'load_pretrained_decoder_from': None,
     }		,				)
 SCREAMING_SNAKE_CASE		    =      model[0].eval()

 # load feature extractor
 SCREAMING_SNAKE_CASE		    =      WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase		,				use_auth_token=_UpperCamelCase						)

 # set weights for wav2vec2 encoder
 SCREAMING_SNAKE_CASE		    =      WavaVecaModel(_UpperCamelCase						)

 recursively_load_weights_wavaveca(model.encoder		,				_UpperCamelCase						)

 # load decoder weights
 SCREAMING_SNAKE_CASE		    =      MBartForCausalLM(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,				strict=_UpperCamelCase						)
 logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""						)
 logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""						)

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderModel(encoder=_UpperCamelCase		,				decoder=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      False

 SCREAMING_SNAKE_CASE		    =      MBartaaTokenizer(_UpperCamelCase						)
 tokenizer.save_pretrained(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      hf_wavavec.config.to_dict()
 SCREAMING_SNAKE_CASE		    =      tokenizer.pad_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.bos_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      'mbart50'
 SCREAMING_SNAKE_CASE		    =      'wav2vec2'

 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      25_00_04
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase						)

 hf_wavavec.save_pretrained(_UpperCamelCase						)
 feature_extractor.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    List[str]     		=   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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
      parser.add_argument(
          "--encoder_config_path",
          default="facebook/wav2vec2-xls-r-1b",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/mbart-large-50-one-to-many-mmt",
          type=str,
          help="Path to hf decoder checkpoint config",
      )
      parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
      parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
      parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
      parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
      parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

      a_     :    Dict     		=   parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          args.config_yaml_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          add_adapter=args.add_adapter,
          adapter_kernel_size=args.adapter_kernel_size,
          adapter_stride=args.adapter_stride,
          decoder_start_token_id=args.start_token_id,
          encoder_output_dim=args.encoder_output_dim,
      )

