import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
     import torch

     snake_case__						      =			True
except ImportError:
     snake_case__						      =			False


try:
     from torch.hub import _get_torch_home

     snake_case__						      =			_get_torch_home()
except ImportError:
     snake_case__						      =			os.path.expanduser(
         os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
     )

snake_case__						      =			os.path.join(torch_cache_home, 'transformers')

snake_case__						      =			'https://cdn.huggingface.co'
snake_case__						      =			'https://s3.amazonaws.com/models.huggingface.co/bert'
snake_case__						      =			'/'.join(str(Path(__file__).resolve()).split('/')[:-1])
snake_case__						      =			os.path.join(PATH, 'config.yaml')
snake_case__						      =			os.path.join(PATH, 'attributes.txt')
snake_case__						      =			os.path.join(PATH, 'objects.txt')
snake_case__						      =			os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
snake_case__						      =			os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
snake_case__						      =			os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
snake_case__						      =			'pytorch_model.bin'
snake_case__						      =			'config.yaml'
def    __magic_name__(	__UpperCAmelCase=OBJECTS			,    __UpperCAmelCase=ATTRIBUTES		)    ->     Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase           =					[]
      with open(lowercase__		) as f:
            for object in f.readlines():
                  vg_classes.append(object.split(''','''		)[0].lower().strip()		)

      _lowerCamelCase           =					[]
      with open(lowercase__		) as f:
            for object in f.readlines():
                  vg_attrs.append(object.split(''','''		)[0].lower().strip()		)
      return vg_classes, vg_attrs
def    __magic_name__(	__UpperCAmelCase		)    ->     List[Any]:



      '''simple docstring'''

      _lowerCamelCase           =					OrderedDict()
      with open(lowercase__			,    '''rb'''		) as f:
            _lowerCamelCase           =					pkl.load(lowercase__		)['''model''']
      for k in copy.deepcopy(list(ckp.keys()		)		):
            _lowerCamelCase           =					ckp.pop(lowercase__		)
            if isinstance(lowercase__			,    np.ndarray		):
                  _lowerCamelCase           =					torch.tensor(lowercase__		)
            else:
                  assert isinstance(lowercase__			,    torch.tensor		), type(lowercase__		)
            _lowerCamelCase           =					v
      return r



class 			UpperCamelCase					:

     '''simple docstring'''

     A_					    =	{}



     def __init__(       self					,					A_					,					A_ = "root"					,					A_=0    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					name
           _lowerCamelCase           =					level
           _lowerCamelCase           =					{}
           for k, v in dictionary.items():
                 if v is None:
                       raise ValueError()
                 _lowerCamelCase           =					copy.deepcopy(__SCREAMING_SNAKE_CASE    )
                 _lowerCamelCase           =					copy.deepcopy(__SCREAMING_SNAKE_CASE    )
                 if isinstance(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE    ):
                       _lowerCamelCase           =					Config(__SCREAMING_SNAKE_CASE					,					name=__SCREAMING_SNAKE_CASE					,					level=level + 1    )
                 _lowerCamelCase           =					v
                 setattr(self					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE    )

           _lowerCamelCase           =					d



     def __repr__(       self    )			->					List[str]:






           """simple docstring"""
           return str(list((self._pointer.keys())    )    )



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






           """simple docstring"""
           _lowerCamelCase           =					val
           _lowerCamelCase           =					val
           _lowerCamelCase           =					key.split('''.'''    )
           _lowerCamelCase           =					len(__SCREAMING_SNAKE_CASE    ) - 1
           _lowerCamelCase           =					self._pointer
           if len(__SCREAMING_SNAKE_CASE    ) > 1:
                 for i, l in enumerate(__SCREAMING_SNAKE_CASE    ):
                       if hasattr(self					,					__SCREAMING_SNAKE_CASE    ) and isinstance(getattr(self					,					__SCREAMING_SNAKE_CASE    )					,					__SCREAMING_SNAKE_CASE    ):
                             setattr(getattr(self					,					__SCREAMING_SNAKE_CASE    )					,					'''.'''.join(levels[i:]    )					,					__SCREAMING_SNAKE_CASE    )
                       if l == last_level:
                             _lowerCamelCase           =					val
                       else:
                             _lowerCamelCase           =					pointer[l]



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           return self._pointer



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






           """simple docstring"""
           with open(F'{file_name}'					,					'''w'''    ) as stream:
                 dump(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_    )			->					List[str]:






           """simple docstring"""
           with open(F'{file_name}'					,					'''w'''    ) as stream:
                 json.dump(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE    )



     @staticmethod
     def 		UpperCamelCase_    (       A_    )			->					str:






           """simple docstring"""
           with open(__SCREAMING_SNAKE_CASE    ) as stream:
                 _lowerCamelCase           =					load(__SCREAMING_SNAKE_CASE					,					Loader=__SCREAMING_SNAKE_CASE    )
           return data



     def __str__(       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					'''    '''
           if self._name != "root":
                 _lowerCamelCase           =					F'{t * (self._level-1)}{self._name}:\n'
           else:
                 _lowerCamelCase           =					''''''
           _lowerCamelCase           =					self._level
           for i, (k, v) in enumerate(self._pointer.items()    ):
                 if isinstance(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE    ):
                       r += F'{t * (self._level)}{v}\n'
                       self._level += 1
                 else:
                       r += F'{t * (self._level)}{k}: {v} ({type(__SCREAMING_SNAKE_CASE    ).__name__})\n'
                 _lowerCamelCase           =					level
           return r[:-1]



     @classmethod
     def 		UpperCamelCase_    (       cls					,					A_					,					**A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase     ,       _lowerCamelCase           =					cls.get_config_dict(__SCREAMING_SNAKE_CASE					,					**__SCREAMING_SNAKE_CASE    )
           return cls(__SCREAMING_SNAKE_CASE    )





     @classmethod
     def 		UpperCamelCase_    (       cls					,					A_					,					**A_    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					kwargs.pop('''cache_dir'''					,					__SCREAMING_SNAKE_CASE    )
           _lowerCamelCase           =					kwargs.pop('''force_download'''					,					__SCREAMING_SNAKE_CASE    )
           _lowerCamelCase           =					kwargs.pop('''resume_download'''					,					__SCREAMING_SNAKE_CASE    )
           _lowerCamelCase           =					kwargs.pop('''proxies'''					,					__SCREAMING_SNAKE_CASE    )
           _lowerCamelCase           =					kwargs.pop('''local_files_only'''					,					__SCREAMING_SNAKE_CASE    )

           if os.path.isdir(__SCREAMING_SNAKE_CASE    ):
                 _lowerCamelCase           =					os.path.join(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE    )
           elif os.path.isfile(__SCREAMING_SNAKE_CASE    ) or is_remote_url(__SCREAMING_SNAKE_CASE    ):
                 _lowerCamelCase           =					pretrained_model_name_or_path
           else:
                 _lowerCamelCase           =					hf_bucket_url(__SCREAMING_SNAKE_CASE					,					filename=__SCREAMING_SNAKE_CASE					,					use_cdn=__SCREAMING_SNAKE_CASE    )

           try:
                 # Load from URL or cache if already cached
                 _lowerCamelCase           =					cached_path(
                     __SCREAMING_SNAKE_CASE					,					cache_dir=__SCREAMING_SNAKE_CASE					,					force_download=__SCREAMING_SNAKE_CASE					,					proxies=__SCREAMING_SNAKE_CASE					,					resume_download=__SCREAMING_SNAKE_CASE					,					local_files_only=__SCREAMING_SNAKE_CASE					,					)
                 # Load config dict
                 if resolved_config_file is None:
                       raise EnvironmentError

                 _lowerCamelCase           =					Config.load_yaml(__SCREAMING_SNAKE_CASE    )

           except EnvironmentError:
                 _lowerCamelCase           =					'''Can\'t load config for'''
                 raise EnvironmentError(__SCREAMING_SNAKE_CASE    )

           if resolved_config_file == config_file:
                 print('''loading configuration file from path'''    )
           else:
                 print('''loading configuration file cache'''    )

           return Config.load_yaml(__SCREAMING_SNAKE_CASE    ), kwargs





def    __magic_name__(	__UpperCAmelCase		)    ->     Any:



      '''simple docstring'''

      _lowerCamelCase           =					torch.load('''dump.pt'''			,    map_location=in_tensor.device		)
      _lowerCamelCase           =					in_tensor.numpy()
      _lowerCamelCase           =					out_tensor.numpy()[0]
      print(na.shape			,    na[0, 0, :5]		)
      print(na.shape			,    na[0, 0, :5]		)
      assert np.allclose(lowercase__			,    lowercase__			,    rtol=0.0_1			,    atol=0.1		), (
          F'{sum([1 for x in np.isclose(lowercase__			,    lowercase__			,    rtol=0.0_1			,    atol=0.1		).flatten() if x is False]		)/len(na.flatten()		)*100:.4f} %'
          " element-wise mismatch"
      )
      raise Exception('''tensors are all good'''		)

      # Hugging face functions below
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					urlparse(lowercase__		)
      return parsed.scheme in ("http", "https")
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase=True		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
      _lowerCamelCase           =					'''/''' not in model_id
      if legacy_format:
            return F'{endpoint}/{model_id}-{filename}'
      else:
            return F'{endpoint}/{model_id}/{filename}'
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase=None			,    __UpperCAmelCase=0			,    __UpperCAmelCase=None			,    )    ->     Dict:



      '''simple docstring'''

      _lowerCamelCase           =					'''python/{}'''.format(sys.version.split()[0]		)
      if _torch_available:
            ua += "; torch/{}".format(torch.__version__		)
      if isinstance(lowercase__			,    lowercase__		):
            ua += "; " + "; ".join('''{}/{}'''.format(lowercase__			,    lowercase__		) for k, v in user_agent.items()		)
      elif isinstance(lowercase__			,    lowercase__		):
            ua += "; " + user_agent
      _lowerCamelCase           =					{'''user-agent''': ua}
      if resume_size > 0:
            _lowerCamelCase           =					'''bytes=%d-''' % (resume_size,)
      _lowerCamelCase           =					requests.get(lowercase__			,    stream=lowercase__			,    proxies=lowercase__			,    headers=lowercase__		)
      if response.status_code == 416:  # Range not satisfiable
            return
      _lowerCamelCase           =					response.headers.get('''Content-Length'''		)
      _lowerCamelCase           =					resume_size + int(lowercase__		) if content_length is not None else None
      _lowerCamelCase           =					tqdm(
          unit='''B'''			,    unit_scale=lowercase__			,    total=lowercase__			,    initial=lowercase__			,    desc='''Downloading'''			,    )
      for chunk in response.iter_content(chunk_size=1024		):
            if chunk:  # filter out keep-alive new chunks
                  progress.update(len(lowercase__		)		)
                  temp_file.write(lowercase__		)
      progress.close()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=None			,    __UpperCAmelCase=False			,    __UpperCAmelCase=None			,    __UpperCAmelCase=10			,    __UpperCAmelCase=False			,    __UpperCAmelCase=None			,    __UpperCAmelCase=False			,    )    ->     int:



      '''simple docstring'''

      if cache_dir is None:
            _lowerCamelCase           =					TRANSFORMERS_CACHE
      if isinstance(lowercase__			,    lowercase__		):
            _lowerCamelCase           =					str(lowercase__		)

      os.makedirs(lowercase__			,    exist_ok=lowercase__		)

      _lowerCamelCase           =					None
      if not local_files_only:
            try:
                  _lowerCamelCase           =					requests.head(lowercase__			,    allow_redirects=lowercase__			,    proxies=lowercase__			,    timeout=lowercase__		)
                  if response.status_code == 200:
                        _lowerCamelCase           =					response.headers.get('''ETag'''		)
            except (EnvironmentError, requests.exceptions.Timeout):
                  # etag is already None
                  pass

      _lowerCamelCase           =					url_to_filename(lowercase__			,    lowercase__		)

      # get cache path to put the file
      _lowerCamelCase           =					os.path.join(lowercase__			,    lowercase__		)

      # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
      # try to get the last downloaded one
      if etag is None:
            if os.path.exists(lowercase__		):
                  return cache_path
            else:
                  _lowerCamelCase           =					[
                      file
                      for file in fnmatch.filter(os.listdir(lowercase__		)			,    filename + '''.*'''		)
                      if not file.endswith('''.json'''		) and not file.endswith('''.lock'''		)
                  ]
                  if len(lowercase__		) > 0:
                        return os.path.join(lowercase__			,    matching_files[-1]		)
                  else:
                        # If files cannot be found and local_files_only=True,
                        # the models might've been found if local_files_only=False
                        # Notify the user about that
                        if local_files_only:
                              raise ValueError(
                                  '''Cannot find the requested files in the cached path and outgoing traffic has been'''
                                  ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
                                  ''' to False.'''		)
                        return None

    # From now on, etag is not None.
      if os.path.exists(lowercase__		) and not force_download:
            return cache_path

      # Prevent parallel downloads of the same file with a lock.
      _lowerCamelCase           =					cache_path + '''.lock'''
      with FileLock(lowercase__		):
            # If the download just completed while the lock was activated.
            if os.path.exists(lowercase__		) and not force_download:
                  # Even if returning early like here, the lock will be released.
                  return cache_path

            if resume_download:
                  _lowerCamelCase           =					cache_path + '''.incomplete'''

                  @contextmanager
                  def _resumable_file_manager():
                        with open(lowercase__			,    '''a+b'''		) as f:
                              yield f

                  _lowerCamelCase           =					_resumable_file_manager
                  if os.path.exists(lowercase__		):
                        _lowerCamelCase           =					os.stat(lowercase__		).st_size
                  else:
                        _lowerCamelCase           =					0
            else:
                  _lowerCamelCase           =					partial(tempfile.NamedTemporaryFile			,    dir=lowercase__			,    delete=lowercase__		)
                  _lowerCamelCase           =					0

            # Download to temporary file, then copy to cache dir once finished.
            # Otherwise you get corrupt cache entries if the download gets interrupted.
            with temp_file_manager() as temp_file:
                  print(
                      '''%s not found in cache or force_download set to True, downloading to %s'''			,    lowercase__			,    temp_file.name			,    )

                  http_get(
                      lowercase__			,    lowercase__			,    proxies=lowercase__			,    resume_size=lowercase__			,    user_agent=lowercase__			,    )

            os.replace(temp_file.name			,    lowercase__		)

            _lowerCamelCase           =					{'''url''': url, '''etag''': etag}
            _lowerCamelCase           =					cache_path + '''.json'''
            with open(lowercase__			,    '''w'''		) as meta_file:
                  json.dump(lowercase__			,    lowercase__		)

      return cache_path
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=None		)    ->     int:



      '''simple docstring'''

      _lowerCamelCase           =					url.encode('''utf-8'''		)
      _lowerCamelCase           =					shaaaa(lowercase__		)
      _lowerCamelCase           =					url_hash.hexdigest()

      if etag:
            _lowerCamelCase           =					etag.encode('''utf-8'''		)
            _lowerCamelCase           =					shaaaa(lowercase__		)
            filename += "." + etag_hash.hexdigest()

      if url.endswith('''.h5'''		):
            filename += ".h5"

      return filename
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=None			,    __UpperCAmelCase=False			,    __UpperCAmelCase=None			,    __UpperCAmelCase=False			,    __UpperCAmelCase=None			,    __UpperCAmelCase=False			,    __UpperCAmelCase=False			,    __UpperCAmelCase=False			,    )    ->     List[Any]:



      '''simple docstring'''

      if cache_dir is None:
            _lowerCamelCase           =					TRANSFORMERS_CACHE
      if isinstance(lowercase__			,    lowercase__		):
            _lowerCamelCase           =					str(lowercase__		)
      if isinstance(lowercase__			,    lowercase__		):
            _lowerCamelCase           =					str(lowercase__		)

      if is_remote_url(lowercase__		):
            # URL, so get it from the cache (downloading if necessary)
            _lowerCamelCase           =					get_from_cache(
                lowercase__			,    cache_dir=lowercase__			,    force_download=lowercase__			,    proxies=lowercase__			,    resume_download=lowercase__			,    user_agent=lowercase__			,    local_files_only=lowercase__			,    )
      elif os.path.exists(lowercase__		):
            # File, and it exists.
            _lowerCamelCase           =					url_or_filename
      elif urlparse(lowercase__		).scheme == "":
            # File, but it doesn't exist.
            raise EnvironmentError('''file {} not found'''.format(lowercase__		)		)
      else:
            # Something unknown
            raise ValueError('''unable to parse {} as a URL or as a local path'''.format(lowercase__		)		)

      if extract_compressed_file:
            if not is_zipfile(lowercase__		) and not tarfile.is_tarfile(lowercase__		):
                  return output_path

            # Path where we extract compressed archives
            # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
            _lowerCamelCase     ,       _lowerCamelCase           =					os.path.split(lowercase__		)
            _lowerCamelCase           =					output_file.replace('''.'''			,    '''-'''		) + '''-extracted'''
            _lowerCamelCase           =					os.path.join(lowercase__			,    lowercase__		)

            if os.path.isdir(lowercase__		) and os.listdir(lowercase__		) and not force_extract:
                  return output_path_extracted

            # Prevent parallel extractions
            _lowerCamelCase           =					output_path + '''.lock'''
            with FileLock(lowercase__		):
                  shutil.rmtree(lowercase__			,    ignore_errors=lowercase__		)
                  os.makedirs(lowercase__		)
                  if is_zipfile(lowercase__		):
                        with ZipFile(lowercase__			,    '''r'''		) as zip_file:
                              zip_file.extractall(lowercase__		)
                              zip_file.close()
                  elif tarfile.is_tarfile(lowercase__		):
                        _lowerCamelCase           =					tarfile.open(lowercase__		)
                        tar_file.extractall(lowercase__		)
                        tar_file.close()
                  else:
                        raise EnvironmentError('''Archive format of {} could not be identified'''.format(lowercase__		)		)

            return output_path_extracted

      return output_path
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=","		)    ->     Union[str, Any]:



      '''simple docstring'''

      assert isinstance(lowercase__			,    lowercase__		)
      if os.path.isfile(lowercase__		):
            with open(lowercase__		) as f:
                  _lowerCamelCase           =					eval(f.read()		)
      else:
            _lowerCamelCase           =					requests.get(lowercase__		)
            try:
                  _lowerCamelCase           =					requests.json()
            except Exception:
                  _lowerCamelCase           =					req.content.decode()
                  assert data is not None, "could not connect"
                  try:
                        _lowerCamelCase           =					eval(lowercase__		)
                  except Exception:
                        _lowerCamelCase           =					data.split('''\n'''		)
            req.close()
      return data
def    __magic_name__(	__UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					requests.get(lowercase__		)
      _lowerCamelCase           =					np.array(Image.open(BytesIO(response.content		)		)		)
      return img
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					url.split('''/'''		)[-1]
      if fn not in os.listdir(os.getcwd()		):
            wget.download(lowercase__		)
      with open(lowercase__			,    '''rb'''		) as stream:
            _lowerCamelCase           =					pkl.load(lowercase__		)
      _lowerCamelCase           =					weights.pop('''model'''		)
      _lowerCamelCase           =					{}
      for k, v in model.items():
            _lowerCamelCase           =					torch.from_numpy(lowercase__		)
            if "running_var" in k:
                  _lowerCamelCase           =					torch.tensor([0]		)
                  _lowerCamelCase           =					k.replace('''running_var'''			,    '''num_batches_tracked'''		)
                  _lowerCamelCase           =					zero
      return new
def    __magic_name__(	)    ->     Tuple:



      '''simple docstring'''

      print(F'{os.path.abspath(os.path.join(lowercase__			,    os.pardir		)		)}/demo.ipynb'		)
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase="RGB"		)    ->     Any:



      '''simple docstring'''

      assert isinstance(lowercase__			,    lowercase__		)
      if os.path.isfile(lowercase__		):
            _lowerCamelCase           =					cva.imread(lowercase__		)
      else:
            _lowerCamelCase           =					get_image_from_url(lowercase__		)
            assert img is not None, F'could not connect to: {im}'
      _lowerCamelCase           =					cva.cvtColor(lowercase__			,    cva.COLOR_BGR2RGB		)
      if input_format == "RGB":
            _lowerCamelCase           =					img[:, :, ::-1]
      return img




def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase=1		)    ->     int:



      '''simple docstring'''

      return (images[i : i + batch] for i in range(0			,    len(lowercase__		)			,    lowercase__		))


"""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()
__A	:       str        =						logging.get_logger(__name__)

__A	:       str        =						{
    '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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					for attribute in key.split("." ):
										A     =   getattr(lowercase__     ,				lowercase__ )

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

					logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   []
					A     =   fairseq_model.state_dict()

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

					for name, value in fairseq_dict.items():
										A     =   False
										if "conv_layers" in name:
															load_conv_layer(
															    lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				hf_model.config.feat_extract_norm == "group"     ,				)
															A     =   True
										else:
															for key, mapped_key in MAPPING.items():
																				A     =   "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]:
																									A     =   True
																									if "*" in mapped_key:
																														A     =   name.split(lowercase__ )[0].split("." )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														A     =   mapped_key.replace("*"     ,				lowercase__ )
																									if "weight_g" in name:
																														A     =   "weight_g"
																									elif "weight_v" in name:
																														A     =   "weight_v"
																									elif "weight" in name:
																														A     =   "weight"
																									elif "bias" in name:
																														A     =   "bias"
																									else:
																														A     =   None
																									set_recursively(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ )
																				continue
										if not is_used:
															unused_weights.append(lowercase__ )

					logger.warning(F"""Unused weights: {unused_weights}""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   full_name.split("conv_layers." )[-1]
					A     =   name.split("." )
					A     =   int(items[0] )
					A     =   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."""
															)
															A     =   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."""
															)
															A     =   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."
															)
															A     =   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."""
															)
															A     =   value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
					else:
										unused_weights.append(lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   SEWDConfig()
					if is_finetuned:
										A     =   model.wav_encoder.wav_model.cfg
					else:
										A     =   model.cfg

					A     =   fs_config.conv_bias
					A     =   eval(fs_config.conv_feature_layers )
					A     =   [x[0] for x in conv_layers]
					A     =   [x[1] for x in conv_layers]
					A     =   [x[2] for x in conv_layers]
					A     =   "gelu"
					A     =   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
					A     =   0.0
					A     =   fs_config.activation_fn.name
					A     =   fs_config.encoder_embed_dim
					A     =   0.02
					A     =   fs_config.encoder_ffn_embed_dim
					A     =   1e-5
					A     =   fs_config.encoder_layerdrop
					A     =   fs_config.encoder_attention_heads
					A     =   fs_config.conv_pos_groups
					A     =   fs_config.conv_pos
					A     =   len(lowercase__ )
					A     =   fs_config.encoder_layers
					A     =   fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					A     =   fs_config.max_position_embeddings
					A     =   fs_config.position_buckets
					A     =   fs_config.share_att_key
					A     =   fs_config.relative_attention
					A     =   fs_config.position_biased_input
					A     =   tuple(fs_config.pos_att_type.split("|" ) )
					A     =   fs_config.norm_rel_ebd

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

					A     =   "Wav2Vec2FeatureExtractor"
					A     =   "Wav2Vec2CTCTokenizer"

					return config
@torch.no_grad()
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=True ):
					"""simple docstring"""

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

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

					A     =   True if config.feat_extract_norm == "layer" else False
					A     =   WavaVecaFeatureExtractor(
					    feature_size=1     ,				sampling_rate=16_000     ,				padding_value=0     ,				do_normalize=lowercase__     ,				return_attention_mask=lowercase__     ,				)

					if is_finetuned:
										if dict_path:
															A     =   Dictionary.load(lowercase__ )

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

										A     =   SEWDForCTC(lowercase__ )
					else:
										A     =   SEWDModel(lowercase__ )
										feature_extractor.save_pretrained(lowercase__ )

					recursively_load_weights(lowercase__     ,				lowercase__     ,				lowercase__ )

					hf_model.save_pretrained(lowercase__ )


if __name__ == "__main__":
	__A	:       Union[str, Any]        =						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'
	)
	__A	:       Tuple        =						parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)





