





import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


_UpperCAmelCase        =				get_logger(__name__)



class 							_UpperCamelCase					:

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  (
                  os.path.join(_SCREAMING_SNAKE_CASE					,   config.EXTRACTED_DATASETS_DIR		) if cache_dir else config.EXTRACTED_DATASETS_PATH
              )
              UpperCamelCase_          =  Extractor

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: str		)       -> str:




              """simple docstring"""


              from .file_utils import hash_url_to_filename

              # Path where we extract compressed archives
              # We extract in the cache dir, and get the extracted path name by hashing the original path"
              UpperCamelCase_          =  os.path.abspath(_SCREAMING_SNAKE_CASE		)
              return os.path.join(self.extract_dir					,   hash_url_to_filename(_SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: bool		)       -> bool:




              """simple docstring"""


              return force_extract or (
                  not os.path.isfile(_SCREAMING_SNAKE_CASE		) and not (os.path.isdir(_SCREAMING_SNAKE_CASE		) and os.listdir(_SCREAMING_SNAKE_CASE		))
              )



       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: bool = False		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  self.extractor.infer_extractor_format(_SCREAMING_SNAKE_CASE		)
              if not extractor_format:
                     return input_path
              UpperCamelCase_          =  self._get_output_path(_SCREAMING_SNAKE_CASE		)
              if self._do_extract(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     self.extractor.extract(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              return output_path



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       @classmethod
       @abstractmethod
       def lowercase (		cls: Tuple					,   _SCREAMING_SNAKE_CASE: Union[Path, str]					,   **_SCREAMING_SNAKE_CASE: Any		)       -> bool:




              """simple docstring"""


              ...



       @staticmethod
       @abstractmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              ...



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						lowerCAmelCase_     ):
       _UpperCamelCase				:					List[bytes]         =   []

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: int		)       -> Optional[int]:




              """simple docstring"""


              with open(_SCREAMING_SNAKE_CASE					,   "rb"		) as f:
                     return f.read(_SCREAMING_SNAKE_CASE		)



       @classmethod
       def lowercase (		cls: Any					,   _SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: bytes = b""		)       -> bool:




              """simple docstring"""


              if not magic_number:
                     UpperCamelCase_          =  max(len(_SCREAMING_SNAKE_CASE		) for cls_magic_number in cls.magic_numbers		)
                     try:
                            UpperCamelCase_          =  cls.read_magic_number(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                     except OSError:
                            return False
              return any(magic_number.startswith(_SCREAMING_SNAKE_CASE		) for cls_magic_number in cls.magic_numbers		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       @classmethod
       def lowercase (		cls: List[str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]					,   **_SCREAMING_SNAKE_CASE: Optional[Any]		)       -> bool:




              """simple docstring"""


              return tarfile.is_tarfile(_SCREAMING_SNAKE_CASE		)

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: List[Any]		)       -> Optional[Any]:




              """simple docstring"""


              def resolved(_SCREAMING_SNAKE_CASE: str		) -> str:
                     return os.path.realpath(os.path.abspath(_SCREAMING_SNAKE_CASE		)		)

              def badpath(_SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: str		) -> bool:
                     # joinpath will ignore base if path is absolute
                     return not resolved(os.path.join(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)		).startswith(_SCREAMING_SNAKE_CASE		)

              def badlink(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: str		) -> bool:
                     # Links are interpreted relative to the directory containing the link
                     UpperCamelCase_          =  resolved(os.path.join(_SCREAMING_SNAKE_CASE					,   os.path.dirname(info.name		)		)		)
                     return badpath(info.linkname					,   base=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  resolved(_SCREAMING_SNAKE_CASE		)

              for finfo in members:
                     if badpath(finfo.name					,   _SCREAMING_SNAKE_CASE		):
                            logger.error(f'''Extraction of {finfo.name} is blocked (illegal path)'''		)
                     elif finfo.issym() and badlink(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                            logger.error(f'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}'''		)
                     elif finfo.islnk() and badlink(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                            logger.error(f'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}'''		)
                     else:
                            yield finfo



       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              os.makedirs(_SCREAMING_SNAKE_CASE					,   exist_ok=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tarfile.open(_SCREAMING_SNAKE_CASE		)
              tar_file.extractall(_SCREAMING_SNAKE_CASE					,   members=TarExtractor.safemembers(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)		)
              tar_file.close()



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[Any]         =   [b'''\x1F\x8B''']

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              with gzip.open(_SCREAMING_SNAKE_CASE					,   "rb"		) as gzip_file:
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as extracted_file:
                            shutil.copyfileobj(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   [
           b'''PK\x03\x04''',
           b'''PK\x05\x06''',  # empty archive
           b'''PK\x07\x08''',  # spanned archive
       ]

       @classmethod
       def lowercase (		cls: List[str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: bytes = b""		)       -> bool:




              """simple docstring"""


              if super().is_extractable(_SCREAMING_SNAKE_CASE					,   magic_number=_SCREAMING_SNAKE_CASE		):
                     return True
              try:
                     # Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
                     # From: https://github.com/python/cpython/pull/5053
                     from zipfile import (
                         _CD_SIGNATURE,
                         _ECD_DISK_NUMBER,
                         _ECD_DISK_START,
                         _ECD_ENTRIES_TOTAL,
                         _ECD_OFFSET,
                         _ECD_SIZE,
                         _EndRecData,
                         sizeCentralDir,
                         stringCentralDir,
                         structCentralDir,
                     )

                     with open(_SCREAMING_SNAKE_CASE					,   "rb"		) as fp:
                            UpperCamelCase_          =  _EndRecData(_SCREAMING_SNAKE_CASE		)
                            if endrec:
                                   if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
                                          return True  # Empty zipfiles are still zipfiles
                                   elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
                                          fp.seek(endrec[_ECD_OFFSET]		)  # Central directory is on the same disk
                                          if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
                                                 UpperCamelCase_          =  fp.read(_SCREAMING_SNAKE_CASE		)  # CD is where we expect it to be
                                                 if len(_SCREAMING_SNAKE_CASE		) == sizeCentralDir:
                                                        UpperCamelCase_          =  struct.unpack(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)  # CD is the right size
                                                        if centdir[_CD_SIGNATURE] == stringCentralDir:
                                                               return True  # First central directory entry  has correct magic number
                     return False
              except Exception:  # catch all errors in case future python versions change the zipfile internals
                     return False



       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              os.makedirs(_SCREAMING_SNAKE_CASE					,   exist_ok=_SCREAMING_SNAKE_CASE		)
              with zipfile.ZipFile(_SCREAMING_SNAKE_CASE					,   "r"		) as zip_file:
                     zip_file.extractall(_SCREAMING_SNAKE_CASE		)
                     zip_file.close()



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   [b'''\xFD\x37\x7A\x58\x5A\x00''']

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              with lzma.open(_SCREAMING_SNAKE_CASE		) as compressed_file:
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as extracted_file:
                            shutil.copyfileobj(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Tuple         =   [b'''Rar!\x1a\x07\x00''', b'''Rar!\x1a\x07\x01\x00''']  # RAR_ID  # RAR5_ID

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              if not config.RARFILE_AVAILABLE:
                     raise ImportError("Please pip install rarfile"		)
              import rarfile

              os.makedirs(_SCREAMING_SNAKE_CASE					,   exist_ok=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  rarfile.RarFile(_SCREAMING_SNAKE_CASE		)
              rf.extractall(_SCREAMING_SNAKE_CASE		)
              rf.close()



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   [b'''\x28\xb5\x2F\xFD''']

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              if not config.ZSTANDARD_AVAILABLE:
                     raise ImportError("Please pip install zstandard"		)
              import zstandard as zstd

              UpperCamelCase_          =  zstd.ZstdDecompressor()
              with open(_SCREAMING_SNAKE_CASE					,   "rb"		) as ifh, open(_SCREAMING_SNAKE_CASE					,   "wb"		) as ofh:
                     dctx.copy_stream(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Any         =   [b'''\x42\x5A\x68''']

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              with bza.open(_SCREAMING_SNAKE_CASE					,   "rb"		) as compressed_file:
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as extracted_file:
                            shutil.copyfileobj(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Tuple         =   [b'''\x37\x7A\xBC\xAF\x27\x1C''']

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              if not config.PY7ZR_AVAILABLE:
                     raise ImportError("Please pip install py7zr"		)
              import pyazr

              os.makedirs(_SCREAMING_SNAKE_CASE					,   exist_ok=_SCREAMING_SNAKE_CASE		)
              with pyazr.SevenZipFile(_SCREAMING_SNAKE_CASE					,   "r"		) as archive:
                     archive.extractall(_SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   [b'''\x04\x22\x4D\x18''']

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> None:




              """simple docstring"""


              if not config.LZ4_AVAILABLE:
                     raise ImportError("Please pip install lz4"		)
              import lza.frame

              with lza.frame.open(_SCREAMING_SNAKE_CASE					,   "rb"		) as compressed_file:
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as extracted_file:
                            shutil.copyfileobj(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)



class 							_UpperCamelCase					:
       #  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
       _UpperCamelCase				:					Dict[str, Type[BaseExtractor]]         =   {
           "tar": TarExtractor,
           "gzip": GzipExtractor,
           "zip": ZipExtractor,
           "xz": XzExtractor,
           "rar": RarExtractor,
           "zstd": ZstdExtractor,
           "bz2": BzipaExtractor,
           "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
           "lz4": LzaExtractor,  # <Added version="2.4.0"/>
       }

       @classmethod
       def lowercase (		cls: List[str]		)       -> int:




              """simple docstring"""


              return max(
                  len(_SCREAMING_SNAKE_CASE		)
                  for extractor in cls.extractors.values()
                  if issubclass(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                  for extractor_magic_number in extractor.magic_numbers		)

       @staticmethod
       def lowercase (		_SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: int		)       -> str:




              """simple docstring"""


              try:
                     return MagicNumberBaseExtractor.read_magic_number(_SCREAMING_SNAKE_CASE					,   magic_number_length=_SCREAMING_SNAKE_CASE		)
              except OSError:
                     return b""

       @classmethod
       def lowercase (		cls: Any					,   _SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: bool = False		)       -> bool:




              """simple docstring"""


              warnings.warn(
                  "Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
                  "Use 'infer_extractor_format' instead."					,   category=_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  cls.infer_extractor_format(_SCREAMING_SNAKE_CASE		)
              if extractor_format:
                     return True if not return_extractor else (True, cls.extractors[extractor_format])
              return False if not return_extractor else (False, None)

       @classmethod
       def lowercase (		cls: Optional[int]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]		)       -> str:  # <Added version="2.4.0"/>




              """simple docstring"""


              UpperCamelCase_          =  cls._get_magic_number_max_length()
              UpperCamelCase_          =  cls._read_magic_number(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
              for extractor_format, extractor in cls.extractors.items():
                     if extractor.is_extractable(_SCREAMING_SNAKE_CASE					,   magic_number=_SCREAMING_SNAKE_CASE		):
                            return extractor_format



       @classmethod
       def lowercase (		cls: Dict					,   _SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Union[Path, str]					,   _SCREAMING_SNAKE_CASE: Optional[str] = None					,   _SCREAMING_SNAKE_CASE: Optional[BaseExtractor] = "deprecated"					,   )       -> None:




              """simple docstring"""


              os.makedirs(os.path.dirname(_SCREAMING_SNAKE_CASE		)					,   exist_ok=_SCREAMING_SNAKE_CASE		)
              # Prevent parallel extractions
              UpperCamelCase_          =  str(Path(_SCREAMING_SNAKE_CASE		).with_suffix(".lock"		)		)
              with FileLock(_SCREAMING_SNAKE_CASE		):
                     shutil.rmtree(_SCREAMING_SNAKE_CASE					,   ignore_errors=_SCREAMING_SNAKE_CASE		)
                     if extractor_format or extractor != "deprecated":
                            if extractor != "deprecated" or not isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):  # passed as positional arg
                                   warnings.warn(
                                       "Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
                                       "Use 'extractor_format' instead."					,   category=_SCREAMING_SNAKE_CASE					,   )
                                   UpperCamelCase_          =  extractor if extractor != "deprecated" else extractor_format
                            else:
                                   UpperCamelCase_          =  cls.extractors[extractor_format]
                            return extractor.extract(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                     else:
                            warnings.warn(
                                "Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
                                "exception in 3.0.0."					,   category=_SCREAMING_SNAKE_CASE					,   )
                            for extractor in cls.extractors.values():
                                   if extractor.is_extractable(_SCREAMING_SNAKE_CASE		):
                                          return extractor.extract(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)








import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_UpperCAmelCase        =				logging.get_logger('transformers.models.speecht5')

_UpperCAmelCase        =				{
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
_UpperCAmelCase        =				{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
_UpperCAmelCase        =				{
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
_UpperCAmelCase        =				{
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
_UpperCAmelCase        =				{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
_UpperCAmelCase        =				{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
_UpperCAmelCase        =				{
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
_UpperCAmelCase        =				{
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCAmelCase        =				[]
_UpperCAmelCase        =				[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
_UpperCAmelCase        =				IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Any:
       for attribute in key.split("."  ):
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  )

       if weight_type is not None:
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  ).shape
       else:
              UpperCamelCase_          =  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":
              UpperCamelCase_          =  value
       elif weight_type == "weight_g":
              UpperCamelCase_          =  value
       elif weight_type == "weight_v":
              UpperCamelCase_          =  value
       elif weight_type == "bias":
              UpperCamelCase_          =  value
       elif weight_type == "running_mean":
              UpperCamelCase_          =  value
       elif weight_type == "running_var":
              UpperCamelCase_          =  value
       elif weight_type == "num_batches_tracked":
              UpperCamelCase_          =  value
       else:
              UpperCamelCase_          =  value

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       for key in ignore_keys:
              if key.endswith(".*"  ):
                     if name.startswith(key[:-1]  ):
                            return True
              elif ".*." in key:
                     UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[Any]:
       UpperCamelCase_          =  []

       if task == "s2t":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2T
              UpperCamelCase_          =  IGNORE_KEYS_S2T
       elif task == "t2s":
              UpperCamelCase_          =  None
              UpperCamelCase_          =  MAPPING_T2S
              UpperCamelCase_          =  IGNORE_KEYS_T2S
       elif task == "s2s":
              UpperCamelCase_          =  hf_model.speechta.encoder.prenet.feature_encoder
              UpperCamelCase_          =  MAPPING_S2S
              UpperCamelCase_          =  IGNORE_KEYS_S2S
       else:
              raise ValueError(F'''Unsupported task: {task}'''  )

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

              UpperCamelCase_          =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		hf_model.config.feat_extract_norm == "group"					,		)
                     UpperCamelCase_          =  True
              else:
                     for key, mapped_key in MAPPING.items():
                            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                            if "*" in key:
                                   UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                                   if prefix in name and suffix in name:
                                          UpperCamelCase_          =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   UpperCamelCase_          =  True
                                   if "*" in mapped_key:
                                          UpperCamelCase_          =  name.split(UpperCamelCase_  )[0].split("."  )[-2]
                                          UpperCamelCase_          =  mapped_key.replace("*"					,		UpperCamelCase_  )
                                   if "weight_g" in name:
                                          UpperCamelCase_          =  "weight_g"
                                   elif "weight_v" in name:
                                          UpperCamelCase_          =  "weight_v"
                                   elif "bias" in name:
                                          UpperCamelCase_          =  "bias"
                                   elif "weight" in name:
                                          UpperCamelCase_          =  "weight"
                                   elif "running_mean" in name:
                                          UpperCamelCase_          =  "running_mean"
                                   elif "running_var" in name:
                                          UpperCamelCase_          =  "running_var"
                                   elif "num_batches_tracked" in name:
                                          UpperCamelCase_          =  "num_batches_tracked"
                                   else:
                                          UpperCamelCase_          =  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_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       UpperCamelCase_          =  full_name.split("conv_layers."  )[-1]
       UpperCamelCase_          =  name.split("."  )
       UpperCamelCase_          =  int(items[0]  )
       UpperCamelCase_          =  int(items[1]  )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''  )
                     UpperCamelCase_          =  value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''  )
                     UpperCamelCase_          =  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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''  )
                     UpperCamelCase_          =  value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''  )
                     UpperCamelCase_          =  value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
       else:
              unused_weights.append(UpperCamelCase_  )




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		)     ->		str:
       if config_path is not None:
              UpperCamelCase_          =  SpeechTaConfig.from_pretrained(UpperCamelCase_  )
       else:
              UpperCamelCase_          =  SpeechTaConfig()

       if task == "s2t":
              UpperCamelCase_          =  config.max_text_positions
              UpperCamelCase_          =  SpeechTaForSpeechToText(UpperCamelCase_  )
       elif task == "t2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  600
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForTextToSpeech(UpperCamelCase_  )
       elif task == "s2s":
              UpperCamelCase_          =  1876
              UpperCamelCase_          =  config.max_speech_positions
              UpperCamelCase_          =  SpeechTaForSpeechToSpeech(UpperCamelCase_  )
       else:
              raise ValueError(F'''Unknown task name: {task}'''  )

       if vocab_path:
              UpperCamelCase_          =  SpeechTaTokenizer(UpperCamelCase_					,		model_max_length=config.max_text_positions  )

              # Mask token behaves like a normal word, i.e. include the space before it
              UpperCamelCase_          =  AddedToken("<mask>"					,		lstrip=UpperCamelCase_					,		rstrip=UpperCamelCase_  )
              UpperCamelCase_          =  mask_token
              tokenizer.add_special_tokens({"mask_token": mask_token}  )
              tokenizer.add_tokens(["<ctc_blank>"]  )

       UpperCamelCase_          =  SpeechTaFeatureExtractor()
       UpperCamelCase_          =  SpeechTaProcessor(tokenizer=UpperCamelCase_					,		feature_extractor=UpperCamelCase_  )
       processor.save_pretrained(UpperCamelCase_  )

       UpperCamelCase_          =  torch.load(UpperCamelCase_  )
       recursively_load_weights(fairseq_checkpoint["model"]					,		UpperCamelCase_					,		UpperCamelCase_  )

       model.save_pretrained(UpperCamelCase_  )

       if repo_id:
              print("Pushing to the hub..."  )
              processor.push_to_hub(UpperCamelCase_  )
              model.push_to_hub(UpperCamelCase_  )


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     parser.add_argument(
         '--task',
         default='s2t',
         type=str,
         help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
     )
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
     parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
     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.'
     )

     _UpperCAmelCase        =				parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )

