
'''simple docstring'''





import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a__		: List[str]  							=							get_logger(__name__)

# Datasets
a__		: Optional[Any]  							=							"https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
a__		: List[Any]  							=							"https://cdn-datasets.huggingface.co/datasets/datasets"
a__		: List[Any]  							=							"https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
a__		: Dict  							=							"https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
a__		: Any  							=							"https://cdn-datasets.huggingface.co/datasets/metric"
a__		: Tuple  							=							"https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
a__		: Tuple  							=							os.environ.get("HF_ENDPOINT", "https://huggingface.co")
a__		: Union[str, Any]  							=							HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
a__		: Optional[int]  							=							"main"

a__		: Optional[int]  							=							version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
a__		: int  							=							{"1", "ON", "YES", "TRUE"}
a__		: Any  							=							ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
a__		: Dict  							=							version.parse(importlib_metadata.version("dill"))
a__		: Tuple  							=							version.parse(importlib_metadata.version("pandas"))
a__		: str  							=							version.parse(importlib_metadata.version("pyarrow"))

a__		: int  							=							os.environ.get("USE_TF", "AUTO").upper()
a__		: str  							=							os.environ.get("USE_TORCH", "AUTO").upper()
a__		: str  							=							os.environ.get("USE_JAX", "AUTO").upper()

a__		: Any  							=							"N/A"
a__		: Dict  							=							False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
 a__		: Optional[int]  							=							importlib.util.find_spec("torch") is not None
 if TORCH_AVAILABLE:
  try:
   a__		: Tuple  							=							version.parse(importlib_metadata.version("torch"))
   logger.info(f'''PyTorch version {TORCH_VERSION} available.''')
  except importlib_metadata.PackageNotFoundError:
   pass
else:
 logger.info("Disabling PyTorch because USE_TF is set")

a__		: Dict  							=							"N/A"
a__		: int  							=							False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
 a__		: Optional[Any]  							=							importlib.util.find_spec("tensorflow") is not None
 if TF_AVAILABLE:
  # For the metadata, we have to look for both tensorflow and tensorflow-cpu
  for package in [
      "tensorflow",
      "tensorflow-cpu",
      "tensorflow-gpu",
      "tf-nightly",
      "tf-nightly-cpu",
      "tf-nightly-gpu",
      "intel-tensorflow",
      "tensorflow-rocm",
      "tensorflow-macos",
  ]:
   try:
    a__		: Any  							=							version.parse(importlib_metadata.version(package))
   except importlib_metadata.PackageNotFoundError:
    continue
   else:
    break
  else:
   a__		: List[Any]  							=							False
 if TF_AVAILABLE:
  if TF_VERSION.major < 2:
   logger.info(f'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
   a__		: Union[str, Any]  							=							False
  else:
   logger.info(f'''TensorFlow version {TF_VERSION} available.''')
else:
 logger.info("Disabling Tensorflow because USE_TORCH is set")


a__		: Tuple  							=							"N/A"
a__		: List[str]  							=							False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
 a__		: Union[str, Any]  							=							importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
 if JAX_AVAILABLE:
  try:
   a__		: Optional[int]  							=							version.parse(importlib_metadata.version("jax"))
   logger.info(f'''JAX version {JAX_VERSION} available.''')
  except importlib_metadata.PackageNotFoundError:
   pass
else:
 logger.info("Disabling JAX because USE_JAX is set to False")


a__		: Optional[Any]  							=							os.environ.get("USE_BEAM", "AUTO").upper()
a__		: Optional[Any]  							=							"N/A"
a__		: Optional[int]  							=							False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
 try:
  a__		: Tuple  							=							version.parse(importlib_metadata.version("apache_beam"))
  a__		: Tuple  							=							True
  logger.info(f'''Apache Beam version {BEAM_VERSION} available.''')
 except importlib_metadata.PackageNotFoundError:
  pass
else:
 logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
a__		: Optional[Any]  							=							importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
a__		: Any  							=							importlib.util.find_spec("PIL") is not None
a__		: Optional[int]  							=							importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
a__		: Optional[int]  							=							importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
a__		: str  							=							importlib.util.find_spec("rarfile") is not None
a__		: List[str]  							=							importlib.util.find_spec("zstandard") is not None
a__		: Tuple  							=							importlib.util.find_spec("lz4") is not None
a__		: Union[str, Any]  							=							importlib.util.find_spec("py7zr") is not None


# Cache location
a__		: Dict  							=							"~/.cache"
a__		: List[str]  							=							os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
a__		: Dict  							=							os.path.join(XDG_CACHE_HOME, "huggingface")
a__		: List[str]  							=							os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

a__		: Union[str, Any]  							=							os.path.join(HF_CACHE_HOME, "datasets")
a__		: str  							=							Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

a__		: int  							=							os.path.join(HF_CACHE_HOME, "metrics")
a__		: Optional[int]  							=							Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

a__		: Dict  							=							os.path.join(HF_CACHE_HOME, "modules")
a__		: List[Any]  							=							Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

a__		: Dict  							=							"downloads"
a__		: List[str]  							=							os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
a__		: Optional[int]  							=							Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

a__		: Tuple  							=							"extracted"
a__		: Union[str, Any]  							=							os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
a__		: Optional[int]  							=							Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
a__		: List[str]  							=							(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
a__		: Union[str, Any]  							=							1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
a__		: Any  							=							1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
a__		: Optional[Any]  							=							4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
a__		: Any  							=							"500MB"

# Parquet configuration
a__		: Any  							=							1_0_0
a__		: Tuple  							=							1_0_0
a__		: Optional[Any]  							=							1_0_0

# Offline mode
a__		: Any  							=							os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
a__		: Tuple  							=							0  # Disabled
a__		: List[Any]  							=							float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
a__		: Optional[Any]  							=							"dataset.arrow"
a__		: List[Any]  							=							"indices.arrow"
a__		: str  							=							"state.json"
a__		: List[Any]  							=							"dataset_info.json"
a__		: int  							=							"dataset_infos.json"
a__		: Dict  							=							"LICENSE"
a__		: Tuple  							=							"metric_info.json"
a__		: Union[str, Any]  							=							"dataset_dict.json"

a__		: str  							=							"datasets_modules"

a__		: List[Any]  							=							2_5_5

# Streaming
a__		: Union[str, Any]  							=							2_0
a__		: str  							=							5

# Datasets without script
a__		: Any  							=							2_0_0
a__		: Union[str, Any]  							=							1_0
a__		: Tuple  							=							2_0_0

# Progress bars
a__		: Any  							=							0.05  # 20 progress updates per sec









import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_a									=  {
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       str:

 """simple docstring"""






 for attribute in key.split('.'						):
  __lowerCAmelCase:  Optional[int]        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 if weight_type is not None:
  __lowerCAmelCase:  Dict        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						).shape
 else:
  __lowerCAmelCase:  str        = 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":
  __lowerCAmelCase:  str        = value
 elif weight_type == "weight_g":
  __lowerCAmelCase:  List[str]        = value
 elif weight_type == "weight_v":
  __lowerCAmelCase:  List[Any]        = value
 elif weight_type == "bias":
  __lowerCAmelCase:  Optional[int]        = value
 else:
  __lowerCAmelCase:  int        = value

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







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Any						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = []
 __lowerCAmelCase:  Optional[int]        = fairseq_model.state_dict()

 __lowerCAmelCase:  int        = hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:  List[str]        = False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_model.config.feat_extract_norm == 'group'       ,   )
   __lowerCAmelCase:  List[str]        = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCAmelCase:  List[Any]        = mapped_key

    if key in name:
     __lowerCAmelCase:  List[Any]        = True
     if "*" in mapped_key:
      __lowerCAmelCase:  Any        = name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
      __lowerCAmelCase:  Dict        = mapped_key.replace('*'       ,   SCREAMING_SNAKE_CASE						)
     if "weight_g" in name:
      __lowerCAmelCase:  str        = 'weight_g'
     elif "weight_v" in name:
      __lowerCAmelCase:  Tuple        = 'weight_v'
     elif "weight" in name:
      __lowerCAmelCase:  Tuple        = 'weight'
     elif "bias" in name:
      __lowerCAmelCase:  Optional[Any]        = 'bias'
     else:
      __lowerCAmelCase:  Optional[Any]        = None
     set_recursively(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE						)

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







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[int]:

 """simple docstring"""






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







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  str        = HubertConfig()
 __lowerCAmelCase:  Any        = model.config

 __lowerCAmelCase:  str        = fs_config.activation_dropout
 __lowerCAmelCase:  Optional[Any]        = False
 __lowerCAmelCase:  Tuple        = fs_config.attention_dropout
 __lowerCAmelCase:  Tuple        = False
 __lowerCAmelCase:  Tuple        = eval(fs_config.extractor_conv_feature_layers						)
 __lowerCAmelCase:  Optional[int]        = [x[0] for x in conv_layers]
 __lowerCAmelCase:  Dict        = [x[1] for x in conv_layers]
 __lowerCAmelCase:  List[str]        = [x[2] for x in conv_layers]
 __lowerCAmelCase:  Dict        = 'gelu'
 __lowerCAmelCase:  List[str]        = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCAmelCase:  int        = False
 __lowerCAmelCase:  Dict        = 0.0
 __lowerCAmelCase:  str        = 0.0
 __lowerCAmelCase:  List[Any]        = fs_config.activation_fn
 __lowerCAmelCase:  str        = fs_config.dropout
 __lowerCAmelCase:  List[str]        = fs_config.encoder_embed_dim
 __lowerCAmelCase:  str        = 0.0_2
 __lowerCAmelCase:  List[Any]        = fs_config.encoder_ffn_embed_dim
 __lowerCAmelCase:  List[str]        = 1E-5
 __lowerCAmelCase:  Optional[Any]        = 0.0
 __lowerCAmelCase:  int        = fs_config.encoder_attention_heads
 __lowerCAmelCase:  Any        = fs_config.conv_pos_groups
 __lowerCAmelCase:  str        = fs_config.conv_pos
 __lowerCAmelCase:  Optional[int]        = len(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = fs_config.encoder_layers

 return config







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=None						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = distilhubert().model.model

 if config_path is not None:
  __lowerCAmelCase:  Dict        = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  Union[str, Any]        = convert_config(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = model.eval()

 __lowerCAmelCase:  Tuple        = WavaVecaFeatureExtractor(
     feature_size=1       ,   sampling_rate=1_60_00       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )
 __lowerCAmelCase:  Tuple        = HubertModel(SCREAMING_SNAKE_CASE						)

 recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  _a									=  argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  _a									=  parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





