





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__A									=				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  for attribute in key.split("."		):
    lowerCamelCase__:      str			  =getattr(__a	,    __a		)

  if weight_type is not None:
    lowerCamelCase__:      Tuple			  =getattr(__a	,    __a		).shape
  else:
    lowerCamelCase__:      List[str]			  =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":
    lowerCamelCase__:      List[Any]			  =value
  elif weight_type == "weight_g":
    lowerCamelCase__:      Tuple			  =value
  elif weight_type == "weight_v":
    lowerCamelCase__:      List[str]			  =value
  elif weight_type == "bias":
    lowerCamelCase__:      List[str]			  =value
  else:
    lowerCamelCase__:      str			  =value

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





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =[]
  lowerCamelCase__:      List[str]			  =fairseq_model.state_dict()

  lowerCamelCase__:      Any			  =hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCamelCase__:      Tuple			  =False
    if "conv_layers" in name:
      load_conv_layer(
          __a	,    __a	,    __a	,    __a	,    hf_model.config.feat_extract_norm == "group"	,    )
      lowerCamelCase__:      Optional[int]			  =True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCamelCase__:      List[Any]			  ="unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
          if "layer_norm_for_extract" in name and (".".join(name.split("."		)[:-1]		) != key):
            # special case since naming is very similar
            continue
          lowerCamelCase__:      str			  =True
          if "*" in mapped_key:
            lowerCamelCase__:      List[str]			  =name.split(__a		)[0].split("."		)[-2]
            lowerCamelCase__:      Tuple			  =mapped_key.replace("*"	,    __a		)
          if "weight_g" in name:
            lowerCamelCase__:      str			  ="weight_g"
          elif "weight_v" in name:
            lowerCamelCase__:      Union[str, Any]			  ="weight_v"
          elif "bias" in name:
            lowerCamelCase__:      Dict			  ="bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCamelCase__:      Dict			  ="weight"
          else:
            lowerCamelCase__:      Any			  =None
          set_recursively(__a	,    __a	,    __a	,    __a	,    __a		)
        continue
    if not is_used:
      unused_weights.append(__a		)

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





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Any			  =full_name.split("conv_layers."		)[-1]
  lowerCamelCase__:      Tuple			  =name.split("."		)
  lowerCamelCase__:      Any			  =int(items[0]		)
  lowerCamelCase__:      Optional[int]			  =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."""		)
      lowerCamelCase__:      List[Any]			  =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."""		)
      lowerCamelCase__:      Any			  =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[layer_id].layer_norm.bias.data.shape} was found."""		)
      lowerCamelCase__:      Optional[int]			  =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[layer_id].layer_norm.weight.data.shape} was found."""		)
      lowerCamelCase__:      str			  =value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
  else:
    unused_weights.append(__a		)





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a=None	,    __a=None	,    __a=True		)     ->		Optional[Any]:







  """simple docstring"""





  if config_path is not None:
    lowerCamelCase__:      Dict			  =UniSpeechSatConfig.from_pretrained(__a		)
  else:
    lowerCamelCase__:      Union[str, Any]			  =UniSpeechSatConfig()

  lowerCamelCase__:      Any			  =""

  if is_finetuned:
    lowerCamelCase__:      Optional[int]			  =UniSpeechSatForCTC(__a		)
  else:
    lowerCamelCase__:      Optional[int]			  =UniSpeechSatForPreTraining(__a		)

  lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Optional[int]			  =fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]	,    arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
  lowerCamelCase__:      Optional[Any]			  =model[0].eval()

  recursively_load_weights(__a	,    __a		)

  hf_wavavec.save_pretrained(__a		)


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("--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(
         "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
     )
     __A									=				parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

