


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
A      :    str		=					logging.get_logger(__name__)

A      :    Any		=					{
    "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      :    List[str]		=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]

def       lowercase_		(		_A			:    Any    ,					_A			:    Optional[Any]    ,					_A			:    Dict    ,					_A			:    int    ,					_A			:    List[Any]						):

      """simple docstring"""




      for attribute in key.split("."						):
            lowerCamelCase__		:				Any    =  getattr(__snake_case    ,					__snake_case						)

      if weight_type is not None:
            lowerCamelCase__		:				Optional[int]    =  getattr(__snake_case    ,					__snake_case						).shape
      else:
            lowerCamelCase__		:				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__		:				Tuple    =  value
      elif weight_type == "weight_g":
            lowerCamelCase__		:				Dict    =  value
      elif weight_type == "weight_v":
            lowerCamelCase__		:				Union[str, Any]    =  value
      elif weight_type == "bias":
            lowerCamelCase__		:				Union[str, Any]    =  value
      else:
            lowerCamelCase__		:				Tuple    =  value

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

def       lowercase_		(		_A			:    str    ,					_A			:    Dict						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  []
      lowerCamelCase__		:				Optional[Any]    =  fairseq_model.state_dict()

      lowerCamelCase__		:				Optional[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(
                      __snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					hf_model.config.feat_extract_norm == "group"    ,					)
                  lowerCamelCase__		:				List[Any]    =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowerCamelCase__		:				int    =  "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__		:				Optional[int]    =  True
                              if "*" in mapped_key:
                                    lowerCamelCase__		:				List[str]    =  name.split(__snake_case						)[0].split("."						)[-2]
                                    lowerCamelCase__		:				Any    =  mapped_key.replace("*"    ,					__snake_case						)
                              if "weight_g" in name:
                                    lowerCamelCase__		:				Dict    =  "weight_g"
                              elif "weight_v" in name:
                                    lowerCamelCase__		:				int    =  "weight_v"
                              elif "bias" in name:
                                    lowerCamelCase__		:				Tuple    =  "bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    lowerCamelCase__		:				Tuple    =  "weight"
                              else:
                                    lowerCamelCase__		:				int    =  None
                              set_recursively(__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case						)
                        continue
            if not is_used:
                  unused_weights.append(__snake_case						)

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

def       lowercase_		(		_A			:    Dict    ,					_A			:    List[Any]    ,					_A			:    Union[str, Any]    ,					_A			:    int    ,					_A			:    Tuple						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  full_name.split("conv_layers."						)[-1]
      lowerCamelCase__		:				Optional[Any]    =  name.split("."						)
      lowerCamelCase__		:				Optional[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__		:				Optional[int]    =  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__		:				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:
                  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__		:				Union[str, Any]    =  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(__snake_case						)

@torch.no_grad()
def       lowercase_		(		_A			:    Optional[Any]    ,					_A			:    Optional[int]    ,					_A			:    Dict=None    ,					_A			:    Optional[int]=None    ,					_A			:    Dict=True						):

      """simple docstring"""




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

      lowerCamelCase__		:				Dict    =  ""

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

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

      recursively_load_weights(__snake_case    ,					__snake_case						)

      hf_wavavec.save_pretrained(__snake_case						)


if __name__ == "__main__":
    A      :    int		=					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      :    Tuple		=					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
    )






from typing import TYPE_CHECKING

from ..utils import _LazyModule


UpperCAmelCase__   	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
  from .config import (
      EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
      OnnxConfig,
      OnnxConfigWithPast,
      OnnxSeqaSeqConfigWithPast,
      PatchingSpec,
  )
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import ParameterFormat, compute_serialized_parameters_size

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

