


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A      :    List[str]		=					logging.get_logger(__name__)

A      :    Tuple		=					{
    "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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
A      :    Any		=					[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

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

      """simple docstring"""




      for attribute in key.split("."						):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        lowerCamelCase__		:				Optional[int]    =  "lm_head"

            lowerCamelCase__		:				Any    =  getattr(_A    ,					_A						)

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

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

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

      """simple docstring"""




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

      lowerCamelCase__		:				Any    =  hf_model.unispeech.feature_extractor

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

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

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

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  full_name.split("conv_layers."						)[-1]
      lowerCamelCase__		:				Dict    =  name.split("."						)
      lowerCamelCase__		:				Union[str, Any]    =  int(items[0]						)
      lowerCamelCase__		:				Optional[Any]    =  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__		:				List[Any]    =  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__		:				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:
                  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(_A						)

@torch.no_grad()
def       lowercase_		(		_A			:    List[str]    ,					_A			:    Tuple    ,					_A			:    List[str]=None    ,					_A			:    int=None    ,					_A			:    int=True						):

      """simple docstring"""




      if config_path is not None:
            lowerCamelCase__		:				Optional[Any]    =  UniSpeechConfig.from_pretrained(_A						)
      else:
            lowerCamelCase__		:				str    =  UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  lowerCamelCase__		:				str    =  Dictionary.load_from_json(_A						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowerCamelCase__		:				Optional[int]    =  target_dict.pad_index
                  lowerCamelCase__		:				Dict    =  target_dict.bos_index
                  lowerCamelCase__		:				List[Any]    =  target_dict.eos_index
                  lowerCamelCase__		:				List[Any]    =  len(target_dict.symbols						)
                  lowerCamelCase__		:				str    =  os.path.join(_A    ,					"vocab.json"						)
                  if not os.path.isdir(_A						):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_A						)						)
                        return
                  os.makedirs(_A    ,					exist_ok=_A						)
                  lowerCamelCase__		:				Union[str, Any]    =  target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  lowerCamelCase__		:				Optional[Any]    =  42
                  lowerCamelCase__		:				Dict    =  43
                  with open(_A    ,					"w"    ,					encoding="utf-8"						) as vocab_handle:
                        json.dump(_A    ,					_A						)
                  lowerCamelCase__		:				Dict    =  WavaVecaPhonemeCTCTokenizer(
                      _A    ,					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=_A    ,					)
                  lowerCamelCase__		:				Optional[Any]    =  True if config.feat_extract_norm == "layer" else False
                  lowerCamelCase__		:				Dict    =  WavaVecaFeatureExtractor(
                      feature_size=1    ,					sampling_rate=16000    ,					padding_value=0    ,					do_normalize=_A    ,					return_attention_mask=_A    ,					)
                  lowerCamelCase__		:				int    =  WavaVecaProcessor(feature_extractor=_A    ,					tokenizer=_A						)
                  processor.save_pretrained(_A						)

            lowerCamelCase__		:				Tuple    =  UniSpeechForCTC(_A						)
      else:
            lowerCamelCase__		:				Optional[Any]    =  UniSpeechForPreTraining(_A						)

      if is_finetuned:
            lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]    ,					arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						), "w2v_path": checkpoint_path}						)
      else:
            lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

      lowerCamelCase__		:				List[Any]    =  model[0].eval()

      recursively_load_weights(_A    ,					_A    ,					_A						)

      hf_unispeech.save_pretrained(_A						)


if __name__ == "__main__":
    A      :    Optional[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(
        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    A      :    List[Any]		=					parser.parse_args()
    convert_unispeech_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 Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase__)
class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			field(default="automatic-speech-recognition"							,  metadata={"include_in_asdict_even_if_is_default": True})
       A__											=			Features({"audio": Audio()})
       A__											=			Features({"transcription": Value("string")})
       A__											=			"audio"
       A__											=			"transcription"





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             if self.audio_column not in features:
                   raise ValueError(f"Column {self.audio_column} is not present in features."       )
             if not isinstance(features[self.audio_column]					,      __lowerCamelCase       ):
                   raise ValueError(f"Column {self.audio_column} is not an Audio type."       )
             lowerCamelCase__		:				Tuple    =  copy.deepcopy(self       )
             lowerCamelCase__		:				Tuple    =  self.input_schema.copy()
             lowerCamelCase__		:				Optional[int]    =  features[self.audio_column]
             lowerCamelCase__		:				int    =  input_schema
             return task_template





       @property
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             return {self.audio_column: "audio", self.transcription_column: "transcription"}


