
"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_a      :			Dict					=							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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
_a      :			Optional[int]					=							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def 				a__   (   a   :				Optional[Any]		):

  """simple docstring"""







  _snake_case     :						Tuple						=   {}
  with open(lowerCAmelCase_       ,     "r"		) as file:
    for line_number, line in enumerate(lowerCAmelCase_		):
      _snake_case     :						Any						=   line.strip()
      if line:
        _snake_case     :						List[Any]						=   line.split()
        _snake_case     :						List[Any]						=   line_number
        _snake_case     :						Optional[int]						=   words[0]
        _snake_case     :						int						=   value
  return result
def 				a__   (   a   :				int       ,     a   :				str       ,     a   :				int       ,     a   :				Tuple       ,     a   :				Optional[Any]		):

  """simple docstring"""







  for attribute in key.split("."		):
    _snake_case     :						Optional[int]						=   getattr(lowerCAmelCase_       ,     lowerCAmelCase_		)

  _snake_case     :						Union[str, Any]						=   None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowerCAmelCase_		):
      _snake_case     :						Optional[int]						=   PARAM_MAPPING[full_name.split("."		)[-1]]
      _snake_case     :						Dict						=   '''param'''

  if weight_type is not None and weight_type != "param":
    _snake_case     :						List[str]						=   getattr(lowerCAmelCase_       ,     lowerCAmelCase_		).shape
  elif weight_type is not None and weight_type == "param":
    _snake_case     :						int						=   hf_pointer
    for attribute in hf_param_name.split("."		):
      _snake_case     :						str						=   getattr(lowerCAmelCase_       ,     lowerCAmelCase_		)
    _snake_case     :						Optional[int]						=   shape_pointer.shape

    # let's reduce dimension
    _snake_case     :						Optional[int]						=   value[0]
  else:
    _snake_case     :						Optional[Any]						=   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":
    _snake_case     :						Any						=   value
  elif weight_type == "weight_g":
    _snake_case     :						Dict						=   value
  elif weight_type == "weight_v":
    _snake_case     :						Dict						=   value
  elif weight_type == "bias":
    _snake_case     :						Optional[Any]						=   value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."		):
      _snake_case     :						List[Any]						=   getattr(lowerCAmelCase_       ,     lowerCAmelCase_		)
    _snake_case     :						Optional[Any]						=   value
  else:
    _snake_case     :						Tuple						=   value

  logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def 				a__   (   a   :				Dict       ,     a   :				Any       ,     a   :				List[Any]       ,     a   :				Tuple       ,     a   :				Any		):

  """simple docstring"""







  _snake_case     :						str						=   None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(lowerCAmelCase_		):
      _snake_case     :						int						=   PARAM_MAPPING[full_name.split("."		)[-1]]
      _snake_case     :						Dict						=   '''param'''

  if weight_type is not None and weight_type != "param":
    _snake_case     :						str						=   '''.'''.join([key, weight_type]		)
  elif weight_type is not None and weight_type == "param":
    _snake_case     :						int						=   '''.'''.join([key, hf_param_name]		)
  else:
    _snake_case     :						List[str]						=   key

  _snake_case     :						Optional[int]						=   value if '''lm_head''' in full_key else value[0]


_a      :			Optional[int]					=							{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}
def 				a__   (   a   :				Any       ,     a   :				Tuple       ,     a   :				str=None       ,     a   :				List[str]=None		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   False
  for key, mapped_key in MAPPING.items():
    _snake_case     :						List[Any]						=   '''wav2vec2.''' + 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]:
      _snake_case     :						Dict						=   True
      if "*" in mapped_key:
        _snake_case     :						Any						=   name.split(lowerCAmelCase_		)[0].split("."		)[-2]
        _snake_case     :						str						=   mapped_key.replace("*"       ,     lowerCAmelCase_		)
      if "weight_g" in name:
        _snake_case     :						str						=   '''weight_g'''
      elif "weight_v" in name:
        _snake_case     :						List[Any]						=   '''weight_v'''
      elif "bias" in name:
        _snake_case     :						List[Any]						=   '''bias'''
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _snake_case     :						int						=   '''weight'''
      else:
        _snake_case     :						Optional[int]						=   None
      if hf_dict is not None:
        rename_dict(lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_		)
      else:
        set_recursively(lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_		)
      return is_used
  return is_used
def 				a__   (   a   :				int       ,     a   :				Any       ,     a   :				int		):

  """simple docstring"""







  _snake_case     :						str						=   []
  _snake_case     :						Optional[int]						=   fairseq_model.state_dict()

  _snake_case     :						Optional[int]						=   hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case     :						Dict						=   False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_       ,     hf_model.config.feat_extract_norm == "group"       ,     )
      _snake_case     :						Optional[int]						=   True
    else:
      _snake_case     :						Optional[Any]						=   load_wavaveca_layer(lowerCAmelCase_       ,     lowerCAmelCase_       ,     lowerCAmelCase_		)
    if not is_used:
      unused_weights.append(lowerCAmelCase_		)

  logger.warning(f'Unused weights: {unused_weights}'		)
def 				a__   (   a   :				Union[str, Any]       ,     a   :				Optional[Any]       ,     a   :				Any       ,     a   :				Dict       ,     a   :				Optional[int]		):

  """simple docstring"""







  _snake_case     :						List[str]						=   full_name.split("conv_layers."		)[-1]
  _snake_case     :						Tuple						=   name.split("."		)
  _snake_case     :						int						=   int(items[0]		)
  _snake_case     :						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.'		)
      _snake_case     :						Dict						=   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.'		)
      _snake_case     :						Dict						=   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.'		)
      _snake_case     :						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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'		)
      _snake_case     :						List[str]						=   value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
  else:
    unused_weights.append(lowerCAmelCase_		)
@torch.no_grad()
def 				a__   (   a   :				List[Any]       ,     a   :				Any       ,     a   :				Optional[int]=None       ,     a   :				Tuple=None       ,     a   :				List[Any]=True       ,     a   :				Union[str, Any]=False		):

  """simple docstring"""







  if config_path is not None:
    _snake_case     :						Dict						=   WavaVecaConfig.from_pretrained(lowerCAmelCase_		)
  else:
    _snake_case     :						Tuple						=   WavaVecaConfig()

  if is_seq_class:
    _snake_case     :						List[str]						=   read_txt_into_dict(lowerCAmelCase_		)
    _snake_case     :						Optional[Any]						=   idalabel
    _snake_case     :						Dict						=   WavaVecaForSequenceClassification(lowerCAmelCase_		)
    _snake_case     :						List[Any]						=   WavaVecaFeatureExtractor(
        feature_size=1       ,     sampling_rate=16_000       ,     padding_value=0       ,     do_normalize=lowerCAmelCase_       ,     return_attention_mask=lowerCAmelCase_       ,     )
    feature_extractor.save_pretrained(lowerCAmelCase_		)

  elif is_finetuned:
    if dict_path:
      _snake_case     :						int						=   Dictionary.load(lowerCAmelCase_		)

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

      # fairseq has the <pad> and <s> switched
      _snake_case     :						Any						=   0
      _snake_case     :						List[str]						=   1
      with open(lowerCAmelCase_       ,     "w"       ,     encoding="utf-8"		) as vocab_handle:
        json.dump(lowerCAmelCase_       ,     lowerCAmelCase_		)
      _snake_case     :						str						=   WavaVecaCTCTokenizer(
          lowerCAmelCase_       ,     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=lowerCAmelCase_       ,     )
      _snake_case     :						Any						=   True if config.feat_extract_norm == '''layer''' else False
      _snake_case     :						Any						=   WavaVecaFeatureExtractor(
          feature_size=1       ,     sampling_rate=16_000       ,     padding_value=0       ,     do_normalize=lowerCAmelCase_       ,     return_attention_mask=lowerCAmelCase_       ,     )
      _snake_case     :						Any						=   WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,     tokenizer=lowerCAmelCase_		)
      processor.save_pretrained(lowerCAmelCase_		)

    _snake_case     :						List[Any]						=   WavaVecaForCTC(lowerCAmelCase_		)
  else:
    _snake_case     :						str						=   WavaVecaForPreTraining(lowerCAmelCase_		)

  if is_finetuned or is_seq_class:
    _snake_case     :						Tuple						=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]       ,     arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
  else:
    _snake_case     :						Tuple						=   argparse.Namespace(task="audio_pretraining"		)
    _snake_case     :						str						=   fairseq.tasks.setup_task(lowerCAmelCase_		)

    _snake_case     :						List[Any]						=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,     task=lowerCAmelCase_		)

  _snake_case     :						Any						=   model[0].eval()

  recursively_load_weights(lowerCAmelCase_       ,     lowerCAmelCase_       ,     not is_finetuned		)

  hf_wavavec.save_pretrained(lowerCAmelCase_		)


if __name__ == "__main__":
    _a      :			str					=							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"""
    )
    parser.add_argument(
        """--is_seq_class""",
        action="""store_true""",
        help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
    )
    _a      :			Optional[int]					=							parser.parse_args()

    _a      :			Optional[Any]					=							not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )





"""simple docstring"""
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: int  =					"""EncodecFeatureExtractor"""
    __lowercase							: str  =					("""T5Tokenizer""", """T5TokenizerFast""")
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      super().__init__(snake_case_						,    snake_case_     )
      _snake_case     :						Dict						=   self.feature_extractor
      _snake_case     :						Any						=   False
    def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=True     ):
      return self.tokenizer.get_decoder_prompt_ids(task=snake_case_						,    language=snake_case_						,    no_timestamps=snake_case_     )
    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*snake_case_						,    **snake_case_     )

      _snake_case     :						str						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						Optional[int]						=   kwargs.pop("sampling_rate"						,    snake_case_     )
      _snake_case     :						Optional[Any]						=   kwargs.pop("text"						,    snake_case_     )
      if len(snake_case_     ) > 0:
        _snake_case     :						Any						=   args[0]
        _snake_case     :						Union[str, Any]						=   args[1:]

      if audio is None and text is None:
        raise ValueError("You need to specify either an `audio` or `text` input to process."     )

      if text is not None:
        _snake_case     :						Any						=   self.tokenizer(snake_case_						,    **snake_case_     )

      if audio is not None:
        _snake_case     :						Any						=   self.feature_extractor(snake_case_						,    *snake_case_						,    sampling_rate=snake_case_						,    **snake_case_     )

      if audio is None:
        return inputs

      elif text is None:
        return audio_inputs

      else:
        _snake_case     :						str						=   audio_inputs["input_values"]
        if "padding_mask" in audio_inputs:
          _snake_case     :						List[str]						=   audio_inputs["padding_mask"]
        return inputs
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      _snake_case     :						Tuple						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						List[str]						=   kwargs.pop("padding_mask"						,    snake_case_     )

      if len(snake_case_     ) > 0:
        _snake_case     :						Tuple						=   args[0]
        _snake_case     :						Dict						=   args[1:]

      if audio_values is not None:
        return self._decode_audio(snake_case_						,    padding_mask=snake_case_     )
      else:
        return self.tokenizer.batch_decode(*snake_case_						,    **snake_case_     )
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      return self.tokenizer.decode(*snake_case_						,    **snake_case_     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )
      _snake_case			,					_snake_case			,					_snake_case     :						Tuple						=   audio_values.shape

      if padding_mask is None:
        return list(snake_case_     )

      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _snake_case     :						Any						=   seq_len - padding_mask.shape[-1]
      _snake_case     :						Optional[Any]						=   1 - self.feature_extractor.padding_value
      _snake_case     :						Optional[int]						=   np.pad(snake_case_						,    ((0, 0), (0, difference))						,    "constant"						,    constant_values=snake_case_     )

      _snake_case     :						Any						=   audio_values.tolist()
      for i in range(snake_case_     ):
        _snake_case     :						Tuple						=   np.asarray(audio_values[i]     )[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        _snake_case     :						Tuple						=   sliced_audio.reshape(snake_case_						,    -1     )

      return audio_values

