




'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase_ 		=      {
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]				,					a_	:							List[str]				,					a_	:							List[str]				,					a_	:							Optional[Any]				,					a_	:							Any			):
  for attribute in key.split('.'			):
    __a			=					getattr(a_				,					a_			)

  if weight_type is not None:
    __a			=					getattr(a_				,					a_			).shape
  else:
    __a			=					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":
    __a			=					value
  elif weight_type == "weight_g":
    __a			=					value
  elif weight_type == "weight_v":
    __a			=					value
  elif weight_type == "bias":
    __a			=					value
  else:
    __a			=					value

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]				,					a_	:							Optional[Any]			):
  __a			=					[]
  __a			=					fairseq_model.state_dict()

  __a			=					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __a			=					False
    if "conv_layers" in name:
      load_conv_layer(
          a_				,					a_				,					a_				,					a_				,					hf_model.config.feat_extract_norm == 'group'				,					)
      __a			=					True
    else:
      for key, mapped_key in MAPPING.items():
        __a			=					'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

        if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
          __a			=					True
          if "*" in mapped_key:
            __a			=					name.split(a_			)[0].split('.'			)[-2]
            __a			=					mapped_key.replace('*'				,					a_			)
          if "weight_g" in name:
            __a			=					'weight_g'
          elif "weight_v" in name:
            __a			=					'weight_v'
          elif "weight" in name:
            __a			=					'weight'
          elif "bias" in name:
            __a			=					'bias'
          else:
            __a			=					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 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							str				,					a_	:							Tuple				,					a_	:							Union[str, Any]				,					a_	:							Optional[int]			):
  __a			=					full_name.split('conv_layers.'			)[-1]
  __a			=					name.split('.'			)
  __a			=					int(items[0]			)
  __a			=					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."
      )
      __a			=					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."
      )
      __a			=					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."
      )
      __a			=					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."
      )
      __a			=					value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
  else:
    unused_weights.append(a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							List[str]			):
  __a			=					SEWConfig()
  if is_finetuned:
    __a			=					model.wav_encoder.wav_model.cfg
  else:
    __a			=					model.cfg

  __a			=					fs_config.conv_bias
  __a			=					eval(fs_config.conv_feature_layers			)
  __a			=					[x[0] for x in conv_layers]
  __a			=					[x[1] for x in conv_layers]
  __a			=					[x[2] for x in conv_layers]
  __a			=					'gelu'
  __a			=					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  __a			=					0.0
  __a			=					fs_config.activation_fn.name
  __a			=					fs_config.encoder_embed_dim
  __a			=					0.02
  __a			=					fs_config.encoder_ffn_embed_dim
  __a			=					1e-5
  __a			=					fs_config.encoder_layerdrop
  __a			=					fs_config.encoder_attention_heads
  __a			=					fs_config.conv_pos_groups
  __a			=					fs_config.conv_pos
  __a			=					len(a_			)
  __a			=					fs_config.encoder_layers
  __a			=					fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    __a			=					model.cfg
    __a			=					fs_config.final_dropout
    __a			=					fs_config.layerdrop
  __a			=					fs_config.activation_dropout
  __a			=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  __a			=					fs_config.attention_dropout
  __a			=					fs_config.dropout_input
  __a			=					fs_config.dropout
  __a			=					fs_config.mask_channel_length
  __a			=					fs_config.mask_channel_prob
  __a			=					fs_config.mask_length
  __a			=					fs_config.mask_prob

  __a			=					'Wav2Vec2FeatureExtractor'
  __a			=					'Wav2Vec2CTCTokenizer'

  return config


@torch.no_grad()
def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							Any				,					a_	:							List[Any]=None				,					a_	:							Tuple=None				,					a_	:							Dict=True			):

  if is_finetuned:
    __a	,       __a	,       __a			=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]				,					arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
  else:
    __a	,       __a	,       __a			=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

  if config_path is not None:
    __a			=					SEWConfig.from_pretrained(a_			)
  else:
    __a			=					convert_config(model[0]				,					a_			)
  __a			=					model[0].eval()

  __a			=					True if config.feat_extract_norm == 'layer' else False
  __a			=					WavaVecaFeatureExtractor(
      feature_size=1				,					sampling_rate=1_6000				,					padding_value=0				,					do_normalize=a_				,					return_attention_mask=a_				,					)

  if is_finetuned:
    if dict_path:
      __a			=					Dictionary.load(a_			)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      __a			=					target_dict.pad_index
      __a			=					target_dict.bos_index
      __a			=					target_dict.pad_index
      __a			=					target_dict.bos_index
      __a			=					target_dict.eos_index
      __a			=					len(target_dict.symbols			)
      __a			=					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_			)
      with open(a_				,					'w'				,					encoding='utf-8'			) as vocab_handle:
        json.dump(target_dict.indices				,					a_			)
      __a			=					WavaVecaCTCTokenizer(
          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_				,					)
      __a			=					WavaVecaProcessor(feature_extractor=a_				,					tokenizer=a_			)
      processor.save_pretrained(a_			)

    __a			=					SEWForCTC(a_			)
  else:
    __a			=					SEWModel(a_			)
    feature_extractor.save_pretrained(a_			)

  recursively_load_weights(a_				,					a_				,					a_			)

  hf_model.save_pretrained(a_			)


if __name__ == "__main__":
  UpperCAmelCase_ 		=      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(
      "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  UpperCAmelCase_ 		=      parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )









'''simple docstring'''


from ..utils import DummyObject, requires_backends





class    __lowercase						(  metaclass=__magic_name__ ):
   _a							=      ["""onnx"""]







   def __init__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     requires_backends(self      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )



