'''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()
lowercase__  :      Tuple        =					logging.get_logger(__name__)

lowercase__  :      int        =					{
    "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  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      for attribute in key.split('''.'''					):
            UpperCAmelCase_            =  getattr(_UpperCamelCase							,    _UpperCamelCase					)

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

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



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Dict					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  fairseq_model.state_dict()

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

      for name, value in fairseq_dict.items():
            UpperCAmelCase_            =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    hf_model.config.feat_extract_norm == '''group'''							,    )
                  UpperCAmelCase_            =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase_            =  '''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]:
                              UpperCAmelCase_            =  True
                              if "*" in mapped_key:
                                    UpperCAmelCase_            =  name.split(_UpperCamelCase					)[0].split('''.'''					)[-2]
                                    UpperCAmelCase_            =  mapped_key.replace('''*'''							,    _UpperCamelCase					)
                              if "weight_g" in name:
                                    UpperCAmelCase_            =  '''weight_g'''
                              elif "weight_v" in name:
                                    UpperCAmelCase_            =  '''weight_v'''
                              elif "weight" in name:
                                    UpperCAmelCase_            =  '''weight'''
                              elif "bias" in name:
                                    UpperCAmelCase_            =  '''bias'''
                              else:
                                    UpperCAmelCase_            =  None
                              set_recursively(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
                        continue
            if not is_used:
                  unused_weights.append(_UpperCamelCase					)

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



def  __lowerCamelCase       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Tuple					):


      '''simple docstring'''


      UpperCAmelCase_            =  full_name.split('''conv_layers.'''					)[-1]
      UpperCAmelCase_            =  name.split('''.'''					)
      UpperCAmelCase_            =  int(items[0]					)
      UpperCAmelCase_            =  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."""
                  )
                  UpperCAmelCase_            =  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."""
                  )
                  UpperCAmelCase_            =  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."
                  )
                  UpperCAmelCase_            =  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."""
                  )
                  UpperCAmelCase_            =  value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
      else:
            unused_weights.append(_UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  List[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  SEWConfig()
      if is_finetuned:
            UpperCAmelCase_            =  model.wav_encoder.wav_model.cfg
      else:
            UpperCAmelCase_            =  model.cfg

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

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

      UpperCAmelCase_            =  '''Wav2Vec2FeatureExtractor'''
      UpperCAmelCase_            =  '''Wav2Vec2CTCTokenizer'''

      return config



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  List[Any]=None							,    _UpperCamelCase  :  Any=None							,    _UpperCamelCase  :  str=True					):


      '''simple docstring'''


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

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

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

      if is_finetuned:
            if dict_path:
                  UpperCAmelCase_            =  Dictionary.load(_UpperCamelCase					)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCAmelCase_            =  target_dict.pad_index
                  UpperCAmelCase_            =  target_dict.bos_index
                  UpperCAmelCase_            =  target_dict.pad_index
                  UpperCAmelCase_            =  target_dict.bos_index
                  UpperCAmelCase_            =  target_dict.eos_index
                  UpperCAmelCase_            =  len(target_dict.symbols					)
                  UpperCAmelCase_            =  os.path.join(_UpperCamelCase							,    '''vocab.json'''					)
                  if not os.path.isdir(_UpperCamelCase					):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCamelCase					)					)
                        return
                  os.makedirs(_UpperCamelCase							,    exist_ok=_UpperCamelCase					)
                  with open(_UpperCamelCase							,    '''w'''							,    encoding='''utf-8'''					) as vocab_handle:
                        json.dump(target_dict.indices							,    _UpperCamelCase					)
                  UpperCAmelCase_            =  WavaVecaCTCTokenizer(
                      _UpperCamelCase							,    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=_UpperCamelCase							,    )
                  UpperCAmelCase_            =  WavaVecaProcessor(feature_extractor=_UpperCamelCase							,    tokenizer=_UpperCamelCase					)
                  processor.save_pretrained(_UpperCamelCase					)

            UpperCAmelCase_            =  SEWForCTC(_UpperCamelCase					)
      else:
            UpperCAmelCase_            =  SEWModel(_UpperCamelCase					)
            feature_extractor.save_pretrained(_UpperCamelCase					)

      recursively_load_weights(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)

      hf_model.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
      lowercase__  :      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(
          "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
      )
      lowercase__  :      Dict        =					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 logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowercase__  :      Dict        =					logging.get_logger(__name__)

lowercase__  :      List[Any]        =					"T5Config"







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''mt5'''
  lowerCAmelCase__										=	MTaConfig







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''mt5'''
  lowerCAmelCase__										=	MTaConfig







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''mt5'''
  lowerCAmelCase__										=	MTaConfig





