
'''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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a_     =  {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "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 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    for attribute in key.split("."    ):
        snake_case_	:      int            =  getattr(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    )

    if weight_type is not None:
        snake_case_	:      Dict            =  getattr(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ).shape
    else:
        snake_case_	:      str            =  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":
        snake_case_	:      Union[str, Any]            =  value
    elif weight_type == "weight_g":
        snake_case_	:      Optional[int]            =  value
    elif weight_type == "weight_v":
        snake_case_	:      Union[str, Any]            =  value
    elif weight_type == "bias":
        snake_case_	:      List[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 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Union[str, Any]            =  []
    snake_case_	:      Dict            =  fairseq_model.state_dict()

    snake_case_	:      Union[str, Any]            =  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_	:      Tuple            =  False
        if "conv_layers" in name:
            load_conv_layer(
                __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						hf_model.config.feat_extract_norm == "group",						)
            snake_case_	:      Dict            =  True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_	:      Tuple            =  "sew_d." + 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]:
                    snake_case_	:      Any            =  True
                    if "*" in mapped_key:
                        snake_case_	:      Any            =  name.split(__SCREAMING_SNAKE_CASE    )[0].split("."    )[-2]
                        if not layer_index.isnumeric():
                            continue
                        snake_case_	:      List[Any]            =  mapped_key.replace("*",						__SCREAMING_SNAKE_CASE    )
                    if "weight_g" in name:
                        snake_case_	:      Optional[Any]            =  "weight_g"
                    elif "weight_v" in name:
                        snake_case_	:      Optional[int]            =  "weight_v"
                    elif "weight" in name:
                        snake_case_	:      Optional[Any]            =  "weight"
                    elif "bias" in name:
                        snake_case_	:      int            =  "bias"
                    else:
                        snake_case_	:      List[Any]            =  None
                    set_recursively(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    )
                continue
        if not is_used:
            unused_weights.append(__SCREAMING_SNAKE_CASE    )

    logger.warning(f'Unused weights: {unused_weights}'    )

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Optional[int]            =  full_name.split("conv_layers."    )[-1]
    snake_case_	:      Union[str, Any]            =  name.split("."    )
    snake_case_	:      int            =  int(items[0]    )
    snake_case_	:      int            =  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.'
            )
            snake_case_	:      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.'
            )
            snake_case_	:      List[str]            =  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."
            )
            snake_case_	:      List[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.'
            )
            snake_case_	:      Optional[int]            =  value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
    else:
        unused_weights.append(__SCREAMING_SNAKE_CASE    )

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Dict            =  SEWDConfig()
    if is_finetuned:
        snake_case_	:      Optional[Any]            =  model.wav_encoder.wav_model.cfg
    else:
        snake_case_	:      int            =  model.cfg

    snake_case_	:      Tuple            =  fs_config.conv_bias
    snake_case_	:      Tuple            =  eval(fs_config.conv_feature_layers    )
    snake_case_	:      Any            =  [x[0] for x in conv_layers]
    snake_case_	:      Optional[int]            =  [x[1] for x in conv_layers]
    snake_case_	:      List[Any]            =  [x[2] for x in conv_layers]
    snake_case_	:      List[Any]            =  "gelu"
    snake_case_	:      Dict            =  "layer" if fs_config.extractor_mode == "layer_norm" else "group"
    snake_case_	:      Tuple            =  0.0
    snake_case_	:      List[Any]            =  fs_config.activation_fn.name
    snake_case_	:      List[Any]            =  fs_config.encoder_embed_dim
    snake_case_	:      List[Any]            =  0.02
    snake_case_	:      Any            =  fs_config.encoder_ffn_embed_dim
    snake_case_	:      Optional[int]            =  1E-5
    snake_case_	:      Dict            =  fs_config.encoder_layerdrop
    snake_case_	:      List[Any]            =  fs_config.encoder_attention_heads
    snake_case_	:      List[str]            =  fs_config.conv_pos_groups
    snake_case_	:      Union[str, Any]            =  fs_config.conv_pos
    snake_case_	:      Tuple            =  len(__SCREAMING_SNAKE_CASE    )
    snake_case_	:      Optional[Any]            =  fs_config.encoder_layers
    snake_case_	:      Optional[int]            =  fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    snake_case_	:      Dict            =  fs_config.max_position_embeddings
    snake_case_	:      Optional[Any]            =  fs_config.position_buckets
    snake_case_	:      Dict            =  fs_config.share_att_key
    snake_case_	:      int            =  fs_config.relative_attention
    snake_case_	:      str            =  fs_config.position_biased_input
    snake_case_	:      int            =  tuple(fs_config.pos_att_type.split("|"    )    )
    snake_case_	:      int            =  fs_config.norm_rel_ebd

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        snake_case_	:      Optional[Any]            =  model.cfg
        snake_case_	:      Union[str, Any]            =  fs_config.final_dropout
        snake_case_	:      str            =  fs_config.layerdrop
    snake_case_	:      int            =  fs_config.activation_dropout
    snake_case_	:      Dict            =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    snake_case_	:      Dict            =  fs_config.attention_dropout
    snake_case_	:      Dict            =  fs_config.dropout_input
    snake_case_	:      Dict            =  fs_config.dropout
    snake_case_	:      Optional[Any]            =  fs_config.mask_channel_length
    snake_case_	:      Tuple            =  fs_config.mask_channel_prob
    snake_case_	:      List[str]            =  fs_config.mask_length
    snake_case_	:      str            =  fs_config.mask_prob

    snake_case_	:      int            =  "Wav2Vec2FeatureExtractor"
    snake_case_	:      Any            =  "Wav2Vec2CTCTokenizer"

    return config

@torch.no_grad()
def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=True    ):
    """simple docstring"""






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

    if config_path is not None:
        snake_case_	:      Any            =  SEWDConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
    else:
        snake_case_	:      Optional[Any]            =  convert_config(model[0],						__SCREAMING_SNAKE_CASE    )
    snake_case_	:      Optional[int]            =  model[0].eval()

    snake_case_	:      str            =  True if config.feat_extract_norm == "layer" else False
    snake_case_	:      int            =  WavaVecaFeatureExtractor(
        feature_size=1,						sampling_rate=1_6_0_0_0,						padding_value=0,						do_normalize=__SCREAMING_SNAKE_CASE,						return_attention_mask=__SCREAMING_SNAKE_CASE,						)

    if is_finetuned:
        if dict_path:
            snake_case_	:      str            =  Dictionary.load(__SCREAMING_SNAKE_CASE    )

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

        snake_case_	:      Optional[Any]            =  SEWDForCTC(__SCREAMING_SNAKE_CASE    )
    else:
        snake_case_	:      Any            =  SEWDModel(__SCREAMING_SNAKE_CASE    )
        feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE    )

    recursively_load_weights(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    )

    hf_model.save_pretrained(__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  a_     =  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"
  )
  a_     =  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 __future__ import annotations

from typing import Any






class 				UpperCAmelCase_			:





 def __init__(      self	,    lowercase_	,    lowercase_):  # noqa: A002
     snake_case_	:      List[str]            =  item
     snake_case_	:      Dict            =  next






class 				UpperCAmelCase_			:





 def __init__(      self):
     snake_case_	:      Node | None            =  None
     snake_case_	:      List[str]            =  0





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      List[Any]            =  Node(lowercase_	,    self.head)
     self.size += 1





 def     snake_case__						(      self):
     # Switched 'self.is_empty()' to 'self.head is None'
     # because mypy was considering the possibility that 'self.head'
     # can be None in below else part and giving error
     if self.head is None:
         return None
     else:
         snake_case_	:      List[Any]            =  self.head.item
         snake_case_	:      Any            =  self.head.next
         self.size -= 1
         return item





 def     snake_case__						(      self):
     return self.head is None





 def __str__(      self):
     if self.is_empty():
         return ""
     else:
         snake_case_	:      Dict            =  self.head
         snake_case_	:      Optional[Any]            =  ""
         snake_case_	:      list[str]            =  []
         while iterate:
             item_list.append(str(iterate.item))
             snake_case_	:      List[str]            =  iterate.next

         snake_case_	:      Optional[Any]            =  " --> ".join(lowercase_)

         return item_str





 def __len__(      self):
     return self.size




