

"""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()
_a							=							logging.get_logger(__name__)

_a							=							{
    '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   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Optional[int])     ->						str:
   '''simple docstring'''


   for attribute in key.split("."):
      __lowercase								=						getattr(lowerCamelCase_,      lowerCamelCase_)

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

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




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						[]
   __lowercase								=						fairseq_model.state_dict()

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

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

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




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Tuple)     ->						List[Any]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	List[Any])     ->						Optional[int]:
   '''simple docstring'''


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

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

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

   __lowercase								=						"""Wav2Vec2FeatureExtractor"""
   __lowercase								=						"""Wav2Vec2CTCTokenizer"""

   return config




@torch.no_grad()
def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	List[Any]=None,      UpperCamelCase_					:	Dict=None,      UpperCamelCase_					:	List[str]=True)     ->						Union[str, Any]:
   '''simple docstring'''


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

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

   __lowercase								=						True if config.feat_extract_norm == """layer""" else False
   __lowercase								=						WavaVecaFeatureExtractor(
       feature_size=1,      sampling_rate=16000,      padding_value=0,      do_normalize=lowerCamelCase_,      return_attention_mask=lowerCamelCase_,      )

   if is_finetuned:
      if dict_path:
         __lowercase								=						Dictionary.load(lowerCamelCase_)

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

      __lowercase								=						SEWForCTC(lowerCamelCase_)
   else:
      __lowercase								=						SEWModel(lowerCamelCase_)
      feature_extractor.save_pretrained(lowerCamelCase_)

   recursively_load_weights(lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_)

   hf_model.save_pretrained(lowerCamelCase_)


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"""





_a							=							[
    'DownloadConfig',
    'DownloadManager',
    'DownloadMode',
    'StreamingDownloadManager',
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager




