

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

__UpperCAmelCase	=			{
    '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 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 for attribute in key.split(""".""" ):
  UpperCAmelCase__   :					str   				=   getattr(__UpperCamelCase							,      __UpperCamelCase )

 if weight_type is not None:
  UpperCAmelCase__   :					str   				=   getattr(__UpperCamelCase							,      __UpperCamelCase ).shape
 else:
  UpperCAmelCase__   :					Dict   				=   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__   :					int   				=   value
 elif weight_type == "weight_g":
  UpperCAmelCase__   :					Union[str, Any]   				=   value
 elif weight_type == "weight_v":
  UpperCAmelCase__   :					Any   				=   value
 elif weight_type == "bias":
  UpperCAmelCase__   :					str   				=   value
 else:
  UpperCAmelCase__   :					Optional[Any]   				=   value

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					int   				=   []
 UpperCAmelCase__   :					Union[str, Any]   				=   fairseq_model.state_dict()

 UpperCAmelCase__   :					Optional[int]   				=   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase__   :					str   				=   False
  if "conv_layers" in name:
   load_conv_layer(
       __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      hf_model.config.feat_extract_norm == """group"""							,      )
   UpperCAmelCase__   :					List[str]   				=   True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCAmelCase__   :					Optional[Any]   				=   """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]:
     UpperCAmelCase__   :					Tuple   				=   True
     if "*" in mapped_key:
      UpperCAmelCase__   :					List[Any]   				=   name.split(__UpperCamelCase )[0].split(""".""" )[-2]
      if not layer_index.isnumeric():
       continue
      UpperCAmelCase__   :					Any   				=   mapped_key.replace("""*"""							,      __UpperCamelCase )
     if "weight_g" in name:
      UpperCAmelCase__   :					Union[str, Any]   				=   """weight_g"""
     elif "weight_v" in name:
      UpperCAmelCase__   :					Tuple   				=   """weight_v"""
     elif "weight" in name:
      UpperCAmelCase__   :					str   				=   """weight"""
     elif "bias" in name:
      UpperCAmelCase__   :					List[Any]   				=   """bias"""
     else:
      UpperCAmelCase__   :					Tuple   				=   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							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					int   				=   full_name.split("""conv_layers.""" )[-1]
 UpperCAmelCase__   :					str   				=   name.split(""".""" )
 UpperCAmelCase__   :					int   				=   int(items[0] )
 UpperCAmelCase__   :					List[Any]   				=   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__   :					List[str]   				=   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__   :					Union[str, Any]   				=   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__   :					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."
   )
   UpperCAmelCase__   :					Any   				=   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							,      __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Tuple   				=   SEWDConfig()
 if is_finetuned:
  UpperCAmelCase__   :					str   				=   model.wav_encoder.wav_model.cfg
 else:
  UpperCAmelCase__   :					List[str]   				=   model.cfg

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

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

 UpperCAmelCase__   :					Tuple   				=   """Wav2Vec2FeatureExtractor"""
 UpperCAmelCase__   :					Dict   				=   """Wav2Vec2CTCTokenizer"""

 return config
@torch.no_grad()
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase=None							,      __UpperCamelCase=None							,      __UpperCamelCase=True ):
 '''simple docstring'''





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

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

 UpperCAmelCase__   :					Union[str, Any]   				=   True if config.feat_extract_norm == """layer""" else False
 UpperCAmelCase__   :					List[Any]   				=   WavaVecaFeatureExtractor(
     feature_size=1							,      sampling_rate=16000							,      padding_value=0							,      do_normalize=__UpperCamelCase							,      return_attention_mask=__UpperCamelCase							,      )

 if is_finetuned:
  if dict_path:
   UpperCAmelCase__   :					Tuple   				=   Dictionary.load(__UpperCamelCase )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   UpperCAmelCase__   :					Any   				=   target_dict.pad_index
   UpperCAmelCase__   :					Optional[Any]   				=   target_dict.bos_index
   UpperCAmelCase__   :					Dict   				=   target_dict.pad_index
   UpperCAmelCase__   :					Optional[int]   				=   target_dict.bos_index
   UpperCAmelCase__   :					int   				=   target_dict.eos_index
   UpperCAmelCase__   :					Union[str, Any]   				=   len(target_dict.symbols )
   UpperCAmelCase__   :					List[Any]   				=   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__   :					List[str]   				=   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__   :					List[str]   				=   WavaVecaProcessor(feature_extractor=__UpperCamelCase							,      tokenizer=__UpperCamelCase )
   processor.save_pretrained(__UpperCamelCase )

  UpperCAmelCase__   :					Union[str, Any]   				=   SEWDForCTC(__UpperCamelCase )
 else:
  UpperCAmelCase__   :					str   				=   SEWDModel(__UpperCamelCase )
  feature_extractor.save_pretrained(__UpperCamelCase )

 recursively_load_weights(__UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase )

 hf_model.save_pretrained(__UpperCamelCase )


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"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	=			logging.get_logger(__name__)

__UpperCAmelCase	=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

__UpperCAmelCase	=			{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
  snake_case_			    =    DebertaTokenizer

  def __init__(					self						:     Optional[Any]   ,A						:     Any=None   ,A						:     Any=None   ,A						:     Dict=None   ,A						:     Optional[int]="replace"   ,A						:     Optional[int]="[CLS]"   ,A						:     str="[SEP]"   ,A						:     Optional[int]="[SEP]"   ,A						:     List[Any]="[CLS]"   ,A						:     Dict="[UNK]"   ,A						:     List[str]="[PAD]"   ,A						:     Optional[Any]="[MASK]"   ,A						:     str=False   ,**A						:     Dict   ,):






   '''simple docstring'''
   super().__init__(
       A   ,A   ,tokenizer_file=A   ,errors=A   ,bos_token=A   ,eos_token=A   ,unk_token=A   ,sep_token=A   ,cls_token=A   ,pad_token=A   ,mask_token=A   ,add_prefix_space=A   ,**A   ,)
   UpperCAmelCase__   :					Tuple   				=   kwargs.pop("""add_bos_token"""   ,A      )

   UpperCAmelCase__   :					Tuple   				=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
   if pre_tok_state.get("""add_prefix_space"""   ,A      ) != add_prefix_space:
    UpperCAmelCase__   :					List[Any]   				=   getattr(A   ,pre_tok_state.pop("""type"""      )      )
    UpperCAmelCase__   :					Any   				=   add_prefix_space
    UpperCAmelCase__   :					List[Any]   				=   pre_tok_class(**A      )

   UpperCAmelCase__   :					str   				=   add_prefix_space

  @property
  def 				__lowercase    (					self						:     str      ):






   '''simple docstring'''
   if self._mask_token is None:
    if self.verbose:
     logger.error("""Using mask_token, but it is not set yet."""      )
    return None
   return str(self._mask_token      )

  @mask_token.setter
  def 				__lowercase    (					self						:     Dict   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   AddedToken(A   ,lstrip=A   ,rstrip=A      ) if isinstance(A   ,A      ) else value
   UpperCAmelCase__   :					List[str]   				=   value

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__   :					Dict   				=   [self.cls_token_id]
   UpperCAmelCase__   :					str   				=   [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep

  def 				__lowercase    (					self						:     Tuple   ,A						:     List[int]   ,A						:     Optional[List[int]] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   [self.sep_token_id]
   UpperCAmelCase__   :					str   				=   [self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep      ) * [0]
   return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

  def 				__lowercase    (					self						:     int   ,*A						:     Tuple   ,**A						:     Optional[int]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   kwargs.get("""is_split_into_words"""   ,A      )
   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     str   ,*A						:     List[str]   ,**A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   kwargs.get("""is_split_into_words"""   ,A      )

   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*A   ,**A      )

  def 				__lowercase    (					self						:     Tuple   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   self._tokenizer.model.save(A   ,name=A      )
   return tuple(A      )




  def 				__lowercase    (					self						:     List[str]   ,A						:     "Conversation"      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   []
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(A   ,add_special_tokens=A      ) + [self.eos_token_id]      )

   if len(A      ) > self.model_max_length:
    UpperCAmelCase__   :					Optional[int]   				=   input_ids[-self.model_max_length :]
   return input_ids





