


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)

@dataclass
class 						A__      :







    """simple docstring"""

    __magic_name__    						=	42
    __magic_name__    						=	42
    __magic_name__    						=	None
    __magic_name__    						=	None
    def 	a_    ( self					):
         return json.dumps(dataclasses.asdict(self					)    ,      indent=2					) + "\n"






@dataclass(frozen=snake_case__			)
class 						A__      :







    """simple docstring"""

    __magic_name__    						=	42
    __magic_name__    						=	None
    __magic_name__    						=	None
    __magic_name__    						=	None
    def 	a_    ( self					):
         return json.dumps(dataclasses.asdict(self					)					) + "\n"






class 						A__      :







    """simple docstring"""

    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         if len(self.get_labels()					) > 1:
              snake_case					=       self.get_labels()[int(example.label					)]
         return example




    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None					):
         with open(__snake_case    ,      '''r'''    ,      encoding='''utf-8-sig'''					) as f:
              return list(csv.reader(__snake_case    ,      delimiter='''\t'''    ,      quotechar=__snake_case					)					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case="classification"    ,      __snake_case=False					):
         snake_case					=       [] if labels is None else labels
         snake_case					=       [] if examples is None else examples
         snake_case					=       mode
         snake_case					=       verbose
    def __len__( self					):
         return len(self.examples					)
    def __getitem__( self    ,      __snake_case					):
         if isinstance(__snake_case    ,      __snake_case					):
              return SingleSentenceClassificationProcessor(labels=self.labels    ,      examples=self.examples[idx]					)
         return self.examples[idx]
    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      **__snake_case					):
         snake_case					=       cls(**__snake_case					)
         processor.add_examples_from_csv(
             __snake_case    ,      split_name=__snake_case    ,      column_label=__snake_case    ,      column_text=__snake_case    ,      column_id=__snake_case    ,      skip_first_row=__snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case    ,      )
         return processor
    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None    ,      **__snake_case					):
         snake_case					=       cls(**__snake_case					)
         processor.add_examples(__snake_case    ,      labels=__snake_case					)
         return processor
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False    ,      __snake_case=False    ,      ):
         snake_case					=       self._read_tsv(__snake_case					)
         if skip_first_row:
              snake_case					=       lines[1:]
         snake_case					=       []
         snake_case					=       []
         snake_case					=       []
         for i, line in enumerate(__snake_case					):
              texts.append(line[column_text]					)
              labels.append(line[column_label]					)
              if column_id is not None:
                   ids.append(line[column_id]					)
              else:
                   snake_case					=       F'''{split_name}-{i}''' if split_name else str(__snake_case					)
                   ids.append(__snake_case					)

         return self.add_examples(
             __snake_case    ,      __snake_case    ,      __snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case					)
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False					):
         if labels is not None and len(__snake_case					) != len(__snake_case					):
              raise ValueError(
                  F'''Text and labels have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
         if ids is not None and len(__snake_case					) != len(__snake_case					):
              raise ValueError(F'''Text and ids have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
         if ids is None:
              snake_case					=       [None] * len(__snake_case					)
         if labels is None:
              snake_case					=       [None] * len(__snake_case					)
         snake_case					=       []
         snake_case					=       set()
         for text_or_text_and_label, label, guid in zip(__snake_case    ,      __snake_case    ,      __snake_case					):
              if isinstance(__snake_case    ,      (tuple, list)					) and label is None:
                   snake_case				,						snake_case					=       text_or_text_and_label
              else:
                   snake_case					=       text_or_text_and_label
              added_labels.add(__snake_case					)
              examples.append(InputExample(guid=__snake_case    ,      text_a=__snake_case    ,      text_b=__snake_case    ,      label=__snake_case					)					)

         # Update examples
         if overwrite_examples:
              snake_case					=       examples
         else:
              self.examples.extend(__snake_case					)

         # Update labels
         if overwrite_labels:
              snake_case					=       list(__snake_case					)
         else:
              snake_case					=       list(set(self.labels					).union(__snake_case					)					)

         return self.examples




    def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=0    ,      __snake_case=True    ,      __snake_case=None    ,      ):
         if max_length is None:
              snake_case					=       tokenizer.max_len

         snake_case					=       {label: i for i, label in enumerate(self.labels					)}

         snake_case					=       []
         for ex_index, example in enumerate(self.examples					):
              if ex_index % 1_0_0_0_0 == 0:
                   logger.info(F'''Tokenizing example {ex_index}'''					)

              snake_case					=       tokenizer.encode(
                  example.text_a    ,      add_special_tokens=__snake_case    ,      max_length=min(__snake_case    ,      tokenizer.max_len					)    ,      )
              all_input_ids.append(__snake_case					)

         snake_case					=       max(len(__snake_case					) for input_ids in all_input_ids					)

         snake_case					=       []
         for ex_index, (input_ids, example) in enumerate(zip(__snake_case    ,      self.examples					)					):
              if ex_index % 1_0_0_0_0 == 0:
                   logger.info(F'''Writing example {ex_index}/{len(self.examples					)}'''					)
              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              snake_case					=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)

              # Zero-pad up to the sequence length.
              snake_case					=       batch_length - len(__snake_case					)
              if pad_on_left:
                   snake_case					=       ([pad_token] * padding_length) + input_ids
                   snake_case					=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
              else:
                   snake_case					=       input_ids + ([pad_token] * padding_length)
                   snake_case					=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

              if len(__snake_case					) != batch_length:
                   raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
              if len(__snake_case					) != batch_length:
                   raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)

              if self.mode == "classification":
                   snake_case					=       label_map[example.label]
              elif self.mode == "regression":
                   snake_case					=       float(example.label					)
              else:
                   raise ValueError(self.mode					)

              if ex_index < 5 and self.verbose:
                   logger.info('''*** Example ***'''					)
                   logger.info(F'''guid: {example.guid}'''					)
                   logger.info(F'''input_ids: {" ".join([str(__snake_case					) for x in input_ids]					)}'''					)
                   logger.info(F'''attention_mask: {" ".join([str(__snake_case					) for x in attention_mask]					)}'''					)
                   logger.info(F'''label: {example.label} (id = {label})'''					)

              features.append(InputFeatures(input_ids=__snake_case    ,      attention_mask=__snake_case    ,      label=__snake_case					)					)

         if return_tensors is None:
              return features
         elif return_tensors == "tf":
              if not is_tf_available():
                   raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''					)
              import tensorflow as tf

              def gen():
                   for ex in features:
                        yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

              snake_case					=       tf.data.Dataset.from_generator(
                  __snake_case    ,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,      ({'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)}, tf.TensorShape([]					))    ,      )
              return dataset
         elif return_tensors == "pt":
              if not is_torch_available():
                   raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''					)
              import torch
              from torch.utils.data import TensorDataset

              snake_case					=       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long					)
              snake_case					=       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long					)
              if self.mode == "classification":
                   snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.long					)
              elif self.mode == "regression":
                   snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.float					)

              snake_case					=       TensorDataset(__snake_case    ,      __snake_case    ,      __snake_case					)
              return dataset
         else:
              raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)








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()
_SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :				Dict       =					{
    "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__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     for attribute in key.split('''.'''       ):
          snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       )

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

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



     """simple docstring"""






     snake_case					=       []
     snake_case					=       fairseq_model.state_dict()

     snake_case					=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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



     """simple docstring"""






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



     """simple docstring"""






     snake_case					=       SEWDConfig()
     if is_finetuned:
          snake_case					=       model.wav_encoder.wav_model.cfg
     else:
          snake_case					=       model.cfg

     snake_case					=       fs_config.conv_bias
     snake_case					=       eval(fs_config.conv_feature_layers       )
     snake_case					=       [x[0] for x in conv_layers]
     snake_case					=       [x[1] for x in conv_layers]
     snake_case					=       [x[2] for x in conv_layers]
     snake_case					=       '''gelu'''
     snake_case					=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
     snake_case					=       0.0
     snake_case					=       fs_config.activation_fn.name
     snake_case					=       fs_config.encoder_embed_dim
     snake_case					=       0.02
     snake_case					=       fs_config.encoder_ffn_embed_dim
     snake_case					=       1e-5
     snake_case					=       fs_config.encoder_layerdrop
     snake_case					=       fs_config.encoder_attention_heads
     snake_case					=       fs_config.conv_pos_groups
     snake_case					=       fs_config.conv_pos
     snake_case					=       len(UpperCamelCase_       )
     snake_case					=       fs_config.encoder_layers
     snake_case					=       fs_config.squeeze_factor
     # DeBERTa-specific parameters:
     snake_case					=       fs_config.max_position_embeddings
     snake_case					=       fs_config.position_buckets
     snake_case					=       fs_config.share_att_key
     snake_case					=       fs_config.relative_attention
     snake_case					=       fs_config.position_biased_input
     snake_case					=       tuple(fs_config.pos_att_type.split('''|'''       )       )
     snake_case					=       fs_config.norm_rel_ebd

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

     snake_case					=       '''Wav2Vec2FeatureExtractor'''
     snake_case					=       '''Wav2Vec2CTCTokenizer'''

     return config
@torch.no_grad()
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):



     """simple docstring"""






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

     if config_path is not None:
          snake_case					=       SEWDConfig.from_pretrained(UpperCamelCase_       )
     else:
          snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
     snake_case					=       model[0].eval()

     snake_case					=       True if config.feat_extract_norm == '''layer''' else False
     snake_case					=       WavaVecaFeatureExtractor(
         feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)

     if is_finetuned:
          if dict_path:
               snake_case					=       Dictionary.load(UpperCamelCase_       )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               snake_case					=       target_dict.pad_index
               snake_case					=       target_dict.bos_index
               snake_case					=       target_dict.pad_index
               snake_case					=       target_dict.bos_index
               snake_case					=       target_dict.eos_index
               snake_case					=       len(target_dict.symbols       )
               snake_case					=       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_       )
               snake_case					=       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_							,)
               snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
               processor.save_pretrained(UpperCamelCase_       )

          snake_case					=       SEWDForCTC(UpperCamelCase_       )
     else:
          snake_case					=       SEWDModel(UpperCamelCase_       )
          feature_extractor.save_pretrained(UpperCamelCase_       )

     recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )

     hf_model.save_pretrained(UpperCamelCase_       )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE    :				List[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"
 )
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )




