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

UpperCamelCase__					:						Tuple											=     {
    "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_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   for attribute in key.split("""."""  ):
      _SCREAMING_SNAKE_CASE          = getattr(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )

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

   logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = []
   _SCREAMING_SNAKE_CASE          = fairseq_model.state_dict()

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

   for name, value in fairseq_dict.items():
      _SCREAMING_SNAKE_CASE          = 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"""			,					)
         _SCREAMING_SNAKE_CASE          = True
      else:
         for key, mapped_key in MAPPING.items():
            _SCREAMING_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]:
               _SCREAMING_SNAKE_CASE          = True
               if "*" in mapped_key:
                  _SCREAMING_SNAKE_CASE          = name.split(SCREAMING_SNAKE_CASE_  )[0].split("""."""  )[-2]
                  if not layer_index.isnumeric():
                     continue
                  _SCREAMING_SNAKE_CASE          = mapped_key.replace("""*"""			,					SCREAMING_SNAKE_CASE_  )
               if "weight_g" in name:
                  _SCREAMING_SNAKE_CASE          = """weight_g"""
               elif "weight_v" in name:
                  _SCREAMING_SNAKE_CASE          = """weight_v"""
               elif "weight" in name:
                  _SCREAMING_SNAKE_CASE          = """weight"""
               elif "bias" in name:
                  _SCREAMING_SNAKE_CASE          = """bias"""
               else:
                  _SCREAMING_SNAKE_CASE          = 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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = full_name.split("""conv_layers."""  )[-1]
   _SCREAMING_SNAKE_CASE          = name.split("""."""  )
   _SCREAMING_SNAKE_CASE          = int(items[0]  )
   _SCREAMING_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."
         )
         _SCREAMING_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."
         )
         _SCREAMING_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."
         )
         _SCREAMING_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."
         )
         _SCREAMING_SNAKE_CASE          = 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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = SEWDConfig()
   if is_finetuned:
      _SCREAMING_SNAKE_CASE          = model.wav_encoder.wav_model.cfg
   else:
      _SCREAMING_SNAKE_CASE          = model.cfg

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

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

   _SCREAMING_SNAKE_CASE          = """Wav2Vec2FeatureExtractor"""
   _SCREAMING_SNAKE_CASE          = """Wav2Vec2CTCTokenizer"""

   return config
@torch.no_grad()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=None			,					SCREAMING_SNAKE_CASE_=None			,					SCREAMING_SNAKE_CASE_=True  )   ->   Optional[int]:




   """simple docstring"""

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

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

   _SCREAMING_SNAKE_CASE          = True if config.feat_extract_norm == """layer""" else False
   _SCREAMING_SNAKE_CASE          = WavaVecaFeatureExtractor(
       feature_size=1			,					sampling_rate=1_60_00			,					padding_value=0			,					do_normalize=SCREAMING_SNAKE_CASE_			,					return_attention_mask=SCREAMING_SNAKE_CASE_			,					)

   if is_finetuned:
      if dict_path:
         _SCREAMING_SNAKE_CASE          = Dictionary.load(SCREAMING_SNAKE_CASE_  )

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

      _SCREAMING_SNAKE_CASE          = SEWDForCTC(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = 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__":
     UpperCamelCase__					:						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"
     )
     UpperCamelCase__					:						List[str]											=     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 sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


UpperCamelCase__					:						Tuple											=     datasets.utils.logging.get_logger(__name__)

@dataclass
class       _a (datasets.BuilderConfig):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     True
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     None
       SCREAMING_SNAKE_CASE					=     1_00_00
       SCREAMING_SNAKE_CASE					=     None






       def 				UpperCamelCase (							self					)			->	str:
          if self.sql is None:
             raise ValueError("""sql must be specified"""					)
          if self.con is None:
             raise ValueError("""con must be specified"""					)






       def 				UpperCamelCase (							self				,							A__				,							A__ = None				,							)			->	str:
          _SCREAMING_SNAKE_CASE          = config_kwargs.copy()
          # We need to stringify the Selectable object to make its hash deterministic

          # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
          _SCREAMING_SNAKE_CASE          = config_kwargs["""sql"""]
          if not isinstance(A__				,							A__					):
             if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                import sqlalchemy

                if isinstance(A__				,							sqlalchemy.sql.Selectable					):
                   _SCREAMING_SNAKE_CASE          = sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""					)[0] + """://"""					)
                   _SCREAMING_SNAKE_CASE          = str(sql.compile(dialect=engine.dialect					)					)
                   _SCREAMING_SNAKE_CASE          = sql_str
                else:
                   raise TypeError(
                       F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__					)}: {sql}"					)
             else:
                raise TypeError(
                    F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(A__					)}: {sql}"					)
          _SCREAMING_SNAKE_CASE          = config_kwargs["""con"""]
          if not isinstance(A__				,							A__					):
             _SCREAMING_SNAKE_CASE          = id(A__					)
             logger.info(
                 F"SQL connection 'con' of type {type(A__					)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."					)

          return super().create_config_id(A__				,							custom_features=A__					)






       @property
       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = {
              """index_col""": self.index_col,
              """columns""": self.columns,
              """params""": self.params,
              """coerce_float""": self.coerce_float,
              """parse_dates""": self.parse_dates,
          }
          return pd_read_sql_kwargs






class       _a (datasets.ArrowBasedBuilder):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     SqlConfig






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return datasets.DatasetInfo(features=self.config.features					)






       def 				UpperCamelCase (							self				,							A__					)			->	Dict:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,							gen_kwargs={}					)]






       def 				UpperCamelCase (							self				,							A__					)			->	pa.Table:
          if self.config.features is not None:
             _SCREAMING_SNAKE_CASE          = self.config.features.arrow_schema
             if all(not require_storage_cast(A__					) for feature in self.config.features.values()					):
                # cheaper cast
                _SCREAMING_SNAKE_CASE          = pa.Table.from_arrays([pa_table[field.name] for field in schema]				,							schema=A__					)
             else:
                # more expensive cast; allows str <-> int/float or str to Audio for example
                _SCREAMING_SNAKE_CASE          = table_cast(A__				,							A__					)
          return pa_table






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          _SCREAMING_SNAKE_CASE          = self.config.chunksize
          _SCREAMING_SNAKE_CASE          = pd.read_sql(
              self.config.sql				,							self.config.con				,							chunksize=A__				,							**self.config.pd_read_sql_kwargs					)
          _SCREAMING_SNAKE_CASE          = [sql_reader] if chunksize is None else sql_reader
          for chunk_idx, df in enumerate(A__					):
             _SCREAMING_SNAKE_CASE          = pa.Table.from_pandas(A__					)
             yield chunk_idx, self._cast_table(A__					)






