
import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode





def 					_a      (		lowerCamelCase,			**lowerCamelCase							):
 return RunBeamCommand(
     args.dataset,			args.name,			args.cache_dir,			args.beam_pipeline_options,			args.data_dir,			args.all_configs,			args.save_info or args.save_infos,			args.ignore_verifications,			args.force_redownload,			**lowerCamelCase,			)





class 							A__	(			__SCREAMING_SNAKE_CASE):
      @staticmethod
      def 		UpperCamelCase__							(   __magic_name__   ):
       lowerCamelCase	:			List[Any]           =   parser.add_parser("""run_beam"""					,						help="""Run a Beam dataset processing pipeline"""   )
       run_beam_parser.add_argument("""dataset"""					,						type=__magic_name__					,						help="""Name of the dataset to download"""   )
       run_beam_parser.add_argument("""--name"""					,						type=__magic_name__					,						default=__magic_name__					,						help="""Dataset config name"""   )
       run_beam_parser.add_argument(
           """--cache_dir"""					,						type=__magic_name__					,						default=__magic_name__					,						help="""Cache directory where the datasets are stored"""					,						)
       run_beam_parser.add_argument(
           """--beam_pipeline_options"""					,						type=__magic_name__					,						default=""""""					,						help="""Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"""					,						)
       run_beam_parser.add_argument(
           """--data_dir"""					,						type=__magic_name__					,						default=__magic_name__					,						help="""Can be used to specify a manual directory to get the files from"""					,						)
       run_beam_parser.add_argument("""--all_configs"""					,						action="""store_true"""					,						help="""Test all dataset configurations"""   )
       run_beam_parser.add_argument("""--save_info"""					,						action="""store_true"""					,						help="""Save the dataset infos file"""   )
       run_beam_parser.add_argument(
           """--ignore_verifications"""					,						action="""store_true"""					,						help="""Run the test without checksums and splits checks"""   )
       run_beam_parser.add_argument("""--force_redownload"""					,						action="""store_true"""					,						help="""Force dataset redownload"""   )
       # aliases
       run_beam_parser.add_argument("""--save_infos"""					,						action="""store_true"""					,						help="""alias for save_info"""   )
       run_beam_parser.set_defaults(func=__magic_name__   )
      def __init__(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						**__magic_name__					,						):
       lowerCamelCase	:			Dict           =   dataset
       lowerCamelCase	:			List[str]           =   name
       lowerCamelCase	:			Any           =   cache_dir
       lowerCamelCase	:			Optional[int]           =   beam_pipeline_options
       lowerCamelCase	:			Optional[Any]           =   data_dir
       lowerCamelCase	:			Tuple           =   all_configs
       lowerCamelCase	:			str           =   save_infos
       lowerCamelCase	:			List[Any]           =   ignore_verifications
       lowerCamelCase	:			Optional[Any]           =   force_redownload
       lowerCamelCase	:			Optional[Any]           =   config_kwargs






      def 		UpperCamelCase__							(   self   ):
       import apache_beam as beam

       if self._name is not None and self._all_configs:
        print("""Both parameters `name` and `all_configs` can't be used at once."""   )
        exit(1   )
       lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   self._dataset, self._name
       lowerCamelCase	:			Dict           =   dataset_module_factory(__magic_name__   )
       lowerCamelCase	:			Union[str, Any]           =   import_main_class(dataset_module.module_path   )
       lowerCamelCase	:			List[DatasetBuilder]           =   []
       if self._beam_pipeline_options:
        lowerCamelCase	:			Optional[Any]           =   beam.options.pipeline_options.PipelineOptions(
            flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(""","""   ) if opt]   )
       else:
        lowerCamelCase	:			Optional[int]           =   None
       if self._all_configs and len(builder_cls.BUILDER_CONFIGS   ) > 0:
        for builder_config in builder_cls.BUILDER_CONFIGS:
         builders.append(
             builder_cls(
                 config_name=builder_config.name					,						data_dir=self._data_dir					,						hash=dataset_module.hash					,						beam_options=__magic_name__					,						cache_dir=self._cache_dir					,						base_path=dataset_module.builder_kwargs.get("""base_path"""   )					,						)   )
       else:
        builders.append(
            builder_cls(
                config_name=__magic_name__					,						data_dir=self._data_dir					,						beam_options=__magic_name__					,						cache_dir=self._cache_dir					,						base_path=dataset_module.builder_kwargs.get("""base_path"""   )					,						**self._config_kwargs					,						)   )

       for builder in builders:
        builder.download_and_prepare(
            download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
            if not self._force_redownload
            else DownloadMode.FORCE_REDOWNLOAD					,						download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH   )					,						verification_mode=VerificationMode.NO_CHECKS
            if self._ignore_verifications
            else VerificationMode.ALL_CHECKS					,						try_from_hf_gcs=__magic_name__					,						)
        if self._save_infos:
         builder._save_infos()

       print("""Apache beam run successful."""   )

       # If save_infos=True, the dataset infos file is created next to the loaded module file.
       # Let's move it to the original directory of the dataset script, to allow the user to
       # upload them on S3 at the same time afterwards.
       if self._save_infos:
        lowerCamelCase	:			List[str]           =   os.path.join(builder_cls.get_imported_module_dir()					,						config.DATASETDICT_INFOS_FILENAME   )

        lowerCamelCase	:			Any           =   Path(__magic_name__   ).name + """.py"""

        lowerCamelCase	:			List[str]           =   os.path.join(__magic_name__					,						__magic_name__   )
        if os.path.isfile(__magic_name__   ):
         lowerCamelCase	:			str           =   os.path.dirname(__magic_name__   )
        elif os.path.isfile(__magic_name__   ):
         lowerCamelCase	:			Tuple           =   path
        else:  # in case of a remote dataset
         print(F'''Dataset Infos file saved at {dataset_infos_path}'''   )
         exit(1   )

        # Move datasetinfo back to the user
        lowerCamelCase	:			List[str]           =   os.path.join(__magic_name__					,						config.DATASETDICT_INFOS_FILENAME   )
        copyfile(__magic_name__					,						__magic_name__   )
        print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''   )






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()
_lowerCamelCase           =logging.get_logger(__name__)

_lowerCamelCase           ={
    """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 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 for attribute in key.split("""."""							):
  lowerCamelCase	:			List[str]           =   getattr(lowerCamelCase,			lowerCamelCase							)

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

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





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   []
 lowerCamelCase	:			Union[str, Any]           =   fairseq_model.state_dict()

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

 for name, value in fairseq_dict.items():
  lowerCamelCase	:			str           =   False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			hf_model.config.feat_extract_norm == """group""",			)
   lowerCamelCase	:			int           =   True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase	:			str           =   """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]:
     lowerCamelCase	:			int           =   True
     if "*" in mapped_key:
      lowerCamelCase	:			Any           =   name.split(lowerCamelCase							)[0].split("""."""							)[-2]
      if not layer_index.isnumeric():
       continue
      lowerCamelCase	:			Union[str, Any]           =   mapped_key.replace("""*""",			lowerCamelCase							)
     if "weight_g" in name:
      lowerCamelCase	:			Any           =   """weight_g"""
     elif "weight_v" in name:
      lowerCamelCase	:			int           =   """weight_v"""
     elif "weight" in name:
      lowerCamelCase	:			int           =   """weight"""
     elif "bias" in name:
      lowerCamelCase	:			int           =   """bias"""
     else:
      lowerCamelCase	:			Dict           =   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      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Tuple           =   full_name.split("""conv_layers."""							)[-1]
 lowerCamelCase	:			Optional[int]           =   name.split("""."""							)
 lowerCamelCase	:			str           =   int(items[0]							)
 lowerCamelCase	:			Optional[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.'''
   )
   lowerCamelCase	:			Union[str, 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.'''
   )
   lowerCamelCase	:			int           =   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."
   )
   lowerCamelCase	:			Dict           =   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.'''
   )
   lowerCamelCase	:			Tuple           =   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      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			List[Any]           =   SEWDConfig()
 if is_finetuned:
  lowerCamelCase	:			int           =   model.wav_encoder.wav_model.cfg
 else:
  lowerCamelCase	:			Optional[Any]           =   model.cfg

 lowerCamelCase	:			List[str]           =   fs_config.conv_bias
 lowerCamelCase	:			Tuple           =   eval(fs_config.conv_feature_layers							)
 lowerCamelCase	:			List[str]           =   [x[0] for x in conv_layers]
 lowerCamelCase	:			List[Any]           =   [x[1] for x in conv_layers]
 lowerCamelCase	:			int           =   [x[2] for x in conv_layers]
 lowerCamelCase	:			List[str]           =   """gelu"""
 lowerCamelCase	:			Union[str, Any]           =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
 lowerCamelCase	:			List[str]           =   0.0
 lowerCamelCase	:			Optional[int]           =   fs_config.activation_fn.name
 lowerCamelCase	:			Union[str, Any]           =   fs_config.encoder_embed_dim
 lowerCamelCase	:			Tuple           =   0.0_2
 lowerCamelCase	:			str           =   fs_config.encoder_ffn_embed_dim
 lowerCamelCase	:			Dict           =   1e-5
 lowerCamelCase	:			str           =   fs_config.encoder_layerdrop
 lowerCamelCase	:			Optional[Any]           =   fs_config.encoder_attention_heads
 lowerCamelCase	:			List[Any]           =   fs_config.conv_pos_groups
 lowerCamelCase	:			Optional[int]           =   fs_config.conv_pos
 lowerCamelCase	:			Dict           =   len(lowerCamelCase							)
 lowerCamelCase	:			List[Any]           =   fs_config.encoder_layers
 lowerCamelCase	:			Optional[int]           =   fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 lowerCamelCase	:			Optional[int]           =   fs_config.max_position_embeddings
 lowerCamelCase	:			List[str]           =   fs_config.position_buckets
 lowerCamelCase	:			List[Any]           =   fs_config.share_att_key
 lowerCamelCase	:			Dict           =   fs_config.relative_attention
 lowerCamelCase	:			List[Any]           =   fs_config.position_biased_input
 lowerCamelCase	:			Dict           =   tuple(fs_config.pos_att_type.split("""|"""							)							)
 lowerCamelCase	:			List[str]           =   fs_config.norm_rel_ebd

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

 lowerCamelCase	:			Dict           =   """Wav2Vec2FeatureExtractor"""
 lowerCamelCase	:			int           =   """Wav2Vec2CTCTokenizer"""

 return config





@torch.no_grad()
def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase=True							):

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

 if config_path is not None:
  lowerCamelCase	:			List[str]           =   SEWDConfig.from_pretrained(lowerCamelCase							)
 else:
  lowerCamelCase	:			Optional[int]           =   convert_config(model[0],			lowerCamelCase							)
 lowerCamelCase	:			Dict           =   model[0].eval()

 lowerCamelCase	:			Dict           =   True if config.feat_extract_norm == """layer""" else False
 lowerCamelCase	:			List[str]           =   WavaVecaFeatureExtractor(
     feature_size=1,			sampling_rate=1_6000,			padding_value=0,			do_normalize=lowerCamelCase,			return_attention_mask=lowerCamelCase,			)

 if is_finetuned:
  if dict_path:
   lowerCamelCase	:			List[Any]           =   Dictionary.load(lowerCamelCase							)

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

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

 recursively_load_weights(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

 hf_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase           =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"""
      )
      _lowerCamelCase           =parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )




