
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCamelCase           =logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_lowerCamelCase           ={
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCamelCase           ={
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCamelCase           =sorted(arg_to_scheduler.keys())
_lowerCamelCase           ="""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class 							A__	(			pl.LightningModule):
      def __init__(   self					,						__magic_name__					,						__magic_name__=None					,						__magic_name__="base"					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__					,						):
       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(__magic_name__   )
       lowerCamelCase	:			List[Any]           =   0
       lowerCamelCase	:			Optional[Any]           =   Path(self.hparams.output_dir   )
       lowerCamelCase	:			Optional[int]           =   self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
        lowerCamelCase	:			Optional[Any]           =   AutoConfig.from_pretrained(
            self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,						**({"""num_labels""": num_labels} if num_labels is not None else {})					,						cache_dir=__magic_name__					,						**__magic_name__					,						)
       else:
        lowerCamelCase	:			PretrainedConfig           =   config

       lowerCamelCase	:			int           =   ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
       for p in extra_model_params:
        if getattr(self.hparams					,						__magic_name__					,						__magic_name__   ):
         assert hasattr(self.config					,						__magic_name__   ), F'''model config doesn\'t have a `{p}` attribute'''
         setattr(self.config					,						__magic_name__					,						getattr(self.hparams					,						__magic_name__   )   )

       if tokenizer is None:
        lowerCamelCase	:			List[Any]           =   AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,						cache_dir=__magic_name__					,						)
       else:
        lowerCamelCase	:			PreTrainedTokenizer           =   tokenizer
       lowerCamelCase	:			Any           =   MODEL_MODES[mode]
       if model is None:
        lowerCamelCase	:			Optional[Any]           =   self.model_type.from_pretrained(
            self.hparams.model_name_or_path					,						from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path   )					,						config=self.config					,						cache_dir=__magic_name__					,						)
       else:
        lowerCamelCase	:			int           =   model
      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       lowerCamelCase	:			Dict           =   self.model_type.from_pretrained(*__magic_name__					,						**__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   arg_to_scheduler[self.hparams.lr_scheduler]
       lowerCamelCase	:			List[Any]           =   get_schedule_func(
           self.opt					,						num_warmup_steps=self.hparams.warmup_steps					,						num_training_steps=self.total_steps()   )
       lowerCamelCase	:			int           =   {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
       return scheduler
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			int           =   self.model
       lowerCamelCase	:			Tuple           =   ["""bias""", """LayerNorm.weight"""]
       lowerCamelCase	:			Dict           =   [
           {
               """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
               """weight_decay""": self.hparams.weight_decay,
           },
           {
               """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )],
               """weight_decay""": 0.0,
           },
       ]
       if self.hparams.adafactor:
        lowerCamelCase	:			List[str]           =   Adafactor(
            __magic_name__					,						lr=self.hparams.learning_rate					,						scale_parameter=__magic_name__					,						relative_step=__magic_name__   )

       else:
        lowerCamelCase	:			List[str]           =   AdamW(
            __magic_name__					,						lr=self.hparams.learning_rate					,						eps=self.hparams.adam_epsilon   )
       lowerCamelCase	:			Tuple           =   optimizer

       lowerCamelCase	:			int           =   self.get_lr_scheduler()

       return [optimizer], [scheduler]
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       return self.validation_step(__magic_name__					,						__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       return self.validation_end(__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			str           =   max(1					,						self.hparams.gpus   )  # TODO: consider num_tpu_cores
       lowerCamelCase	:			List[str]           =   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
       return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       if mode == "test":
        lowerCamelCase	:			Dict           =   len(self.test_dataloader().dataset   )
       else:
        lowerCamelCase	:			str           =   self.get_dataloader("""train"""					,						self.hparams.train_batch_size					,						shuffle=__magic_name__   )
        lowerCamelCase	:			Union[str, Any]           =   len(self.train_dataloader().dataset   )
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__ = False   ):
       raise NotImplementedError("""You must implement this for your task"""   )
      def 		UpperCamelCase__							(   self   ):
       return self.train_loader
      def 		UpperCamelCase__							(   self   ):
       return self.get_dataloader("""dev"""					,						self.hparams.eval_batch_size					,						shuffle=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       return self.get_dataloader("""test"""					,						self.hparams.eval_batch_size					,						shuffle=__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       return os.path.join(
           self.hparams.data_dir					,						"""cached_{}_{}_{}""".format(
               __magic_name__					,						list(filter(__magic_name__					,						self.hparams.model_name_or_path.split("""/"""   )   )   ).pop()					,						str(self.hparams.max_seq_length   )					,						)					,						)
      @pl.utilities.rank_zero_only
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			Tuple           =   self.output_dir.joinpath("""best_tfmr"""   )
       lowerCamelCase	:			Any           =   self.step_count
       self.model.save_pretrained(__magic_name__   )
       self.tokenizer.save_pretrained(__magic_name__   )






      @staticmethod
      def 		UpperCamelCase__							(   __magic_name__					,						__magic_name__   ):
       parser.add_argument(
           """--model_name_or_path"""					,						default=__magic_name__					,						type=__magic_name__					,						required=__magic_name__					,						help="""Path to pretrained model or model identifier from huggingface.co/models"""					,						)
       parser.add_argument(
           """--config_name"""					,						default=""""""					,						type=__magic_name__					,						help="""Pretrained config name or path if not the same as model_name"""   )
       parser.add_argument(
           """--tokenizer_name"""					,						default=__magic_name__					,						type=__magic_name__					,						help="""Pretrained tokenizer name or path if not the same as model_name"""					,						)
       parser.add_argument(
           """--cache_dir"""					,						default=""""""					,						type=__magic_name__					,						help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""					,						)
       parser.add_argument(
           """--encoder_layerdrop"""					,						type=__magic_name__					,						help="""Encoder layer dropout probability (Optional). Goes into model.config"""					,						)
       parser.add_argument(
           """--decoder_layerdrop"""					,						type=__magic_name__					,						help="""Decoder layer dropout probability (Optional). Goes into model.config"""					,						)
       parser.add_argument(
           """--dropout"""					,						type=__magic_name__					,						help="""Dropout probability (Optional). Goes into model.config"""					,						)
       parser.add_argument(
           """--attention_dropout"""					,						type=__magic_name__					,						help="""Attention dropout probability (Optional). Goes into model.config"""					,						)
       parser.add_argument("""--learning_rate"""					,						default=5e-5					,						type=__magic_name__					,						help="""The initial learning rate for Adam."""   )
       parser.add_argument(
           """--lr_scheduler"""					,						default="""linear"""					,						choices=__magic_name__					,						metavar=__magic_name__					,						type=__magic_name__					,						help="""Learning rate scheduler"""					,						)
       parser.add_argument("""--weight_decay"""					,						default=0.0					,						type=__magic_name__					,						help="""Weight decay if we apply some."""   )
       parser.add_argument("""--adam_epsilon"""					,						default=1e-8					,						type=__magic_name__					,						help="""Epsilon for Adam optimizer."""   )
       parser.add_argument("""--warmup_steps"""					,						default=0					,						type=__magic_name__					,						help="""Linear warmup over warmup_steps."""   )
       parser.add_argument("""--num_workers"""					,						default=4					,						type=__magic_name__					,						help="""kwarg passed to DataLoader"""   )
       parser.add_argument("""--num_train_epochs"""					,						dest="""max_epochs"""					,						default=3					,						type=__magic_name__   )
       parser.add_argument("""--train_batch_size"""					,						default=3_2					,						type=__magic_name__   )
       parser.add_argument("""--eval_batch_size"""					,						default=3_2					,						type=__magic_name__   )
       parser.add_argument("""--adafactor"""					,						action="""store_true"""   )







class 							A__	(			pl.Callback):
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       lowerCamelCase	:			Optional[int]           =   trainer.lr_schedulers[0]["""scheduler"""]
       lowerCamelCase	:			List[str]           =   {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
       pl_module.logger.log_metrics(__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       rank_zero_info("""***** Validation results *****"""   )
       lowerCamelCase	:			str           =   trainer.callback_metrics
       # Log results
       for key in sorted(__magic_name__   ):
        if key not in ["log", "progress_bar"]:
         rank_zero_info("""{} = {}\n""".format(__magic_name__					,						str(metrics[key]   )   )   )






      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__   ):
       rank_zero_info("""***** Test results *****"""   )
       lowerCamelCase	:			Any           =   trainer.callback_metrics
       # Log and save results to file
       lowerCamelCase	:			Dict           =   os.path.join(pl_module.hparams.output_dir					,						"""test_results.txt"""   )
       with open(__magic_name__					,						"""w"""   ) as writer:
        for key in sorted(__magic_name__   ):
         if key not in ["log", "progress_bar"]:
          rank_zero_info("""{} = {}\n""".format(__magic_name__					,						str(metrics[key]   )   )   )
          writer.write("""{} = {}\n""".format(__magic_name__					,						str(metrics[key]   )   )   )







def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     """--output_dir""",			default=lowerCamelCase,			type=lowerCamelCase,			required=lowerCamelCase,			help="""The output directory where the model predictions and checkpoints will be written.""",			)
 parser.add_argument(
     """--fp16""",			action="""store_true""",			help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""",			)

 parser.add_argument(
     """--fp16_opt_level""",			type=lowerCamelCase,			default="""O2""",			help=(
         """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
         """See details at https://nvidia.github.io/apex/amp.html"""
     ),			)
 parser.add_argument("""--n_tpu_cores""",			dest="""tpu_cores""",			type=lowerCamelCase							)
 parser.add_argument("""--max_grad_norm""",			dest="""gradient_clip_val""",			default=1.0,			type=lowerCamelCase,			help="""Max gradient norm"""							)
 parser.add_argument("""--do_train""",			action="""store_true""",			help="""Whether to run training."""							)
 parser.add_argument("""--do_predict""",			action="""store_true""",			help="""Whether to run predictions on the test set."""							)
 parser.add_argument(
     """--gradient_accumulation_steps""",			dest="""accumulate_grad_batches""",			type=lowerCamelCase,			default=1,			help="""Number of updates steps to accumulate before performing a backward/update pass.""",			)
 parser.add_argument("""--seed""",			type=lowerCamelCase,			default=42,			help="""random seed for initialization"""							)
 parser.add_argument(
     """--data_dir""",			default=lowerCamelCase,			type=lowerCamelCase,			required=lowerCamelCase,			help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""",			)





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=True,			lowerCamelCase=[],			lowerCamelCase=None,			lowerCamelCase=None,			**lowerCamelCase,			):
 pl.seed_everything(args.seed							)

 # init model
 lowerCamelCase	:			List[str]           =   Path(model.hparams.output_dir							)
 odir.mkdir(exist_ok=lowerCamelCase							)

 # add custom checkpoints
 if checkpoint_callback is None:
  lowerCamelCase	:			int           =   pl.callbacks.ModelCheckpoint(
      filepath=args.output_dir,			prefix="""checkpoint""",			monitor="""val_loss""",			mode="""min""",			save_top_k=1							)
 if early_stopping_callback:
  extra_callbacks.append(lowerCamelCase							)
 if logging_callback is None:
  lowerCamelCase	:			Tuple           =   LoggingCallback()

 lowerCamelCase	:			Dict           =   {}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  lowerCamelCase	:			int           =   16
  lowerCamelCase	:			Tuple           =   args.fpaa_opt_level

 if args.gpus > 1:
  lowerCamelCase	:			int           =   """ddp"""

 lowerCamelCase	:			Any           =   args.accumulate_grad_batches
 lowerCamelCase	:			str           =   extra_train_kwargs.get("""accelerator""",			lowerCamelCase							)
 lowerCamelCase	:			Optional[Any]           =   extra_train_kwargs.get("""profiler""",			lowerCamelCase							)

 lowerCamelCase	:			Optional[int]           =   pl.Trainer.from_argparse_args(
     lowerCamelCase,			weights_summary=lowerCamelCase,			callbacks=[logging_callback] + extra_callbacks,			logger=lowerCamelCase,			checkpoint_callback=lowerCamelCase,			**lowerCamelCase,			)

 if args.do_train:
  trainer.fit(lowerCamelCase							)

 return trainer






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
      )




