




import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


_snake_case										=       logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_ = 16000     ):
      _A							:     Optional[int]			=						int(round(sample_rate * max_length     )     )
      if len(snake_case_     ) <= sample_length:
            return wav
      _A							:     Any			=						randint(0,len(snake_case_     ) - sample_length - 1     )
      return wav[random_offset : random_offset + sample_length]







@dataclass
class 						lowercase							:
     _a							 =						field(default=lowerCAmelCase_,metadata={"help": "Name of a dataset from the datasets package"}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "A file containing the training audio paths and labels."}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "A file containing the validation audio paths and labels."}		)
     _a							 =						field(
         default="train",metadata={
             "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
         },)
     _a							 =						field(
         default="validation",metadata={
             "help": (
                 "The name of the training data set split to use (via the datasets library). Defaults to 'validation'"
             )
         },)
     _a							 =						field(
         default="audio",metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},)
     _a							 =						field(
         default="label",metadata={"help": "The name of the dataset column containing the labels. Defaults to 'label'"}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         },)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         },)
     _a							 =						field(
         default=2_0,metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."},)







@dataclass
class 						lowercase							:
     _a							 =						field(
         default="facebook/wav2vec2-base",metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}		)
     _a							 =						field(
         default="main",metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "Name or path of preprocessor config."}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "Whether to freeze the feature encoder layers of the model."}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "Whether to generate an attention mask in the feature extractor."}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         },)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "Whether to freeze the feature extractor layers of the model."}		)
     _a							 =						field(
         default=lowerCAmelCase_,metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},)

     def 					a__	(  self				)						->							str:
           if not self.freeze_feature_extractor and self.freeze_feature_encoder:
                 warnings.warn(
                     """The argument `--freeze_feature_extractor` is deprecated and """
                     """will be removed in a future version. Use `--freeze_feature_encoder`"""
                     """instead. Setting `freeze_feature_encoder==True`."""		,     _snake_case		,     )
           if self.freeze_feature_extractor and not self.freeze_feature_encoder:
                 raise ValueError(
                     """The argument `--freeze_feature_extractor` is deprecated and """
                     """should not be used in combination with `--freeze_feature_encoder`."""
                     """Only make use of `--freeze_feature_encoder`."""				)







def 				lowerCAmelCase_	(     ):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      _A							:     Optional[int]			=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
      if len(sys.argv     ) == 2 and sys.argv[1].endswith(""".json"""     ):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            _A							:     Dict			=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            _A							:     str			=						parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry("""run_audio_classification""",snake_case_,snake_case_     )

      # Setup logging
      logging.basicConfig(
          format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",datefmt="""%m/%d/%Y %H:%M:%S""",handlers=[logging.StreamHandler(sys.stdout     )],)

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      _A							:     Optional[Any]			=						training_args.get_process_log_level()
      logger.setLevel(snake_case_     )
      transformers.utils.logging.set_verbosity(snake_case_     )
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '''
          + f'''distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'''     )
      logger.info(f'''Training/evaluation parameters {training_args}'''     )

      # Set seed before initializing model.
      set_seed(training_args.seed     )

      # Detecting last checkpoint.
      _A							:     Dict			=						None
      if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
            _A							:     Optional[int]			=						get_last_checkpoint(training_args.output_dir     )
            if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
                  raise ValueError(
                      f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                      """Use --overwrite_output_dir to train from scratch."""     )
            elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                  logger.info(
                      f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                      """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""     )

    # Initialize our dataset and prepare it for the audio classification task.
      _A							:     Dict			=						DatasetDict()
      _A							:     Tuple			=						load_dataset(
          data_args.dataset_name,data_args.dataset_config_name,split=data_args.train_split_name,use_auth_token=True if model_args.use_auth_token else None,)
      _A							:     Optional[int]			=						load_dataset(
          data_args.dataset_name,data_args.dataset_config_name,split=data_args.eval_split_name,use_auth_token=True if model_args.use_auth_token else None,)

      if data_args.audio_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                f'''--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
                """Make sure to set `--audio_column_name` to the correct audio column - one of """
                f'''{", ".join(raw_datasets["train"].column_names     )}.'''     )

      if data_args.label_column_name not in raw_datasets["train"].column_names:
            raise ValueError(
                f'''--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
                """Make sure to set `--label_column_name` to the correct text column - one of """
                f'''{", ".join(raw_datasets["train"].column_names     )}.'''     )

      # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
      # transformer outputs in the classifier, but it doesn't always lead to better accuracy
      _A							:     Optional[Any]			=						AutoFeatureExtractor.from_pretrained(
          model_args.feature_extractor_name or model_args.model_name_or_path,return_attention_mask=model_args.attention_mask,cache_dir=model_args.cache_dir,revision=model_args.model_revision,use_auth_token=True if model_args.use_auth_token else None,)

      # `datasets` takes care of automatically loading and resampling the audio,
      # so we just need to set the correct target sampling rate.
      _A							:     List[str]			=						raw_datasets.cast_column(
          data_args.audio_column_name,datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate     )     )

      _A							:     Union[str, Any]			=						feature_extractor.model_input_names[0]

      def train_transforms(snake_case_     ):
            _A							:     Optional[Any]			=						[]
            for audio in batch[data_args.audio_column_name]:
                  _A							:     List[Any]			=						random_subsample(
                      audio["""array"""],max_length=data_args.max_length_seconds,sample_rate=feature_extractor.sampling_rate     )
                  subsampled_wavs.append(snake_case_     )
            _A							:     Optional[int]			=						feature_extractor(snake_case_,sampling_rate=feature_extractor.sampling_rate     )
            _A							:     Optional[int]			=						{model_input_name: inputs.get(snake_case_     )}
            _A							:     Any			=						list(batch[data_args.label_column_name]     )

            return output_batch

      def val_transforms(snake_case_     ):
            _A							:     Optional[Any]			=						[audio['array'] for audio in batch[data_args.audio_column_name]]
            _A							:     Union[str, Any]			=						feature_extractor(snake_case_,sampling_rate=feature_extractor.sampling_rate     )
            _A							:     List[str]			=						{model_input_name: inputs.get(snake_case_     )}
            _A							:     List[str]			=						list(batch[data_args.label_column_name]     )

            return output_batch

      # Prepare label mappings.
      # We'll include these in the model's config to get human readable labels in the Inference API.
      _A							:     Dict			=						raw_datasets['train'].features[data_args.label_column_name].names
      _A							:     Tuple			=						{}, {}
      for i, label in enumerate(snake_case_     ):
            _A							:     Tuple			=						str(snake_case_     )
            _A							:     List[str]			=						label

      # Load the accuracy metric from the datasets package
      _A							:     Union[str, Any]			=						evaluate.load("""accuracy"""     )

      # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
      # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
      def compute_metrics(snake_case_     ):
            _A							:     Tuple			=						np.argmax(eval_pred.predictions,axis=1     )
            return metric.compute(predictions=snake_case_,references=eval_pred.label_ids     )

      _A							:     List[str]			=						AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path,num_labels=len(snake_case_     ),labelaid=snake_case_,idalabel=snake_case_,finetuning_task="""audio-classification""",cache_dir=model_args.cache_dir,revision=model_args.model_revision,use_auth_token=True if model_args.use_auth_token else None,)
      _A							:     Any			=						AutoModelForAudioClassification.from_pretrained(
          model_args.model_name_or_path,from_tf=bool(""".ckpt""" in model_args.model_name_or_path     ),config=snake_case_,cache_dir=model_args.cache_dir,revision=model_args.model_revision,use_auth_token=True if model_args.use_auth_token else None,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,)

      # freeze the convolutional waveform encoder
      if model_args.freeze_feature_encoder:
            model.freeze_feature_encoder()

      if training_args.do_train:
            if data_args.max_train_samples is not None:
                  _A							:     Dict			=						(
                      raw_datasets['train'].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
                  )
            # Set the training transforms
            raw_datasets["train"].set_transform(snake_case_,output_all_columns=snake_case_     )

      if training_args.do_eval:
            if data_args.max_eval_samples is not None:
                  _A							:     List[Any]			=						(
                      raw_datasets['eval'].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
                  )
            # Set the validation transforms
            raw_datasets["eval"].set_transform(snake_case_,output_all_columns=snake_case_     )

      # Initialize our trainer
      _A							:     Dict			=						Trainer(
          model=snake_case_,args=snake_case_,train_dataset=raw_datasets["""train"""] if training_args.do_train else None,eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None,compute_metrics=snake_case_,tokenizer=snake_case_,)

      # Training
      if training_args.do_train:
            _A							:     Tuple			=						None
            if training_args.resume_from_checkpoint is not None:
                  _A							:     Any			=						training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  _A							:     Optional[Any]			=						last_checkpoint
            _A							:     int			=						trainer.train(resume_from_checkpoint=snake_case_     )
            trainer.save_model()
            trainer.log_metrics("""train""",train_result.metrics     )
            trainer.save_metrics("""train""",train_result.metrics     )
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            _A							:     int			=						trainer.evaluate()
            trainer.log_metrics("""eval""",snake_case_     )
            trainer.save_metrics("""eval""",snake_case_     )

      # Write model card and (optionally) push to hub
      _A							:     Any			=						{
          'finetuned_from': model_args.model_name_or_path,
          'tasks': 'audio-classification',
          'dataset': data_args.dataset_name,
          'tags': ['audio-classification'],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**snake_case_     )
      else:
            trainer.create_model_card(**snake_case_     )


if __name__ == "__main__":
  main()






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
a_   :List[Any]        =      logging.get_logger(__name__)

a_   :List[Any]        =      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
a_   :List[Any]        =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]
def        lowercase_							(A : Dict      ):
       snake_case__							:	Optional[Any]   	=	{}
       with open(A		,					'r'      ) as file:
              for line_number, line in enumerate(A      ):
                     snake_case__							:	Dict   	=	line.strip()
                     if line:
                            snake_case__							:	int   	=	line.split()
                            snake_case__							:	List[str]   	=	line_number
                            snake_case__							:	Dict   	=	words[0]
                            snake_case__							:	Optional[Any]   	=	value
       return result
def        lowercase_							(A : int		,					A : int		,					A : Optional[int]		,					A : Optional[Any]		,					A : Tuple      ):
       for attribute in key.split('.'      ):
              snake_case__							:	Optional[int]   	=	getattr(A		,					A      )

       snake_case__							:	Union[str, Any]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	Dict   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	Union[str, Any]   	=	getattr(A		,					A      ).shape
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Optional[int]   	=	hf_pointer
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	Optional[Any]   	=	getattr(A		,					A      )
              snake_case__							:	Dict   	=	shape_pointer.shape

              # let's reduce dimension
              snake_case__							:	List[Any]   	=	value[0]
       else:
              snake_case__							:	Union[str, Any]   	=	hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  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__							:	Any   	=	value
       elif weight_type == "weight_g":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "weight_v":
              snake_case__							:	Any   	=	value
       elif weight_type == "bias":
              snake_case__							:	List[Any]   	=	value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'      ):
                     snake_case__							:	int   	=	getattr(A		,					A      )
              snake_case__							:	Optional[int]   	=	value
       else:
              snake_case__							:	Optional[Any]   	=	value

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )
def        lowercase_							(A : Tuple		,					A : List[Any]		,					A : int		,					A : str		,					A : Tuple      ):
       snake_case__							:	Optional[int]   	=	None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(A      ):
                     snake_case__							:	List[str]   	=	PARAM_MAPPING[full_name.split('.'      )[-1]]
                     snake_case__							:	str   	=	'param'

       if weight_type is not None and weight_type != "param":
              snake_case__							:	int   	=	'.'.join([key, weight_type]      )
       elif weight_type is not None and weight_type == "param":
              snake_case__							:	Any   	=	'.'.join([key, hf_param_name]      )
       else:
              snake_case__							:	Dict   	=	key

       snake_case__							:	List[str]   	=	value if 'lm_head' in full_key else value[0]


a_   :List[str]        =      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}
def        lowercase_							(A : str		,					A : Optional[Any]		,					A : Optional[Any]=None		,					A : List[str]=None      ):
       snake_case__							:	Optional[int]   	=	False
       for key, mapped_key in MAPPING.items():
              snake_case__							:	Tuple   	=	'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
              if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                     snake_case__							:	Optional[int]   	=	True
                     if "*" in mapped_key:
                            snake_case__							:	List[Any]   	=	name.split(A      )[0].split('.'      )[-2]
                            snake_case__							:	Union[str, Any]   	=	mapped_key.replace('*'		,					A      )
                     if "weight_g" in name:
                            snake_case__							:	Tuple   	=	'weight_g'
                     elif "weight_v" in name:
                            snake_case__							:	List[str]   	=	'weight_v'
                     elif "bias" in name:
                            snake_case__							:	Dict   	=	'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            snake_case__							:	Optional[int]   	=	'weight'
                     else:
                            snake_case__							:	str   	=	None
                     if hf_dict is not None:
                            rename_dict(A		,					A		,					A		,					A		,					A      )
                     else:
                            set_recursively(A		,					A		,					A		,					A		,					A      )
                     return is_used
       return is_used
def        lowercase_							(A : Optional[Any]		,					A : Dict		,					A : Optional[int]      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	Tuple   	=	fairseq_model.state_dict()

       snake_case__							:	str   	=	hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              snake_case__							:	str   	=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == 'group'		,					)
                     snake_case__							:	Any   	=	True
              else:
                     snake_case__							:	Dict   	=	load_wavaveca_layer(A		,					A		,					A      )
              if not is_used:
                     unused_weights.append(A      )

       logger.warning(F'''Unused weights: {unused_weights}'''      )
def        lowercase_							(A : Dict		,					A : Optional[Any]		,					A : Tuple		,					A : str		,					A : List[str]      ):
       snake_case__							:	List[Any]   	=	full_name.split('conv_layers.'      )[-1]
       snake_case__							:	List[str]   	=	name.split('.'      )
       snake_case__							:	List[Any]   	=	int(items[0]      )
       snake_case__							:	str   	=	int(items[1]      )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''      )
                     snake_case__							:	Any   	=	value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''      )
                     snake_case__							:	str   	=	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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''      )
                     snake_case__							:	str   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                F'''{full_name} has size {value.shape}, but'''
                                F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''      )
                     snake_case__							:	int   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
@torch.no_grad()
def        lowercase_							(A : Union[str, Any]		,					A : str		,					A : Tuple=None		,					A : List[str]=None		,					A : Any=True		,					A : Optional[int]=False      ):
       if config_path is not None:
              snake_case__							:	List[Any]   	=	WavaVecaConfig.from_pretrained(A      )
       else:
              snake_case__							:	List[Any]   	=	WavaVecaConfig()

       if is_seq_class:
              snake_case__							:	Dict   	=	read_txt_into_dict(A      )
              snake_case__							:	Any   	=	idalabel
              snake_case__							:	Union[str, Any]   	=	WavaVecaForSequenceClassification(A      )
              snake_case__							:	Any   	=	WavaVecaFeatureExtractor(
                  feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
              feature_extractor.save_pretrained(A      )

       elif is_finetuned:
              if dict_path:
                     snake_case__							:	str   	=	Dictionary.load(A      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     snake_case__							:	List[str]   	=	target_dict.pad_index
                     snake_case__							:	Optional[int]   	=	target_dict.bos_index
                     snake_case__							:	Optional[int]   	=	target_dict.eos_index
                     snake_case__							:	List[Any]   	=	len(target_dict.symbols      )
                     snake_case__							:	str   	=	os.path.join(A		,					'vocab.json'      )
                     if not os.path.isdir(A      ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A      )      )
                            return
                     os.makedirs(A		,					exist_ok=A      )
                     snake_case__							:	Optional[Any]   	=	target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     snake_case__							:	Optional[Any]   	=	0
                     snake_case__							:	Union[str, Any]   	=	1
                     with open(A		,					'w'		,					encoding='utf-8'      ) as vocab_handle:
                            json.dump(A		,					A      )
                     snake_case__							:	List[Any]   	=	WavaVecaCTCTokenizer(
                         A		,					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=A		,					)
                     snake_case__							:	str   	=	True if config.feat_extract_norm == 'layer' else False
                     snake_case__							:	Optional[Any]   	=	WavaVecaFeatureExtractor(
                         feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
                     snake_case__							:	Union[str, Any]   	=	WavaVecaProcessor(feature_extractor=A		,					tokenizer=A      )
                     processor.save_pretrained(A      )

              snake_case__							:	str   	=	WavaVecaForCTC(A      )
       else:
              snake_case__							:	int   	=	WavaVecaForPreTraining(A      )

       if is_finetuned or is_seq_class:
              snake_case__	,				snake_case__	,				snake_case__							:	str   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]		,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
       else:
              snake_case__							:	Tuple   	=	argparse.Namespace(task='audio_pretraining'      )
              snake_case__							:	str   	=	fairseq.tasks.setup_task(A      )

              snake_case__	,				snake_case__	,				snake_case__							:	Any   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,					task=A      )

       snake_case__							:	List[Any]   	=	model[0].eval()

       recursively_load_weights(A		,					A		,					not is_finetuned      )

       hf_wavavec.save_pretrained(A      )


if __name__ == "__main__":
 a_   :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(
     "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 parser.add_argument(
     "--is_seq_class",
     action="store_true",
     help="Whether the model to convert is a fine-tuned sequence classification model or not",
 )
 a_   :str        =      parser.parse_args()

 a_   :Tuple        =      not args.not_finetuned and not args.is_seq_class
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.dict_path,
     is_finetuned,
     args.is_seq_class,
 )






