"""simple docstring"""







import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  List[str] = None  ,    ):
     lowercase_			:       Optional[Any]									=			{}

     if train_file is not None:
          lowercase_			:       Optional[Any]									=			[train_file]
     if eval_file is not None:
          lowercase_			:       Optional[Any]									=			[eval_file]
     if test_file is not None:
          lowercase_			:       str									=			[test_file]

     lowercase_			:       Dict									=			datasets.load_dataset('csv'  ,    data_files=_SCREAMING_SNAKE_CASE   )
     lowercase_			:       Optional[Any]									=			list(ds[list(files.keys()   )[0]].features.keys()   )
     lowercase_			:       List[Any]									=			features_name.pop(_SCREAMING_SNAKE_CASE   )
     lowercase_			:       Any									=			list(set(ds[list(files.keys()   )[0]][label_name]   )   )
     lowercase_			:       List[Any]									=			{label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE   )}
     lowercase_			:       Any									=			tokenizer.model_input_names
     lowercase_			:       List[str]									=			{}

     if len(_SCREAMING_SNAKE_CASE   ) == 1:
          for k in files.keys():
               lowercase_			:       Dict									=			ds[k].map(
                   lambda __SCREAMING_SNAKE_CASE   : tokenizer.batch_encode_plus(
                       example[features_name[0]]  ,    truncation=_SCREAMING_SNAKE_CASE  ,    max_length=_SCREAMING_SNAKE_CASE  ,    padding='max_length'   )  ,    batched=_SCREAMING_SNAKE_CASE  ,    )
     elif len(_SCREAMING_SNAKE_CASE   ) == 2:
          for k in files.keys():
               lowercase_			:       Optional[Any]									=			ds[k].map(
                   lambda __SCREAMING_SNAKE_CASE   : tokenizer.batch_encode_plus(
                       (example[features_name[0]], example[features_name[1]])  ,    truncation=_SCREAMING_SNAKE_CASE  ,    max_length=_SCREAMING_SNAKE_CASE  ,    padding='max_length'  ,    )  ,    batched=_SCREAMING_SNAKE_CASE  ,    )

     def gen_train():
          for ex in transformed_ds[datasets.Split.TRAIN]:
               lowercase_			:       List[Any]									=			{k: v for k, v in ex.items() if k in input_names}
               lowercase_			:       Optional[Any]									=			labelaid[ex[label_name]]
               yield (d, label)

     def gen_val():
          for ex in transformed_ds[datasets.Split.VALIDATION]:
               lowercase_			:       Any									=			{k: v for k, v in ex.items() if k in input_names}
               lowercase_			:       Union[str, Any]									=			labelaid[ex[label_name]]
               yield (d, label)

     def gen_test():
          for ex in transformed_ds[datasets.Split.TEST]:
               lowercase_			:       Union[str, Any]									=			{k: v for k, v in ex.items() if k in input_names}
               lowercase_			:       List[Any]									=			labelaid[ex[label_name]]
               yield (d, label)

     lowercase_			:       Any									=			(
         tf.data.Dataset.from_generator(
             _SCREAMING_SNAKE_CASE  ,    ({k: tf.intaa for k in input_names}, tf.intaa)  ,    ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))  ,    )
         if datasets.Split.TRAIN in transformed_ds
         else None
     )

     if train_ds is not None:
          lowercase_			:       int									=			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

     lowercase_			:       List[str]									=			(
         tf.data.Dataset.from_generator(
             _SCREAMING_SNAKE_CASE  ,    ({k: tf.intaa for k in input_names}, tf.intaa)  ,    ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))  ,    )
         if datasets.Split.VALIDATION in transformed_ds
         else None
     )

     if val_ds is not None:
          lowercase_			:       Any									=			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

     lowercase_			:       List[str]									=			(
         tf.data.Dataset.from_generator(
             _SCREAMING_SNAKE_CASE  ,    ({k: tf.intaa for k in input_names}, tf.intaa)  ,    ({k: tf.TensorShape([None]   ) for k in input_names}, tf.TensorShape([]   ))  ,    )
         if datasets.Split.TEST in transformed_ds
         else None
     )

     if test_ds is not None:
          lowercase_			:       Optional[Any]									=			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

     return train_ds, val_ds, test_ds, labelaid


__SCREAMING_SNAKE_CASE								=logging.getLogger(__name__)







@dataclass
class  UpperCamelCase	:
   lowercase							=						field(metadata={'help': 'Which column contains the label'}						)
   lowercase							=						field(default=snake_case__		,   metadata={'help': 'The path of the training file'}						)
   lowercase							=						field(default=snake_case__		,   metadata={'help': 'The path of the development file'}						)
   lowercase							=						field(default=snake_case__		,   metadata={'help': 'The path of the test file'}						)
   lowercase							=						field(
       default=1_2_8		,   metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }		,   )
   lowercase							=						field(
       default=snake_case__		,   metadata={'help': 'Overwrite the cached training and evaluation sets'}						)







@dataclass
class  UpperCamelCase	:
   lowercase							=						field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
   lowercase							=						field(
       default=snake_case__		,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
   lowercase							=						field(
       default=snake_case__		,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
   lowercase							=						field(default=snake_case__		,   metadata={'help': 'Set this flag to use fast tokenization.'}						)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   lowercase							=						field(
       default=snake_case__		,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,   )






def     lowercase__( ):
     # 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.
     lowercase_			:       Dict									=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
     lowercase_							,						lowercase_							,						lowercase_			:       List[Any]									=			parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir   )
         and os.listdir(training_args.output_dir   )
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
              ' --overwrite_output_dir to overcome.'   )

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'  ,    datefmt='%m/%d/%Y %H:%M:%S'  ,    level=logging.INFO  ,    )
     logger.info(
         F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1   )}, '''
         F'''16-bits training: {training_args.fpaa}'''   )
     logger.info(F'''Training/evaluation parameters {training_args}'''   )

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     lowercase_			:       Optional[Any]									=			AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,    cache_dir=model_args.cache_dir  ,    )

     lowercase_							,						lowercase_							,						lowercase_							,						lowercase_			:       str									=			get_tfds(
         train_file=data_args.train_file  ,    eval_file=data_args.dev_file  ,    test_file=data_args.test_file  ,    tokenizer=_SCREAMING_SNAKE_CASE  ,    label_column_id=data_args.label_column_id  ,    max_seq_length=data_args.max_seq_length  ,    )

     lowercase_			:       List[str]									=			AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,    num_labels=len(_SCREAMING_SNAKE_CASE   )  ,    labelaid=_SCREAMING_SNAKE_CASE  ,    idalabel={id: label for label, id in labelaid.items()}  ,    finetuning_task='text-classification'  ,    cache_dir=model_args.cache_dir  ,    )

     with training_args.strategy.scope():
          lowercase_			:       Dict									=			TFAutoModelForSequenceClassification.from_pretrained(
              model_args.model_name_or_path  ,    from_pt=bool('.bin' in model_args.model_name_or_path   )  ,    config=_SCREAMING_SNAKE_CASE  ,    cache_dir=model_args.cache_dir  ,    )

     def compute_metrics(__SCREAMING_SNAKE_CASE							:  str   ) -> Dict:
          lowercase_			:       Optional[int]									=			np.argmax(p.predictions  ,    axis=1   )

          return {"acc": (preds == p.label_ids).mean()}

     # Initialize our Trainer
     lowercase_			:       Dict									=			TFTrainer(
         model=_SCREAMING_SNAKE_CASE  ,    args=_SCREAMING_SNAKE_CASE  ,    train_dataset=_SCREAMING_SNAKE_CASE  ,    eval_dataset=_SCREAMING_SNAKE_CASE  ,    compute_metrics=_SCREAMING_SNAKE_CASE  ,    )

     # Training
     if training_args.do_train:
          trainer.train()
          trainer.save_model()
          tokenizer.save_pretrained(training_args.output_dir   )

     # Evaluation
     lowercase_			:       Union[str, Any]									=			{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'   )
          lowercase_			:       int									=			trainer.evaluate()
          lowercase_			:       List[Any]									=			os.path.join(training_args.output_dir  ,    'eval_results.txt'   )

          with open(_SCREAMING_SNAKE_CASE  ,    'w'   ) as writer:
               logger.info('***** Eval results *****'   )

               for key, value in result.items():
                    logger.info(F'''  {key} = {value}'''   )
                    writer.write(F'''{key} = {value}\n'''   )

               results.update(_SCREAMING_SNAKE_CASE   )

     return results


if __name__ == "__main__":
       main()


"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[str]									=			UniSpeechSatForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[Any]									=			downstream_dict['projector.weight']
     lowercase_			:       Optional[Any]									=			downstream_dict['projector.bias']
     lowercase_			:       Optional[int]									=			downstream_dict['model.post_net.linear.weight']
     lowercase_			:       Tuple									=			downstream_dict['model.post_net.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       List[Any]									=			UniSpeechSatForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			downstream_dict['model.linear.weight']
     lowercase_			:       Union[str, Any]									=			downstream_dict['model.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       Any									=			UniSpeechSatForXVector.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			downstream_dict['connector.weight']
     lowercase_			:       List[str]									=			downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          lowercase_			:       Dict									=			downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase_			:       List[Any]									=			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     lowercase_			:       Optional[Any]									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     lowercase_			:       Dict									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     lowercase_			:       Dict									=			downstream_dict['objective.W']
     return model



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       int									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )

     lowercase_			:       str									=			checkpoint['Downstream']

     lowercase_			:       str									=			UniSpeechSatConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Tuple									=			WavaVecaFeatureExtractor.from_pretrained(
         __SCREAMING_SNAKE_CASE  ,    return_attention_mask=__SCREAMING_SNAKE_CASE  ,    do_normalize=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[str]									=			hf_config.architectures[0]
     if arch.endswith('ForSequenceClassification'   ):
          lowercase_			:       List[Any]									=			convert_classification(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForAudioFrameClassification'   ):
          lowercase_			:       List[str]									=			convert_diarization(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForXVector'   ):
          lowercase_			:       Optional[Any]									=			convert_xvector(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     else:
          raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''   )

     if hf_config.use_weighted_layer_sum:
          lowercase_			:       Dict									=			checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE   )
     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE								=argparse.ArgumentParser()
       parser.add_argument(
           "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
       )
       parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
       parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
       __SCREAMING_SNAKE_CASE								=parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

