



"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase							:       Dict          =       logging.get_logger(__name__)
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      Dict       ,			__UpperCamelCase   :      Tuple     ):


						'''simple docstring'''






						snake_case_ :			List[str]            =      WavaVecaForSequenceClassification.from_pretrained(__UpperCamelCase       ,			config=__UpperCamelCase     )
						snake_case_ :			int            =      downstream_dict["""projector.weight"""]
						snake_case_ :			Optional[int]            =      downstream_dict["""projector.bias"""]
						snake_case_ :			List[Any]            =      downstream_dict["""model.post_net.linear.weight"""]
						snake_case_ :			Union[str, Any]            =      downstream_dict["""model.post_net.linear.bias"""]
						return model
def 						__lowerCAmelCase    (		__UpperCamelCase   :      int       ,			__UpperCamelCase   :      Tuple       ,			__UpperCamelCase   :      str     ):


						'''simple docstring'''






						snake_case_ :			int            =      WavaVecaForAudioFrameClassification.from_pretrained(__UpperCamelCase       ,			config=__UpperCamelCase     )
						snake_case_ :			Any            =      downstream_dict["""model.linear.weight"""]
						snake_case_ :			int            =      downstream_dict["""model.linear.bias"""]
						return model
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Tuple       ,			__UpperCamelCase   :      List[str]       ,			__UpperCamelCase   :      List[Any]     ):


						'''simple docstring'''






						snake_case_ :			Optional[int]            =      WavaVecaForXVector.from_pretrained(__UpperCamelCase       ,			config=__UpperCamelCase     )
						snake_case_ :			Any            =      downstream_dict["""connector.weight"""]
						snake_case_ :			str            =      downstream_dict["""connector.bias"""]
						for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
												snake_case_ :			Dict            =      downstream_dict[
												    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
												]
												snake_case_ :			int            =      downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

						snake_case_ :			str            =      downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
						snake_case_ :			int            =      downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
						snake_case_ :			Any            =      downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
						snake_case_ :			int            =      downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
						snake_case_ :			List[str]            =      downstream_dict["""objective.W"""]
						return model
@torch.no_grad()
def 						__lowerCAmelCase    (		__UpperCamelCase   :      List[Any]       ,			__UpperCamelCase   :      Any       ,			__UpperCamelCase   :      List[str]       ,			__UpperCamelCase   :      Tuple     ):


						'''simple docstring'''






						snake_case_ :			Any            =      torch.load(__UpperCamelCase       ,			map_location="""cpu"""     )

						snake_case_ :			Any            =      checkpoint["""Downstream"""]

						snake_case_ :			Optional[Any]            =      WavaVecaConfig.from_pretrained(__UpperCamelCase     )
						snake_case_ :			Optional[int]            =      WavaVecaFeatureExtractor.from_pretrained(
						    __UpperCamelCase       ,			return_attention_mask=__UpperCamelCase       ,			do_normalize=__UpperCamelCase     )

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

						if hf_config.use_weighted_layer_sum:
												snake_case_ :			List[Any]            =      checkpoint["""Featurizer"""]["""weights"""]

						hf_feature_extractor.save_pretrained(__UpperCamelCase     )
						hf_model.save_pretrained(__UpperCamelCase     )


if __name__ == "__main__":
					__lowerCAmelCase							:       Optional[int]          =       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.''')
					__lowerCAmelCase							:       Dict          =       parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




'''simple docstring'''





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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCamelCase_    						=       logging.getLogger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						return (preds == labels).mean()





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,							)





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()      )}      )
				lowerCamelCase_		       =    field(metadata={'help': 'Should contain the data files for the task.'}      )
				lowerCamelCase_		       =    field(
				    default=1_28						,							metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    }						,							)
				lowerCamelCase_		       =    field(
				    default=lowercase__						,							metadata={'help': 'Overwrite the cached training and evaluation sets'}      )






def 				_lowerCAmelCase			(   )				->  int:
						# 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, TrainingArguments)						)
						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 if training_args.local_rank in [-1, 0] else logging.WARN   , )
						logger.warning(
						    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''   , training_args.local_rank   , training_args.device   , training_args.n_gpu   , bool(training_args.local_rank != -1						)   , training_args.fpaa   , )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank						):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('''Training/evaluation parameters %s'''   , __magic_name__						)

						# Set seed
						set_seed(training_args.seed						)

						try:
												lowercase :							Any    		=processors[data_args.task_name]()
												lowercase :							Optional[int]    		=processor.get_labels()
												lowercase :							str    		=len(__magic_name__						)
						except KeyError:
												raise ValueError('''Task not found: %s''' % (data_args.task_name)						)

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

						lowercase :							List[str]    		=AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path   , num_labels=__magic_name__   , finetuning_task=data_args.task_name   , cache_dir=model_args.cache_dir   , )
						lowercase :							int    		=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 :							Any    		=AutoModelForMultipleChoice.from_pretrained(
						    model_args.model_name_or_path   , from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)   , config=__magic_name__   , cache_dir=model_args.cache_dir   , )

						# Get datasets
						lowercase :							int    		=(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir   , tokenizer=__magic_name__   , task=data_args.task_name   , max_seq_length=data_args.max_seq_length   , overwrite_cache=data_args.overwrite_cache   , mode=Split.train   , )
						    if training_args.do_train
						    else None
						)
						lowercase :							Union[str, Any]    		=(
						    MultipleChoiceDataset(
						        data_dir=data_args.data_dir   , tokenizer=__magic_name__   , task=data_args.task_name   , max_seq_length=data_args.max_seq_length   , overwrite_cache=data_args.overwrite_cache   , mode=Split.dev   , )
						    if training_args.do_eval
						    else None
						)

						def compute_metrics(__magic_name__						:     EvalPrediction						) -> Dict:
												lowercase :							Dict    		=np.argmax(p.predictions   , axis=1						)
												return {"acc": simple_accuracy(__magic_name__   , p.label_ids						)}

						# Data collator
						lowercase :							List[str]    		=DataCollatorWithPadding(__magic_name__   , pad_to_multiple_of=8						) if training_args.fpaa else None

						# Initialize our Trainer
						lowercase :							Dict    		=Trainer(
						    model=__magic_name__   , args=__magic_name__   , train_dataset=__magic_name__   , eval_dataset=__magic_name__   , compute_metrics=__magic_name__   , data_collator=__magic_name__   , )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
						lowercase :							Optional[Any]    		={}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''						)

												lowercase :							List[Any]    		=trainer.evaluate()

												lowercase :							Any    		=os.path.join(training_args.output_dir   , '''eval_results.txt'''						)
												if trainer.is_world_master():
																		with open(__magic_name__   , '''w'''						) as writer:
																								logger.info('''***** Eval results *****'''						)
																								for key, value in result.items():
																														logger.info('''  %s = %s'''   , __magic_name__   , __magic_name__						)
																														writer.write('''%s = %s\n''' % (key, value)						)

																								results.update(__magic_name__						)

						return results






def 				_lowerCAmelCase			(   __magic_name__						:     Any						)				->  Optional[Any]:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()





