


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
 from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
 SCREAMING_SNAKE_CASE :   Dict							      =				True
 from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE :   int							      =				logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_				:			str									=    field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
       lowercase_				:			Optional[str]									=    field(
           default=_a,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},				)
       lowercase_				:			Optional[bool]									=    field(
           default=_a,				metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}     )
       lowercase_				:			Optional[bool]									=    field(
           default=_a,				metadata={"""help""": """Whether to log verbose messages or not."""},				)
       lowercase_				:			Optional[float]									=    field(
           default=2.0,				metadata={"""help""": """Maximum temperature for gumbel softmax."""}     )
       lowercase_				:			Optional[float]									=    field(
           default=0.5,				metadata={"""help""": """Minimum temperature for gumbel softmax."""}     )
       lowercase_				:			Optional[float]									=    field(
           default=0.999995,				metadata={"""help""": """Decay of gumbel temperature during training."""}     )



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Tuple:
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,				datefmt='%m/%d/%Y %H:%M:%S'							,				handlers=[logging.StreamHandler(sys.stdout				)]							,				)
    _lowercase :							Any   	=	logging.WARNING
    if model_args.verbose_logging:
        _lowercase :							Tuple   	=	logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank				):
        _lowercase :							List[str]   	=	logging.INFO
    logger.setLevel(lowerCamelCase_				)






@dataclass
class _lowerCamelCase:
       lowercase_				:			str									=    field(
           default=_a,				metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}     )
       lowercase_				:			Optional[str]									=    field(
           default=_a,				metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}     )
       lowercase_				:			Optional[str]									=    field(
           default="""train""",				metadata={
               """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
           },				)
       lowercase_				:			Optional[str]									=    field(
           default="""validation""",				metadata={
               """help""": (
                   """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
               )
           },				)
       lowercase_				:			Optional[str]									=    field(
           default="""file""",				metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""},				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}     )
       lowercase_				:			Optional[int]									=    field(
           default=1,				metadata={
               """help""": """The percentage of the train set used as validation set in case there's no validation split"""
           },				)
       lowercase_				:			Optional[int]									=    field(
           default=_a,				metadata={"""help""": """The number of processes to use for the preprocessing."""},				)
       lowercase_				:			Optional[float]									=    field(
           default=20.0,				metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}     )






@dataclass
class _lowerCamelCase:
       lowercase_				:			WavaVecaForPreTraining
       lowercase_				:			WavaVecaFeatureExtractor
       lowercase_				:			Union[bool, str]									=    "longest"
       lowercase_				:			Optional[int]									=    None
       lowercase_				:			Optional[int]									=    None
       def __call__(      self,   lowerCamelCase) ->     Dict[str, torch.Tensor]:




           """simple docstring"""

           _lowercase :							int   	=	self.feature_extractor.pad(
               lowerCamelCase,   max_length=self.max_length,   padding=self.padding,   pad_to_multiple_of=self.pad_to_multiple_of,   return_tensors='pt',   )
           _lowercase :							Any   	=	self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1])

           _lowercase :							Tuple   	=	batch['input_values'].shape[0]

           # make sure that no loss is computed on padded inputs
           if batch["attention_mask"] is not None:
               # compute real output lengths according to convolution formula
               _lowercase :							Tuple   	=	self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1)).to(
                   torch.long)

               _lowercase :							Dict   	=	torch.zeros(
                   (batch_size, mask_indices_seq_length),   dtype=torch.long,   device=batch['input_values'].device)

               # these two operations makes sure that all values
               # before the output lengths indices are attended to
               _lowercase :							Union[str, Any]   	=	1
               _lowercase :							Any   	=	attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()

           # sample randomly masked indices
           _lowercase :							List[Any]   	=	_compute_mask_indices(
               (batch_size, mask_indices_seq_length),   self.model.config.mask_time_prob,   self.model.config.mask_time_length,   attention_mask=lowerCamelCase,   min_masks=2,   )

           return batch






class _lowerCamelCase(		_a     ):
       def __init__(      self,   *lowerCamelCase,   lowerCamelCase=1,   lowerCamelCase=0,   lowerCamelCase=1.0,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           super().__init__(*lowerCamelCase,   **lowerCamelCase)
           _lowercase :							List[str]   	=	0
           _lowercase :							str   	=	max_gumbel_temp
           _lowercase :							Optional[Any]   	=	min_gumbel_temp
           _lowercase :							Dict   	=	gumbel_temp_decay




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase) ->     torch.Tensor:




           """simple docstring"""

           model.train()
           _lowercase :							Tuple   	=	self._prepare_inputs(lowerCamelCase)

           if self.use_amp:
               with autocast():
                   _lowercase :							Union[str, Any]   	=	self.compute_loss(lowerCamelCase,   lowerCamelCase)
           else:
               _lowercase :							int   	=	self.compute_loss(lowerCamelCase,   lowerCamelCase)

           if self.args.n_gpu > 1 or self.deepspeed:
               if model.module.config.ctc_loss_reduction == "mean":
                   _lowercase :							Optional[int]   	=	loss.mean()
               elif model.module.config.ctc_loss_reduction == "sum":
                   _lowercase :							Optional[Any]   	=	loss.sum() / (inputs['mask_time_indices']).sum()
               else:
                   raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''')

           if self.args.gradient_accumulation_steps > 1:
               _lowercase :							Union[str, Any]   	=	loss / self.args.gradient_accumulation_steps

           if self.use_amp:
               self.scaler.scale(lowerCamelCase).backward()
           elif self.use_apex:
               with amp.scale_loss(lowerCamelCase,   self.optimizer) as scaled_loss:
                   scaled_loss.backward()
           elif self.deepspeed:
               self.deepspeed.backward(lowerCamelCase)
           else:
               loss.backward()

           self.num_update_step += 1
           # make sure gumbel softmax temperature is decayed
           if self.args.n_gpu > 1 or self.deepspeed:
               model.module.set_gumbel_temperature(
                   max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,   self.min_gumbel_temp))
           else:
               model.set_gumbel_temperature(
                   max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,   self.min_gumbel_temp))

           return loss.detach()



def        UpperCamelCase_(   )					->		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 :							Any   	=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

    _lowercase	,       _lowercase	,       _lowercase :							Optional[int]   	=	parser.parse_args_into_dataclasses()
    configure_logger(lowerCamelCase_							,				lowerCamelCase_				)

    # Downloading and loading a dataset from the hub.
    _lowercase :							List[str]   	=	load_dataset(data_args.dataset_name							,				data_args.dataset_config_name							,				cache_dir=model_args.cache_dir				)

    if "validation" not in datasets.keys():
        # make sure only "validation" and "train" keys remain"
        _lowercase :							Dict   	=	DatasetDict()
        _lowercase :							Optional[int]   	=	load_dataset(
            data_args.dataset_name							,				data_args.dataset_config_name							,				split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''							,				cache_dir=model_args.cache_dir							,				)
        _lowercase :							str   	=	load_dataset(
            data_args.dataset_name							,				data_args.dataset_config_name							,				split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''							,				cache_dir=model_args.cache_dir							,				)
    else:
        # make sure only "validation" and "train" keys remain"
        _lowercase :							str   	=	DatasetDict()
        _lowercase :							Any   	=	load_dataset(
            data_args.dataset_name							,				data_args.dataset_config_name							,				split='validation'							,				cache_dir=model_args.cache_dir							,				)
        _lowercase :							Union[str, Any]   	=	load_dataset(
            data_args.dataset_name							,				data_args.dataset_config_name							,				split=F'''{data_args.train_split_name}'''							,				cache_dir=model_args.cache_dir							,				)

    # only normalized-inputs-training is supported
    _lowercase :							Optional[int]   	=	WavaVecaFeatureExtractor.from_pretrained(
        model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				do_normalize=lowerCamelCase_				)

    def prepare_dataset(lowerCamelCase_				):
        # check that all files have the correct sampling rate
        _lowercase	,       _lowercase :							str   	=	librosa.load(batch[data_args.speech_file_column]							,				sr=feature_extractor.sampling_rate				)
        return batch

    # load audio files into numpy arrays
    _lowercase :							Optional[Any]   	=	datasets.map(
        lowerCamelCase_							,				num_proc=data_args.preprocessing_num_workers							,				remove_columns=datasets['train'].column_names				)

    # filter audio files that are too long
    _lowercase :							Any   	=	vectorized_datasets.filter(
        lambda lowerCamelCase_				: len(data['speech']				) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate				)				)

    def normalize(lowerCamelCase_				):
        return feature_extractor(batch['speech']							,				sampling_rate=feature_extractor.sampling_rate				)

    # normalize and transform to `BatchFeatures`
    _lowercase :							List[str]   	=	vectorized_datasets.map(
        lowerCamelCase_							,				batched=lowerCamelCase_							,				num_proc=data_args.preprocessing_num_workers							,				load_from_cache_file=not data_args.overwrite_cache							,				remove_columns=vectorized_datasets['train'].column_names							,				)

    # pretraining is only supported for "newer" stable layer norm architecture
    # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
    _lowercase :							int   	=	WavaVecaConfig.from_pretrained(
        model_args.model_name_or_path							,				cache_dir=model_args.cache_dir							,				gradient_checkpointing=training_args.gradient_checkpointing							,				)

    if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
        raise ValueError(
            'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
            ' ``config.feat_extract_norm=\'layer\''				)

    _lowercase :							Any   	=	WavaVecaForPreTraining(lowerCamelCase_				)

    _lowercase :							List[str]   	=	DataCollatorForWavaVecaPretraining(model=lowerCamelCase_							,				feature_extractor=lowerCamelCase_				)

    _lowercase :							Optional[int]   	=	WavaVecaPreTrainer(
        model=lowerCamelCase_							,				data_collator=lowerCamelCase_							,				args=lowerCamelCase_							,				train_dataset=vectorized_datasets['train']							,				eval_dataset=vectorized_datasets['validation']							,				tokenizer=lowerCamelCase_							,				max_gumbel_temp=model_args.max_gumbel_temperature							,				min_gumbel_temp=model_args.min_gumbel_temperature							,				gumbel_temp_decay=model_args.gumbel_temperature_decay							,				)
    trainer.train()


if __name__ == "__main__":
 main()






from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE :   Tuple							      =				"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE :   Any							      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return datasets.MetricInfo(
               description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
                   {
                       'predictions': datasets.Value('string',   id='sequence'),
                       'references': datasets.Value('string',   id='sequence'),
                   }),   codebase_urls=['https://github.com/jitsi/jiwer/'],   reference_urls=[
                   'https://en.wikipedia.org/wiki/Word_error_rate',
               ],   )




       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False) ->     Optional[Any]:




           """simple docstring"""

           if concatenate_texts:
               return compute_measures(lowerCamelCase,   lowerCamelCase)["wer"]
           else:
               _lowercase :							Dict   	=	0
               _lowercase :							Optional[int]   	=	0
               for prediction, reference in zip(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							List[Any]   	=	compute_measures(lowerCamelCase,   lowerCamelCase)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total


