






'''simple docstring'''







import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


UpperCamelCase_					=				logging.get_logger(__name__)

UpperCamelCase_					=				[
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]




class 					__UpperCAmelCase (		__snake_case	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    int				       =					RetriBertConfig
      SCREAMING_SNAKE_CASE				:    List[Any]				       =					None
      SCREAMING_SNAKE_CASE				:    Tuple				       =					"retribert"




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             if isinstance(lowerCamelCase_  ,		nn.Linear						):
                    module.weight.data.normal_(mean=0.0  ,		std=self.config.initializer_range						)
                    if module.bias is not None:
                           module.bias.data.zero_()
             elif isinstance(lowerCamelCase_  ,		nn.Embedding						):
                    module.weight.data.normal_(mean=0.0  ,		std=self.config.initializer_range						)
                    if module.padding_idx is not None:
                           module.weight.data[module.padding_idx].zero_()
             elif isinstance(lowerCamelCase_  ,		nn.LayerNorm						):
                    module.bias.data.zero_()
                    module.weight.data.fill_(1.0						)


UpperCamelCase_					=				R"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"




@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."      ,				__snake_case      ,				)
class 					__UpperCAmelCase (		__snake_case	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase						):
             super().__init__(lowerCamelCase_						)
             UpperCAmelCase__	:   Union[str, Any]												=	config.projection_dim

             UpperCAmelCase__	:   Union[str, Any]												=	BertModel(lowerCamelCase_						)
             UpperCAmelCase__	:   str												=	None if config.share_encoders else BertModel(lowerCamelCase_						)
             UpperCAmelCase__	:   Dict												=	nn.Dropout(config.hidden_dropout_prob						)
             UpperCAmelCase__	:   Optional[int]												=	nn.Linear(config.hidden_size  ,		config.projection_dim  ,		bias=lowerCamelCase_						)
             UpperCAmelCase__	:   Union[str, Any]												=	nn.Linear(config.hidden_size  ,		config.projection_dim  ,		bias=lowerCamelCase_						)

             UpperCAmelCase__	:   Any												=	nn.CrossEntropyLoss(reduction='''mean'''						)

             # Initialize weights and apply final processing
             self.post_init()




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase=-1  ,		):
             # reproduces BERT forward pass with checkpointing
             if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
                    return sent_encoder(lowerCamelCase_  ,		attention_mask=lowerCamelCase_						)[1]
             else:
                    # prepare implicit variables
                    UpperCAmelCase__	:   str												=	input_ids.device
                    UpperCAmelCase__	:   int												=	input_ids.size()
                    UpperCAmelCase__	:   List[Any]												=	torch.zeros(lowerCamelCase_  ,		dtype=torch.long  ,		device=lowerCamelCase_						)
                    UpperCAmelCase__	:   int												=	[None] * sent_encoder.config.num_hidden_layers
                    UpperCAmelCase__	:   torch.Tensor												=	sent_encoder.get_extended_attention_mask(
                        lowerCamelCase_  ,		lowerCamelCase_						)

                    # define function for checkpointing
                    def partial_encode(*_UpperCAmelCase						):
                           UpperCAmelCase__	:   Tuple												=	sent_encoder.encoder(
                               inputs[0]  ,		attention_mask=inputs[1]  ,		head_mask=lowerCamelCase_  ,		)
                           UpperCAmelCase__	:   Dict												=	encoder_outputs[0]
                           UpperCAmelCase__	:   Any												=	sent_encoder.pooler(lowerCamelCase_						)
                           return pooled_output

                    # run embedding layer on everything at once
                    UpperCAmelCase__	:   Any												=	sent_encoder.embeddings(
                        input_ids=lowerCamelCase_  ,		position_ids=lowerCamelCase_  ,		token_type_ids=lowerCamelCase_  ,		inputs_embeds=lowerCamelCase_						)
                    # run encoding and pooling on one mini-batch at a time
                    UpperCAmelCase__	:   Optional[int]												=	[]
                    for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size						)						):
                           UpperCAmelCase__	:   Union[str, Any]												=	embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                           UpperCAmelCase__	:   Optional[Any]												=	extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                           UpperCAmelCase__	:   Optional[Any]												=	checkpoint.checkpoint(lowerCamelCase_  ,		lowerCamelCase_  ,		lowerCamelCase_						)
                           pooled_output_list.append(lowerCamelCase_						)
                    return torch.cat(lowerCamelCase_  ,		dim=0						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=-1  ,		):
             UpperCAmelCase__	:   Union[str, Any]												=	self.embed_sentences_checkpointed(
                 lowerCamelCase_  ,		lowerCamelCase_  ,		self.bert_query  ,		lowerCamelCase_  ,		)
             return self.project_query(lowerCamelCase_						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=-1  ,		):
             UpperCAmelCase__	:   Optional[Any]												=	self.embed_sentences_checkpointed(
                 lowerCamelCase_  ,		lowerCamelCase_  ,		self.bert_query if self.bert_doc is None else self.bert_doc  ,		lowerCamelCase_  ,		)
             return self.project_doc(lowerCamelCase_						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = -1  ,		):
             UpperCAmelCase__	:   Union[str, Any]												=	input_ids_query.device
             UpperCAmelCase__	:   List[str]												=	self.embed_questions(lowerCamelCase_  ,		lowerCamelCase_  ,		lowerCamelCase_						)
             UpperCAmelCase__	:   str												=	self.embed_answers(lowerCamelCase_  ,		lowerCamelCase_  ,		lowerCamelCase_						)
             UpperCAmelCase__	:   List[Any]												=	torch.mm(lowerCamelCase_  ,		a_reps.t()						)
             UpperCAmelCase__	:   Any												=	self.ce_loss(lowerCamelCase_  ,		torch.arange(compare_scores.shape[1]						).to(lowerCamelCase_						)						)
             UpperCAmelCase__	:   Optional[int]												=	self.ce_loss(compare_scores.t()  ,		torch.arange(compare_scores.shape[0]						).to(lowerCamelCase_						)						)
             UpperCAmelCase__	:   Optional[int]												=	(loss_qa + loss_aq) / 2
             return loss




import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
      from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
      UpperCamelCase_									=   True
      from torch.cuda.amp import autocast


UpperCamelCase_									=   logging.getLogger(__name__)







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : str       =						field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
   A__      : Optional[str]       =						field(
       default=__snake_case ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,							)
   A__      : Optional[bool]       =						field(
       default=__snake_case ,							metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
   A__      : Optional[bool]       =						field(
       default=__snake_case ,							metadata={"help": "Whether to log verbose messages or not."} ,							)







def       lowerCamelCase_  (    _a       :						ModelArguments   ,		_a       :						TrainingArguments    ):



    '''simple docstring'''




    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,		datefmt="""%m/%d/%Y %H:%M:%S"""   ,		handlers=[logging.StreamHandler(sys.stdout    )]   ,		)
    UpperCAmelCase_     :       List[Any]  		=					logging.WARNING
    if model_args.verbose_logging:
        UpperCAmelCase_     :       int  		=					logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank    ):
        UpperCAmelCase_     :       Union[str, Any]  		=					logging.INFO
    logger.setLevel(_a    )







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : str       =						field(
       default=__snake_case ,							metadata={"help": "The name of the dataset to use (via the datasets library)."} )
   A__      : Optional[str]       =						field(
       default=__snake_case ,							metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
   A__      : Optional[str]       =						field(
       default="train" ,							metadata={
           "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
       } ,							)
   A__      : Optional[str]       =						field(
       default="validation" ,							metadata={
           "help": (
               "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
           )
       } ,							)
   A__      : Optional[str]       =						field(
       default="text" ,							metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"} ,							)
   A__      : Optional[str]       =						field(
       default="file" ,							metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,							)
   A__      : Optional[bool]       =						field(
       default=__snake_case ,							metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."} ,							)
   A__      : Optional[float]       =						field(
       default=__snake_case ,							metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."} ,							)
   A__      : Optional[str]       =						field(
       default="librispeech" ,							metadata={
           "help": (
               "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
               " 'buckwalter'."
           )
       } ,							)
   A__      : bool       =						field(
       default=__snake_case ,							metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
   A__      : Optional[int]       =						field(
       default=__snake_case ,							metadata={"help": "The number of processes to use for the preprocessing."} ,							)







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : bool       =						False
   A__      : Optional[str]       =						None
   A__      : Optional[str]       =						"|"
   A__      : Optional[Dict[str, str]]       =						field(default_factory=__snake_case )
   A__      : Optional[Set[str]]       =						field(default_factory=__snake_case )
   A__      : Optional[Callable[[str], str]]       =						None


   @classmethod
   def       A__    (    cls:     List[Any]  ,lowerCamelCase_:     str							)	->      Union[str, Any]:
       if name == "librispeech":
           return cls()
       if name == "timit":
           return cls(
               do_lower_case=lowerCamelCase_  ,translation_table=str.maketrans({"""-""": """ """}							)  ,)
       if name == "buckwalter":
           UpperCAmelCase_     :       Dict  		=					{
               """-""": """ """,  # sometimes used to represent pauses
               """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
           }
           return cls(
               vocab_file=pathlib.Path(lowerCamelCase_							).parent.joinpath("""vocab/buckwalter.json"""							)  ,word_delimiter_token="""/"""  ,translation_table=str.maketrans(lowerCamelCase_							)  ,words_to_remove={"""sil"""}  ,untransliterator=arabic.buckwalter.untransliterate  ,)
       raise ValueError(F'''Unsupported orthography: \'{name}\'.'''							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     str							)	->      str:
       # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
       if len(self.translation_table							) > 0:
           UpperCAmelCase_     :       Optional[int]  		=					text.translate(self.translation_table							)
       if len(self.words_to_remove							) == 0:
           UpperCAmelCase_     :       List[Any]  		=					""" """.join(text.split()							)  # clean up whitespaces
       else:
           UpperCAmelCase_     :       List[str]  		=					""" """.join(w for w in text.split() if w not in self.words_to_remove							)  # and clean up whilespaces
       return text






   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     ModelArguments							)	->      WavaVecaProcessor:
       UpperCAmelCase_     :       List[Any]  		=					WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path  ,cache_dir=model_args.cache_dir							)
       if self.vocab_file:
           UpperCAmelCase_     :       Tuple  		=					WavaVecaCTCTokenizer(
               self.vocab_file  ,cache_dir=model_args.cache_dir  ,do_lower_case=self.do_lower_case  ,word_delimiter_token=self.word_delimiter_token  ,)
       else:
           UpperCAmelCase_     :       Tuple  		=					WavaVecaCTCTokenizer.from_pretrained(
               model_args.model_name_or_path  ,cache_dir=model_args.cache_dir  ,do_lower_case=self.do_lower_case  ,word_delimiter_token=self.word_delimiter_token  ,)
       return WavaVecaProcessor(lowerCamelCase_  ,lowerCamelCase_							)







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : WavaVecaProcessor
   A__      : Union[bool, str]       =						True
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None


   def __call__(    self:     Tuple  ,lowerCamelCase_:     List[Dict[str, Union[List[int], torch.Tensor]]]							)	->      Dict[str, torch.Tensor]:
       # split inputs and labels since they have to be of different lenghts and need
       # different padding methods
       UpperCAmelCase_     :       Union[str, Any]  		=					[{"""input_values""": feature["""input_values"""]} for feature in features]
       UpperCAmelCase_     :       List[Any]  		=					[{"""input_ids""": feature["""labels"""]} for feature in features]

       UpperCAmelCase_     :       List[str]  		=					self.processor.pad(
           lowerCamelCase_  ,padding=self.padding  ,max_length=self.max_length  ,pad_to_multiple_of=self.pad_to_multiple_of  ,return_tensors="""pt"""  ,)
       UpperCAmelCase_     :       Optional[Any]  		=					self.processor.pad(
           labels=lowerCamelCase_  ,padding=self.padding  ,max_length=self.max_length_labels  ,pad_to_multiple_of=self.pad_to_multiple_of_labels  ,return_tensors="""pt"""  ,)

       # replace padding with -100 to ignore loss correctly
       UpperCAmelCase_     :       Any  		=					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1							)  ,-100							)

       UpperCAmelCase_     :       List[str]  		=					labels

       return batch







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def       A__    (    self:     Any  ,lowerCamelCase_:     nn.Module  ,lowerCamelCase_:     Dict[str, Union[torch.Tensor, Any]]							)	->      torch.Tensor:
       model.train()
       UpperCAmelCase_     :       Optional[int]  		=					self._prepare_inputs(lowerCamelCase_							)

       if self.use_amp:
           with autocast():
               UpperCAmelCase_     :       int  		=					self.compute_loss(lowerCamelCase_  ,lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Tuple  		=					self.compute_loss(lowerCamelCase_  ,lowerCamelCase_							)

       if self.args.n_gpu > 1:
           if model.module.config.ctc_loss_reduction == "mean":
               UpperCAmelCase_     :       List[str]  		=					loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
               UpperCAmelCase_     :       List[str]  		=					loss.sum() / (inputs["""labels"""] >= 0).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:
           UpperCAmelCase_     :       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()

       return loss.detach()







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					parser.parse_args_into_dataclasses()
    configure_logger(_a   ,		_a    )

    UpperCAmelCase_     :       Tuple  		=					Orthography.from_name(data_args.orthography.lower()    )
    UpperCAmelCase_     :       int  		=					orthography.create_processor(_a    )
    UpperCAmelCase_     :       Dict  		=					WavaVecaForCTC.from_pretrained(
        model_args.model_name_or_path   ,		cache_dir=model_args.cache_dir   ,		gradient_checkpointing=training_args.gradient_checkpointing   ,		vocab_size=len(processor.tokenizer    )   ,		)

    UpperCAmelCase_     :       Dict  		=					datasets.load_dataset(
        data_args.dataset_name   ,		data_args.dataset_config_name   ,		split=data_args.train_split_name    )
    UpperCAmelCase_     :       int  		=					datasets.load_dataset(
        data_args.dataset_name   ,		data_args.dataset_config_name   ,		split=data_args.validation_split_name    )

    UpperCAmelCase_     :       Optional[Any]  		=					datasets.load_metric("""wer"""    )
    UpperCAmelCase_     :       str  		=					processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
    UpperCAmelCase_     :       Optional[Any]  		=					"""""".join(t for t in processor.tokenizer.get_vocab().keys() if len(_a    ) == 1    )
    UpperCAmelCase_     :       Optional[Any]  		=					re.compile(  # remove characters not in vocabulary
        rF'''[^\s{re.escape(_a    )}]'''   ,		flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0   ,		)
    UpperCAmelCase_     :       int  		=					[]

    def prepare_example(_a       :						Dict    ):  # TODO(elgeish) make use of multiprocessing?
        UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					librosa.load(example[data_args.speech_file_column]   ,		sr=_a    )
        if data_args.max_duration_in_seconds is not None:
            UpperCAmelCase_     :       Dict  		=					len(example["""speech"""]    ) / example["""sampling_rate"""]
        # Normalize and clean up text; order matters!
        UpperCAmelCase_     :       Optional[int]  		=					orthography.preprocess_for_training(example[data_args.target_text_column]    )
        UpperCAmelCase_     :       int  		=					vocabulary_text_cleaner.sub(""""""   ,		_a    )
        if updated_text != example[data_args.target_text_column]:
            text_updates.append((example[data_args.target_text_column], updated_text)    )
            UpperCAmelCase_     :       int  		=					updated_text
        return example

    UpperCAmelCase_     :       Any  		=					train_dataset.map(_a   ,		remove_columns=[data_args.speech_file_column]    )
    UpperCAmelCase_     :       Optional[Any]  		=					val_dataset.map(_a   ,		remove_columns=[data_args.speech_file_column]    )

    if data_args.max_duration_in_seconds is not None:

        def filter_by_max_duration(_a       :						List[Any]    ):
            return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

        UpperCAmelCase_     :       Union[str, Any]  		=					len(_a    )
        UpperCAmelCase_     :       int  		=					len(_a    )
        UpperCAmelCase_     :       Optional[Any]  		=					train_dataset.filter(_a   ,		remove_columns=["""duration_in_seconds"""]    )
        UpperCAmelCase_     :       Tuple  		=					val_dataset.filter(_a   ,		remove_columns=["""duration_in_seconds"""]    )
        if len(_a    ) > old_train_size:
            logger.warning(
                F'''Filtered out {len(_a    ) - old_train_size} train example(s) longer than'''
                F''' {data_args.max_duration_in_seconds} second(s).'''    )
        if len(_a    ) > old_val_size:
            logger.warning(
                F'''Filtered out {len(_a    ) - old_val_size} validation example(s) longer than'''
                F''' {data_args.max_duration_in_seconds} second(s).'''    )
    logger.info(F'''Split sizes: {len(_a    )} train and {len(_a    )} validation.'''    )

    logger.warning(F'''Updated {len(_a    )} transcript(s) using \'{data_args.orthography}\' orthography rules.'''    )
    if logger.isEnabledFor(logging.DEBUG    ):
        for original_text, updated_text in text_updates:
            logger.debug(F'''Updated text: "{original_text}" -> "{updated_text}"'''    )
    UpperCAmelCase_     :       Dict  		=					None

    def prepare_dataset(_a       :						Optional[int]    ):
        # check that all files have the correct sampling rate
        assert (
            len(set(batch["""sampling_rate"""]    )    ) == 1
        ), F'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

        UpperCAmelCase_     :       List[str]  		=					processor(
            audio=batch["""speech"""]   ,		text=batch[data_args.target_text_column]   ,		sampling_rate=batch["""sampling_rate"""][0]    )
        batch.update(_a    )
        return batch

    UpperCAmelCase_     :       int  		=					train_dataset.map(
        _a   ,		batch_size=training_args.per_device_train_batch_size   ,		batched=_a   ,		num_proc=data_args.preprocessing_num_workers   ,		)
    UpperCAmelCase_     :       Optional[Any]  		=					val_dataset.map(
        _a   ,		batch_size=training_args.per_device_train_batch_size   ,		batched=_a   ,		num_proc=data_args.preprocessing_num_workers   ,		)

    UpperCAmelCase_     :       Optional[int]  		=					DataCollatorCTCWithPadding(processor=_a   ,		padding=_a    )

    def compute_metrics(_a       :						int    ):
        UpperCAmelCase_     :       List[Any]  		=					pred.predictions
        UpperCAmelCase_     :       Optional[int]  		=					np.argmax(_a   ,		axis=-1    )

        UpperCAmelCase_     :       List[str]  		=					processor.tokenizer.pad_token_id

        UpperCAmelCase_     :       str  		=					processor.batch_decode(_a    )
        # we do not want to group tokens when computing the metrics
        UpperCAmelCase_     :       List[Any]  		=					processor.batch_decode(pred.label_ids   ,		group_tokens=_a    )
        if logger.isEnabledFor(logging.DEBUG    ):
            for reference, predicted in zip(_a   ,		_a    ):
                logger.debug(F'''reference: "{reference}"'''    )
                logger.debug(F'''predicted: "{predicted}"'''    )
                if orthography.untransliterator is not None:
                    logger.debug(F'''reference (untransliterated): "{orthography.untransliterator(_a    )}"'''    )
                    logger.debug(F'''predicted (untransliterated): "{orthography.untransliterator(_a    )}"'''    )

        UpperCAmelCase_     :       Dict  		=					wer_metric.compute(predictions=_a   ,		references=_a    )

        return {"wer": wer}

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    UpperCAmelCase_     :       Union[str, Any]  		=					CTCTrainer(
        model=_a   ,		data_collator=_a   ,		args=_a   ,		compute_metrics=_a   ,		train_dataset=_a   ,		eval_dataset=_a   ,		tokenizer=processor.feature_extractor   ,		)

    trainer.train()


if __name__ == "__main__":
      main()
