


from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__UpperCamelCase							:							Union[str, Any]    							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class    __magic_name__						(  __snake_case):
     A:		torch.FloatTensor



class    __magic_name__						(  __snake_case       ,					__snake_case):
     A:		List[str]								=   1


     @register_to_config
     def __init__(   self				:  Optional[int]				,						lowerCamelCase__				:  int = 40				,						lowerCamelCase__				:  float = 0.002				,						lowerCamelCase__				:  float = 80.0				,						lowerCamelCase__				:  float = 0.5				,						lowerCamelCase__				:  float = 1.0				,						lowerCamelCase__				:  float = 7.0				,						lowerCamelCase__				:  bool = True				,						)	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= sigma_max

      UpperCamelCase__       :     int 							= np.linspace(0				,						1				,						lowerCamelCase_      )
      UpperCamelCase__       :     Optional[int] 							= self._convert_to_karras(lowerCamelCase_      )
      UpperCamelCase__       :     List[Any] 							= self.sigma_to_t(lowerCamelCase_      )

      # setable values
      UpperCamelCase__       :     Optional[Any] 							= None
      UpperCamelCase__       :     Any 							= torch.from_numpy(lowerCamelCase_      )
      UpperCamelCase__       :     Optional[Any] 							= torch.from_numpy(lowerCamelCase_      )
      UpperCamelCase__       :     Tuple 							= False
      UpperCamelCase__       :     Dict 							= False


     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  Tuple				,						lowerCamelCase__				:  List[str]=None      )	->   Optional[Any]:





      '''simple docstring'''






      if schedule_timesteps is None:
       UpperCamelCase__       :     str 							= self.timesteps

      UpperCamelCase__       :     str 							= (schedule_timesteps == timestep).nonzero()
      return indices.item()


     def   UpperCAmelCase__						(   self				:  List[str]				,						lowerCamelCase__				:  torch.FloatTensor				,						lowerCamelCase__				:  Union[float, torch.FloatTensor]      )	->   torch.FloatTensor:





      '''simple docstring'''






      if isinstance(lowerCamelCase_				,						torch.Tensor      ):
       UpperCamelCase__       :     Any 							= timestep.to(self.timesteps.device      )
      UpperCamelCase__       :     Optional[Any] 							= self.index_for_timestep(lowerCamelCase_      )
      UpperCamelCase__       :     Any 							= self.sigmas[step_idx]

      UpperCamelCase__       :     Tuple 							= sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

      UpperCamelCase__       :     Any 							= True
      return sample


     def   UpperCAmelCase__						(   self				:  List[Any]				,						lowerCamelCase__				:  Union[float, np.ndarray]      )	->   Tuple:





      '''simple docstring'''






      if not isinstance(lowerCamelCase_				,						np.ndarray      ):
       UpperCamelCase__       :     str 							= np.array(lowerCamelCase_				,						dtype=np.floataa      )

      UpperCamelCase__       :     Dict 							= 1000 * 0.25 * np.log(sigmas + 1E-4_4      )

      return timesteps


     def   UpperCAmelCase__						(   self				:  List[Any]				,						lowerCamelCase__				:  Optional[int] = None				,						lowerCamelCase__				:  Union[str, torch.device] = None				,						lowerCamelCase__				:  Optional[List[int]] = None				,						)	->   Optional[Any]:





      '''simple docstring'''






      if num_inference_steps is None and timesteps is None:
       raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''      )

      if num_inference_steps is not None and timesteps is not None:
       raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''      )

      # Follow DDPMScheduler custom timesteps logic
      if timesteps is not None:
       for i in range(1				,						len(lowerCamelCase_      )      ):
        if timesteps[i] >= timesteps[i - 1]:
         raise ValueError('''`timesteps` must be in descending order.'''      )

       if timesteps[0] >= self.config.num_train_timesteps:
        raise ValueError(
            F"`timesteps` must start before `self.config.train_timesteps`:"
            F" {self.config.num_train_timesteps}."      )

       UpperCamelCase__       :     List[Any] 							= np.array(lowerCamelCase_				,						dtype=np.intaa      )
       UpperCamelCase__       :     Tuple 							= True
      else:
       if num_inference_steps > self.config.num_train_timesteps:
        raise ValueError(
            F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
            F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
            F" maximal {self.config.num_train_timesteps} timesteps."      )

       UpperCamelCase__       :     Any 							= num_inference_steps

       UpperCamelCase__       :     Any 							= self.config.num_train_timesteps // self.num_inference_steps
       UpperCamelCase__       :     Optional[int] 							= (np.arange(0				,						lowerCamelCase_      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
       UpperCamelCase__       :     int 							= False

      # Map timesteps to Karras sigmas directly for multistep sampling
      # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
      UpperCamelCase__       :     List[str] 							= self.config.num_train_timesteps
      UpperCamelCase__       :     Any 							= timesteps[::-1].copy()
      UpperCamelCase__       :     int 							= ramp / (num_train_timesteps - 1)
      UpperCamelCase__       :     Tuple 							= self._convert_to_karras(lowerCamelCase_      )
      UpperCamelCase__       :     Dict 							= self.sigma_to_t(lowerCamelCase_      )

      UpperCamelCase__       :     List[str] 							= np.concatenate([sigmas, [self.sigma_min]]      ).astype(np.floataa      )
      UpperCamelCase__       :     Union[str, Any] 							= torch.from_numpy(lowerCamelCase_      ).to(device=lowerCamelCase_      )

      if str(lowerCamelCase_      ).startswith('''mps'''      ):
       # mps does not support float64
       UpperCamelCase__       :     Optional[Any] 							= torch.from_numpy(lowerCamelCase_      ).to(lowerCamelCase_				,						dtype=torch.floataa      )
      else:
       UpperCamelCase__       :     Optional[int] 							= torch.from_numpy(lowerCamelCase_      ).to(device=lowerCamelCase_      )


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Any      )	->   Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     float 							= self.config.sigma_min
      UpperCamelCase__       :     float 							= self.config.sigma_max

      UpperCamelCase__       :     Tuple 							= self.config.rho
      UpperCamelCase__       :     str 							= sigma_min ** (1 / rho)
      UpperCamelCase__       :     Optional[int] 							= sigma_max ** (1 / rho)
      UpperCamelCase__       :     str 							= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas


     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  int      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= self.config.sigma_data

      UpperCamelCase__       :     int 							= sigma_data**2 / (sigma**2 + sigma_data**2)
      UpperCamelCase__       :     str 							= sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out


     def   UpperCAmelCase__						(   self				:  Optional[Any]				,						lowerCamelCase__				:  List[Any]      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= self.config.sigma_min
      UpperCamelCase__       :     List[str] 							= self.config.sigma_data

      UpperCamelCase__       :     Union[str, Any] 							= sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
      UpperCamelCase__       :     Any 							= (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
      return c_skip, c_out


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  torch.FloatTensor				,						lowerCamelCase__				:  Union[float, torch.FloatTensor]				,						lowerCamelCase__				:  torch.FloatTensor				,						lowerCamelCase__				:  Optional[torch.Generator] = None				,						lowerCamelCase__				:  bool = True				,						)	->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:





      '''simple docstring'''






      if (
          isinstance(lowerCamelCase_				,						lowerCamelCase_      )
          or isinstance(lowerCamelCase_				,						torch.IntTensor      )
          or isinstance(lowerCamelCase_				,						torch.LongTensor      )
      ):
       raise ValueError(
           (
               '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
               F" `{self.__class__}.step()` is not supported. Make sure to pass"
               ''' one of the `scheduler.timesteps` as a timestep.'''
           )				,						)

      if not self.is_scale_input_called:
       logger.warning(
           '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
           '''See `StableDiffusionPipeline` for a usage example.'''      )

      if isinstance(lowerCamelCase_				,						torch.Tensor      ):
       UpperCamelCase__       :     List[str] 							= timestep.to(self.timesteps.device      )

      UpperCamelCase__       :     Optional[Any] 							= self.config.sigma_min
      UpperCamelCase__       :     List[str] 							= self.config.sigma_max

      UpperCamelCase__       :     int 							= self.index_for_timestep(lowerCamelCase_      )

      # sigma_next corresponds to next_t in original implementation
      UpperCamelCase__       :     Optional[Any] 							= self.sigmas[step_index]
      if step_index + 1 < self.config.num_train_timesteps:
       UpperCamelCase__       :     Dict 							= self.sigmas[step_index + 1]
      else:
       # Set sigma_next to sigma_min
       UpperCamelCase__       :     int 							= self.sigmas[-1]

      # Get scalings for boundary conditions
      UpperCamelCase__       :     Any 							= self.get_scalings_for_boundary_condition(lowerCamelCase_      )

      # 1. Denoise model output using boundary conditions
      UpperCamelCase__       :     Optional[Any] 							= c_out * model_output + c_skip * sample
      if self.config.clip_denoised:
       UpperCamelCase__       :     int 							= denoised.clamp(-1				,						1      )

      # 2. Sample z ~ N(0, s_noise^2 * I)
      # Noise is not used for onestep sampling.
      if len(self.timesteps      ) > 1:
       UpperCamelCase__       :     Optional[int] 							= randn_tensor(
           model_output.shape				,						dtype=model_output.dtype				,						device=model_output.device				,						generator=lowerCamelCase_      )
      else:
       UpperCamelCase__       :     Union[str, Any] 							= torch.zeros_like(lowerCamelCase_      )
      UpperCamelCase__       :     Any 							= noise * self.config.s_noise

      UpperCamelCase__       :     str 							= sigma_next.clamp(min=lowerCamelCase_				,						max=lowerCamelCase_      )

      # 3. Return noisy sample
      # tau = sigma_hat, eps = sigma_min
      UpperCamelCase__       :     Tuple 							= denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

      if not return_dict:
       return (prev_sample,)

      return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCamelCase_      )


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  torch.FloatTensor				,						lowerCamelCase__				:  torch.FloatTensor				,						lowerCamelCase__				:  torch.FloatTensor				,						)	->   torch.FloatTensor:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= self.sigmas.to(device=original_samples.device				,						dtype=original_samples.dtype      )
      if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_      ):
       # mps does not support float64
       UpperCamelCase__       :     List[str] 							= self.timesteps.to(original_samples.device				,						dtype=torch.floataa      )
       UpperCamelCase__       :     Dict 							= timesteps.to(original_samples.device				,						dtype=torch.floataa      )
      else:
       UpperCamelCase__       :     int 							= self.timesteps.to(original_samples.device      )
       UpperCamelCase__       :     Tuple 							= timesteps.to(original_samples.device      )

      UpperCamelCase__       :     Tuple 							= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

      UpperCamelCase__       :     Any 							= sigmas[step_indices].flatten()
      while len(sigma.shape      ) < len(original_samples.shape      ):
       UpperCamelCase__       :     Dict 							= sigma.unsqueeze(-1      )

      UpperCamelCase__       :     List[str] 							= original_samples + noise * sigma
      return noisy_samples


     def __len__(   self				:  Tuple      )	->   Optional[Any]:





      '''simple docstring'''






      return self.config.num_train_timesteps







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()
