
'''simple docstring'''





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			(DPMSolverMultistepScheduler,)
  _UpperCamelCase							=			(('num_inference_steps', 25),)

  def 		_lowerCamelCase						(self	:			Any						,			**lowerCAmelCase_	:			Union[str, Any]     ) ->							str:
        snake_case_    							=				{
            "num_train_timesteps": 10_00,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "solver_order": 2,
            "prediction_type": "epsilon",
            "thresholding": False,
            "sample_max_value": 1.0,
            "algorithm_type": "dpmsolver++",
            "solver_type": "midpoint",
            "lower_order_final": False,
            "lambda_min_clipped": -float("inf"     ),
            "variance_type": None,
        }

        config.update(**lowerCAmelCase_     )
        return config

  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			Union[str, Any]=0						,			**lowerCAmelCase_	:			List[str]     ) ->							str:
        snake_case_    							=				dict(self.forward_default_kwargs     )
        snake_case_    							=				kwargs.pop("num_inference_steps"						,			lowerCAmelCase_     )
        snake_case_    							=				self.dummy_sample
        snake_case_    							=				0.1 * sample
        snake_case_    							=				[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
              snake_case_    							=				self.get_scheduler_config(**lowerCAmelCase_     )
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )
              scheduler.set_timesteps(lowerCAmelCase_     )
              # copy over dummy past residuals
              snake_case_    							=				dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase_     )
                    snake_case_    							=				scheduler_class.from_pretrained(lowerCAmelCase_     )
                    new_scheduler.set_timesteps(lowerCAmelCase_     )
                    # copy over dummy past residuals
                    snake_case_    							=				dummy_past_residuals[: new_scheduler.config.solver_order]

              snake_case_							,						snake_case_    							=				sample, sample
              for t in range(lowerCAmelCase_						,			time_step + scheduler.config.solver_order + 1     ):
                    snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample
                    snake_case_    							=				new_scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample

                    assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							List[str]:
        pass

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			Union[str, Any]=0						,			**lowerCAmelCase_	:			Dict     ) ->							Optional[Any]:
        snake_case_    							=				dict(self.forward_default_kwargs     )
        snake_case_    							=				kwargs.pop("num_inference_steps"						,			lowerCAmelCase_     )
        snake_case_    							=				self.dummy_sample
        snake_case_    							=				0.1 * sample
        snake_case_    							=				[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
              snake_case_    							=				self.get_scheduler_config()
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )
              scheduler.set_timesteps(lowerCAmelCase_     )

              # copy over dummy past residuals (must be after setting timesteps)
              snake_case_    							=				dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase_     )
                    snake_case_    							=				scheduler_class.from_pretrained(lowerCAmelCase_     )
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(lowerCAmelCase_     )

                    # copy over dummy past residual (must be after setting timesteps)
                    snake_case_    							=				dummy_past_residuals[: new_scheduler.config.solver_order]

              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample
              snake_case_    							=				new_scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample

              assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			int=None						,			**lowerCAmelCase_	:			Optional[Any]     ) ->							Dict:
        if scheduler is None:
              snake_case_    							=				self.scheduler_classes[0]
              snake_case_    							=				self.get_scheduler_config(**lowerCAmelCase_     )
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

        snake_case_    							=				10
        snake_case_    							=				self.dummy_model()
        snake_case_    							=				self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase_     )

        for i, t in enumerate(scheduler.timesteps     ):
              snake_case_    							=				model(lowerCAmelCase_						,			lowerCAmelCase_     )
              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     ).prev_sample

        return sample

  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[str]:
        snake_case_    							=				dict(self.forward_default_kwargs     )

        snake_case_    							=				kwargs.pop("num_inference_steps"						,			lowerCAmelCase_     )

        for scheduler_class in self.scheduler_classes:
              snake_case_    							=				self.get_scheduler_config()
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

              snake_case_    							=				self.dummy_sample
              snake_case_    							=				0.1 * sample

              if num_inference_steps is not None and hasattr(lowerCAmelCase_						,			"set_timesteps"     ):
                    scheduler.set_timesteps(lowerCAmelCase_     )
              elif num_inference_steps is not None and not hasattr(lowerCAmelCase_						,			"set_timesteps"     ):
                    snake_case_    							=				num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              snake_case_    							=				[residual + 0.2, residual + 0.15, residual + 0.10]
              snake_case_    							=				dummy_past_residuals[: scheduler.config.solver_order]

              snake_case_    							=				scheduler.timesteps[5]
              snake_case_    							=				scheduler.timesteps[6]

              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample
              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample

              self.assertEqual(output_a.shape						,			sample.shape     )
              self.assertEqual(output_a.shape						,			output_a.shape     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[Any]:
        for timesteps in [25, 50, 1_00, 9_99, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Union[str, Any]:
        self.check_over_configs(thresholding=lowerCAmelCase_     )
        for order in [1, 2, 3]:
              for solver_type in ["midpoint", "heun"]:
                    for threshold in [0.5, 1.0, 2.0]:
                          for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=lowerCAmelCase_						,			prediction_type=lowerCAmelCase_						,			sample_max_value=lowerCAmelCase_						,			algorithm_type="dpmsolver++"						,			solver_order=lowerCAmelCase_						,			solver_type=lowerCAmelCase_						,			)

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Tuple:
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Dict:
        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                    for order in [1, 2, 3]:
                          for prediction_type in ["epsilon", "sample"]:
                                if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                      if order == 3:
                                            continue
                                else:
                                      self.check_over_configs(
                                          solver_order=lowerCAmelCase_						,			solver_type=lowerCAmelCase_						,			prediction_type=lowerCAmelCase_						,			algorithm_type=lowerCAmelCase_						,			)
                                snake_case_    							=				self.full_loop(
                                    solver_order=lowerCAmelCase_						,			solver_type=lowerCAmelCase_						,			prediction_type=lowerCAmelCase_						,			algorithm_type=lowerCAmelCase_						,			)
                                assert not torch.isnan(lowerCAmelCase_     ).any(), "Samples have nan numbers"

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Optional[Any]:
        self.check_over_configs(lower_order_final=lowerCAmelCase_     )
        self.check_over_configs(lower_order_final=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        self.check_over_configs(lambda_min_clipped=-float("inf"     )     )
        self.check_over_configs(lambda_min_clipped=-5.1     )

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Union[str, Any]:
        self.check_over_configs(variance_type=lowerCAmelCase_     )
        self.check_over_configs(variance_type="learned_range"     )

  def 		_lowerCamelCase						(self	:			str     ) ->							List[Any]:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
              self.check_over_forward(num_inference_steps=lowerCAmelCase_						,			time_step=0     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Union[str, Any]:
        snake_case_    							=				self.full_loop()
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.3_301     ) < 1E-3

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							List[str]:
        snake_case_    							=				self.full_loop(thresholding=lowerCAmelCase_						,			dynamic_thresholding_ratio=0.87						,			sample_max_value=0.5     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 1.1_364     ) < 1E-3

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Union[str, Any]:
        snake_case_    							=				self.full_loop(prediction_type="v_prediction"     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.2_251     ) < 1E-3

  def 		_lowerCamelCase						(self	:			str     ) ->							List[Any]:
        snake_case_    							=				self.full_loop(prediction_type="v_prediction"						,			use_karras_sigmas=lowerCAmelCase_     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.2_096     ) < 1E-3

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Tuple:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        snake_case_    							=				DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
        snake_case_    							=				self.full_loop(scheduler=lowerCAmelCase_     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.3_301     ) < 1E-3

        snake_case_    							=				DPMSolverSinglestepScheduler.from_config(scheduler.config     )
        snake_case_    							=				UniPCMultistepScheduler.from_config(scheduler.config     )
        snake_case_    							=				DEISMultistepScheduler.from_config(scheduler.config     )
        snake_case_    							=				DPMSolverMultistepScheduler.from_config(scheduler.config     )

        snake_case_    							=				self.full_loop(scheduler=lowerCAmelCase_     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.3_301     ) < 1E-3

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Tuple:
        snake_case_    							=				self.scheduler_classes[0]
        snake_case_    							=				self.get_scheduler_config(thresholding=lowerCAmelCase_						,			dynamic_thresholding_ratio=0     )
        snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

        snake_case_    							=				10
        snake_case_    							=				self.dummy_model()
        snake_case_    							=				self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCAmelCase_     )

        for i, t in enumerate(scheduler.timesteps     ):
              snake_case_    							=				model(lowerCAmelCase_						,			lowerCAmelCase_     )
              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     ).prev_sample

        assert sample.dtype == torch.floataa


  def 		_lowerCamelCase						(self	:			Any						,			**lowerCAmelCase_	:			Union[str, Any]     ) ->							Dict:
        for scheduler_class in self.scheduler_classes:
              snake_case_    							=				self.get_scheduler_config(**lowerCAmelCase_     )
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

              scheduler.set_timesteps(scheduler.config.num_train_timesteps     )
              assert len(scheduler.timesteps.unique()     ) == scheduler.num_inference_steps






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()
