
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


lowercase		:						Tuple =					logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						42






class    _snake_case  (						_lowercase ,				_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						1






							@register_to_config
							def __init__(					self						:			Union[str, Any]						, snake_case__						:			int = 4_0						, snake_case__						:			float = 0.002						, snake_case__						:			float = 80.0						, snake_case__						:			float = 0.5						, snake_case__						:			float = 1.0						, snake_case__						:			float = 7.0						, snake_case__						:			bool = True						, ):






												"""simple docstring"""



												__a:						str		    =					sigma_max

												__a:						Tuple		    =					np.linspace(0						, 1						, snake_case__    )
												__a:						Dict		    =					self._convert_to_karras(snake_case__    )
												__a:						Optional[Any]		    =					self.sigma_to_t(snake_case__    )

												# setable values
												__a:						Any		    =					None
												__a:						List[str]		    =					torch.from_numpy(snake_case__    )
												__a:						List[str]		    =					torch.from_numpy(snake_case__    )
												__a:						Union[str, Any]		    =					False
												__a:						Union[str, Any]		    =					False






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			int						, snake_case__						:			Union[str, Any]=None    ):






												"""simple docstring"""



												if schedule_timesteps is None:
																	__a:						Tuple		    =					self.timesteps

												__a:						List[Any]		    =					(schedule_timesteps == timestep).nonzero()
												return indices.item()






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			torch.FloatTensor						, snake_case__						:			Union[float, torch.FloatTensor]    ):






												"""simple docstring"""



												if isinstance(snake_case__						, torch.Tensor    ):
																	__a:						int		    =					timestep.to(self.timesteps.device    )
												__a:						List[Any]		    =					self.index_for_timestep(snake_case__    )
												__a:						List[str]		    =					self.sigmas[step_idx]

												__a:						int		    =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

												__a:						Union[str, Any]		    =					True
												return sample






							def 							__lowercase  (					self						:			int						, snake_case__						:			Union[float, np.ndarray]    ):






												"""simple docstring"""



												if not isinstance(snake_case__						, np.ndarray    ):
																	__a:						Dict		    =					np.array(snake_case__						, dtype=np.floataa    )

												__a:						Union[str, Any]		    =					1_0_0_0 * 0.25 * np.log(sigmas + 1e-44    )

												return timesteps






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[int] = None						, snake_case__						:			Union[str, torch.device] = None						, snake_case__						:			Optional[List[int]] = None						, ):






												"""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(snake_case__    )    ):
																						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}."""    )

																	__a:						Dict		    =					np.array(snake_case__						, dtype=np.intaa    )
																	__a:						List[str]		    =					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."""    )

																	__a:						Tuple		    =					num_inference_steps

																	__a:						int		    =					self.config.num_train_timesteps // self.num_inference_steps
																	__a:						Tuple		    =					(np.arange(0						, snake_case__    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
																	__a:						Union[str, Any]		    =					False

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

												__a:						str		    =					np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
												__a:						Tuple		    =					torch.from_numpy(snake_case__    ).to(device=snake_case__    )

												if str(snake_case__    ).startswith("""mps"""    ):
																	# mps does not support float64
																	__a:						int		    =					torch.from_numpy(snake_case__    ).to(snake_case__						, dtype=torch.floataa    )
												else:
																	__a:						int		    =					torch.from_numpy(snake_case__    ).to(device=snake_case__    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			Any    ):






												"""simple docstring"""



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

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






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			Dict    ):






												"""simple docstring"""



												__a:						Dict		    =					self.config.sigma_data

												__a:						List[str]		    =					sigma_data**2 / (sigma**2 + sigma_data**2)
												__a:						Any		    =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						Any		    =					self.config.sigma_min
												__a:						Optional[Any]		    =					self.config.sigma_data

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






							def 							__lowercase  (					self						:			Any						, snake_case__						:			torch.FloatTensor						, snake_case__						:			Union[float, torch.FloatTensor]						, snake_case__						:			torch.FloatTensor						, snake_case__						:			Optional[torch.Generator] = None						, snake_case__						:			bool = True						, ):






												"""simple docstring"""



												if (
												    isinstance(snake_case__						, snake_case__    )
												    or isinstance(snake_case__						, torch.IntTensor    )
												    or isinstance(snake_case__						, 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(snake_case__						, torch.Tensor    ):
																	__a:						Union[str, Any]		    =					timestep.to(self.timesteps.device    )

												__a:						Union[str, Any]		    =					self.config.sigma_min
												__a:						Optional[Any]		    =					self.config.sigma_max

												__a:						List[str]		    =					self.index_for_timestep(snake_case__    )

												# sigma_next corresponds to next_t in original implementation
												__a:						Tuple		    =					self.sigmas[step_index]
												if step_index + 1 < self.config.num_train_timesteps:
																	__a:						int		    =					self.sigmas[step_index + 1]
												else:
																	# Set sigma_next to sigma_min
																	__a:						List[str]		    =					self.sigmas[-1]

												# Get scalings for boundary conditions
												__a:						Optional[int]		    =					self.get_scalings_for_boundary_condition(snake_case__    )

												# 1. Denoise model output using boundary conditions
												__a:						Tuple		    =					c_out * model_output + c_skip * sample
												if self.config.clip_denoised:
																	__a:						str		    =					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:
																	__a:						Union[str, Any]		    =					randn_tensor(
																	    model_output.shape						, dtype=model_output.dtype						, device=model_output.device						, generator=snake_case__    )
												else:
																	__a:						Optional[int]		    =					torch.zeros_like(snake_case__    )
												__a:						Dict		    =					noise * self.config.s_noise

												__a:						str		    =					sigma_next.clamp(min=snake_case__						, max=snake_case__    )

												# 3. Return noisy sample
												# tau = sigma_hat, eps = sigma_min
												__a:						Optional[int]		    =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

												if not return_dict:
																	return (prev_sample,)

												return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case__    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			torch.FloatTensor						, snake_case__						:			torch.FloatTensor						, snake_case__						:			torch.FloatTensor						, ):






												"""simple docstring"""



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

												__a:						List[str]		    =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

												__a:						Tuple		    =					sigmas[step_indices].flatten()
												while len(sigma.shape    ) < len(original_samples.shape    ):
																	__a:						List[str]		    =					sigma.unsqueeze(-1    )

												__a:						List[Any]		    =					original_samples + noise * sigma
												return noisy_samples






							def __len__(					self						:			List[str]    ):






												"""simple docstring"""



												return self.config.num_train_timesteps


"""simple docstring"""
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'):
			lowercase		:						str =					True
			from torch.cuda.amp import autocast


lowercase		:						int =					logging.getLogger(__name__)




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to log verbose messages or not.'''} ,				)






def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


					'''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 )]	,				)
					__a:						List[Any]		    =					logging.WARNING
					if model_args.verbose_logging:
										__a:						Dict		    =					logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank ):
										__a:						Optional[int]		    =					logging.INFO
					logger.setLevel(lowerCamelCase_ )




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






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						False
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						"|"
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						None






							@classmethod
							def 							__lowercase  (					cls						:			List[Any]						, snake_case__						:			str    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			str    ):






												"""simple docstring"""



												if len(self.translation_table    ) > 0:
																	__a:						Optional[int]		    =					text.translate(self.translation_table    )
												if len(self.words_to_remove    ) == 0:
																	__a:						Tuple		    =					""" """.join(text.split()    )  # clean up whitespaces
												else:
																	__a:						int		    =					""" """.join(w for w in text.split() if w not in self.words_to_remove    )  # and clean up whilespaces
												return text






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			ModelArguments    ):






												"""simple docstring"""



												__a:						Dict		    =					WavaVecaFeatureExtractor.from_pretrained(
												    model_args.model_name_or_path						, cache_dir=model_args.cache_dir    )
												if self.vocab_file:
																	__a:						Dict		    =					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:
																	__a:						Dict		    =					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(snake_case__						, snake_case__    )






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						True
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None






							def __call__(					self						:			Optional[Any]						, snake_case__						:			List[Dict[str, Union[List[int], torch.Tensor]]]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					[{"""input_values""": feature["""input_values"""]} for feature in features]
												__a:						Optional[Any]		    =					[{"""input_ids""": feature["""labels"""]} for feature in features]

												__a:						Optional[Any]		    =					self.processor.pad(
												    snake_case__						, padding=self.padding						, max_length=self.max_length						, pad_to_multiple_of=self.pad_to_multiple_of						, return_tensors="""pt"""						, )
												__a:						str		    =					self.processor.pad(
												    labels=snake_case__						, 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
												__a:						Any		    =					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )						, -1_0_0    )

												__a:						List[Any]		    =					labels

												return batch






class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			nn.Module						, snake_case__						:			Dict[str, Union[torch.Tensor, Any]]    ):






												"""simple docstring"""



												model.train()
												__a:						int		    =					self._prepare_inputs(snake_case__    )

												if self.use_amp:
																	with autocast():
																						__a:						List[str]		    =					self.compute_loss(snake_case__						, snake_case__    )
												else:
																	__a:						Tuple		    =					self.compute_loss(snake_case__						, snake_case__    )

												if self.args.n_gpu > 1:
																	if model.module.config.ctc_loss_reduction == "mean":
																						__a:						Tuple		    =					loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																						__a:						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:
																	__a:						Any		    =					loss / self.args.gradient_accumulation_steps

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

												return loss.detach()






def     lowerCamelCase_    (	)     ->			List[Any]:


					'''simple docstring'''
					# 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.

					__a:						int		    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

					__a    ,				__a    ,				__a:						List[str]		    =					parser.parse_args_into_dataclasses()
					configure_logger(lowerCamelCase_	,				lowerCamelCase_ )

					__a:						Union[str, Any]		    =					Orthography.from_name(data_args.orthography.lower() )
					__a:						List[str]		    =					orthography.create_processor(lowerCamelCase_ )
					__a:						int		    =					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 )	,				)

					__a:						int		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.train_split_name )
					__a:						Tuple		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.validation_split_name )

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

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

					__a:						str		    =					train_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )
					__a:						Union[str, Any]		    =					val_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )

					if data_args.max_duration_in_seconds is not None:

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

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

					logger.warning(f"""Updated {len(lowerCamelCase_ )} 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}\"""" )
					__a:						List[str]		    =					None

					def prepare_dataset(lowerCamelCase_ ):
										# 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}."""

										__a:						Tuple		    =					processor(
										    audio=batch["""speech"""]	,				text=batch[data_args.target_text_column]	,				sampling_rate=batch["""sampling_rate"""][0] )
										batch.update(lowerCamelCase_ )
										return batch

					__a:						Union[str, Any]		    =					train_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)
					__a:						Tuple		    =					val_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)

					__a:						Union[str, Any]		    =					DataCollatorCTCWithPadding(processor=lowerCamelCase_	,				padding=lowerCamelCase_ )

					def compute_metrics(lowerCamelCase_ ):
										__a:						Any		    =					pred.predictions
										__a:						List[Any]		    =					np.argmax(lowerCamelCase_	,				axis=-1 )

										__a:						int		    =					processor.tokenizer.pad_token_id

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

										__a:						Optional[int]		    =					wer_metric.compute(predictions=lowerCamelCase_	,				references=lowerCamelCase_ )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					__a:						Any		    =					CTCTrainer(
					    model=lowerCamelCase_	,				data_collator=lowerCamelCase_	,				args=lowerCamelCase_	,				compute_metrics=lowerCamelCase_	,				train_dataset=lowerCamelCase_	,				eval_dataset=lowerCamelCase_	,				tokenizer=processor.feature_extractor	,				)

					trainer.train()


if __name__ == "__main__":
			main()
