





'''simple docstring'''



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

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

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


if is_apex_available():
			from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
			snake_case_ :	Optional[int]             =   True
			from torch.cuda.amp import autocast


snake_case_ :	int             =   logging.getLogger(__name__)

@dataclass
class      A_			:

			'''simple docstring'''





			_lowerCAmelCase							=     field(
			    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
			_lowerCAmelCase							=     field(
			    default=__snake_case     ,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}     ,      )
			_lowerCAmelCase							=     field(
			    default=__snake_case     ,      metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}	)
			_lowerCAmelCase							=     field(
			    default=__snake_case     ,      metadata={"""help""": """Whether to log verbose messages or not."""}     ,      )
			_lowerCAmelCase							=     field(
			    default=2.0     ,      metadata={"""help""": """Maximum temperature for gumbel softmax."""}	)
			_lowerCAmelCase							=     field(
			    default=0.5     ,      metadata={"""help""": """Minimum temperature for gumbel softmax."""}	)
			_lowerCAmelCase							=     field(
			    default=0.999995     ,      metadata={"""help""": """Decay of gumbel temperature during training."""}	)




def 			lowercase__(		_UpperCamelCase	:			ModelArguments			, _UpperCamelCase	:			TrainingArguments					)-> int:






					"""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									= logging.WARNING
					if model_args.verbose_logging:
										_UpperCamelCase									= logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank					):
										_UpperCamelCase									= logging.INFO
					logger.setLevel(__lowercase					)

@dataclass
class      A_			:

			'''simple docstring'''





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




@dataclass
class      A_			:

			'''simple docstring'''





			_lowerCAmelCase							=     42
			_lowerCAmelCase							=     42
			_lowerCAmelCase							=     "longest"
			_lowerCAmelCase							=     None
			_lowerCAmelCase							=     None




			def __call__(       self      ,  A_ ):
								# reformat list to dict and set to pytorch format
								_UpperCamelCase									= self.feature_extractor.pad(
								    A__      ,  max_length=self.max_length      ,  padding=self.padding      ,  pad_to_multiple_of=self.pad_to_multiple_of      ,  return_tensors="pt"      ,  )
								_UpperCamelCase									= self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )

								_UpperCamelCase									= batch['''input_values'''].shape[0]

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

													_UpperCamelCase									= torch.zeros(
													    (batch_size, mask_indices_seq_length)      ,  dtype=torch.long      ,  device=batch["input_values"].device )

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

								# sample randomly masked indices
								_UpperCamelCase									= _compute_mask_indices(
								    (batch_size, mask_indices_seq_length)      ,  self.model.config.mask_time_prob      ,  self.model.config.mask_time_length      ,  attention_mask=A__      ,  min_masks=2      ,  )

								return batch




class      A_			(	__snake_case	):

			'''simple docstring'''





			def __init__(       self      ,  *A_      ,  A_=1      ,  A_=0      ,  A_=1.0      ,  **A_ ):
								super().__init__(*A__      ,  **A__ )
								_UpperCamelCase									= 0
								_UpperCamelCase									= max_gumbel_temp
								_UpperCamelCase									= min_gumbel_temp
								_UpperCamelCase									= gumbel_temp_decay




			def    a						(       self      ,  A_      ,  A_ ):
								model.train()
								_UpperCamelCase									= self._prepare_inputs(A__ )

								if self.use_amp:
													with autocast():
																		_UpperCamelCase									= self.compute_loss(A__      ,  A__ )
								else:
													_UpperCamelCase									= self.compute_loss(A__      ,  A__ )

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

								if self.args.gradient_accumulation_steps > 1:
													_UpperCamelCase									= loss / self.args.gradient_accumulation_steps

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

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

								return loss.detach()




def 			lowercase__(		)-> Optional[Any]:






					"""simple docstring"""

					_UpperCamelCase									= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

					_UpperCamelCase									= parser.parse_args_into_dataclasses()
					configure_logger(__lowercase			, __lowercase					)

					# Downloading and loading a dataset from the hub.
					_UpperCamelCase									= load_dataset(data_args.dataset_name			, data_args.dataset_config_name			, cache_dir=model_args.cache_dir					)

					if "validation" not in datasets.keys():
										# make sure only "validation" and "train" keys remain"
										_UpperCamelCase									= DatasetDict()
										_UpperCamelCase									= load_dataset(
										    data_args.dataset_name			, data_args.dataset_config_name			, split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"			, cache_dir=model_args.cache_dir			, )
										_UpperCamelCase									= load_dataset(
										    data_args.dataset_name			, data_args.dataset_config_name			, split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"			, cache_dir=model_args.cache_dir			, )
					else:
										# make sure only "validation" and "train" keys remain"
										_UpperCamelCase									= DatasetDict()
										_UpperCamelCase									= load_dataset(
										    data_args.dataset_name			, data_args.dataset_config_name			, split="validation"			, cache_dir=model_args.cache_dir			, )
										_UpperCamelCase									= load_dataset(
										    data_args.dataset_name			, data_args.dataset_config_name			, split=f"{data_args.train_split_name}"			, cache_dir=model_args.cache_dir			, )

					# only normalized-inputs-training is supported
					_UpperCamelCase									= WavaVecaFeatureExtractor.from_pretrained(
					    model_args.model_name_or_path			, cache_dir=model_args.cache_dir			, do_normalize=__lowercase					)

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

					# load audio files into numpy arrays
					_UpperCamelCase									= datasets.map(
					    __lowercase			, num_proc=data_args.preprocessing_num_workers			, remove_columns=datasets["train"].column_names					)

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

					def normalize(_UpperCamelCase	:			Dict					):
										return feature_extractor(batch["speech"]			, sampling_rate=feature_extractor.sampling_rate					)

					# normalize and transform to `BatchFeatures`
					_UpperCamelCase									= vectorized_datasets.map(
					    __lowercase			, batched=__lowercase			, num_proc=data_args.preprocessing_num_workers			, load_from_cache_file=not data_args.overwrite_cache			, remove_columns=vectorized_datasets["train"].column_names			, )

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

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

					_UpperCamelCase									= WavaVecaForPreTraining(__lowercase					)

					_UpperCamelCase									= DataCollatorForWavaVecaPretraining(model=__lowercase			, feature_extractor=__lowercase					)

					_UpperCamelCase									= WavaVecaPreTrainer(
					    model=__lowercase			, data_collator=__lowercase			, args=__lowercase			, train_dataset=vectorized_datasets["train"]			, eval_dataset=vectorized_datasets["validation"]			, tokenizer=__lowercase			, max_gumbel_temp=model_args.max_gumbel_temperature			, min_gumbel_temp=model_args.min_gumbel_temperature			, gumbel_temp_decay=model_args.gumbel_temperature_decay			, )
					trainer.train()


if __name__ == "__main__":
			main()











def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> float:

				def get_matched_characters(__lowercase							:str					,  __lowercase							:str   ) -> str:
								_A	:				Union[str, Any]   =      []
								_A	:				Dict   =      min(len(_stra   )					,  len(_stra   )   ) // 2
								for i, l in enumerate(_stra   ):
												_A	:				Tuple   =      int(max(0					,  i - limit   )   )
												_A	:				Union[str, Any]   =      int(min(i + limit + 1					,  len(_stra   )   )   )
												if l in _stra[left:right]:
																matched.append(__lowercase   )
																_A	:				List[Any]   =      f"""{_stra[0:_stra.index(__lowercase   )]} {_stra[_stra.index(__lowercase   ) + 1:]}"""

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

				# transposition
				_A	:				str   =      (
				    len([(ca, ca) for ca, ca in zip(__lowercase					,  __lowercase   ) if ca != ca]   ) // 2
				)

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				Any   =      0
				for ca, ca in zip(stra[:4]					,  stra[:4]   ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler('hello', 'world'))



