






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'):
	_UpperCamelCase			:	Optional[int]								=True
	from torch.cuda.amp import autocast


_UpperCamelCase			:	int								=logging.getLogger(__name__)






@dataclass
class 						UpperCAmelCase__ :
		__snake_case      :       str     =   field(
		    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
		__snake_case      :       Optional[str]     =   field(
		    default=__snake_case     ,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,				)
		__snake_case      :       Optional[bool]     =   field(
		    default=__snake_case     ,				metadata={"help": "Whether to freeze the feature extractor layers of the model."}							)
		__snake_case      :       Optional[bool]     =   field(
		    default=__snake_case     ,				metadata={"help": "Whether to log verbose messages or not."}     ,				)
		__snake_case      :       Optional[float]     =   field(
		    default=2.0     ,				metadata={"help": "Maximum temperature for gumbel softmax."}							)
		__snake_case      :       Optional[float]     =   field(
		    default=0.5     ,				metadata={"help": "Minimum temperature for gumbel softmax."}							)
		__snake_case      :       Optional[float]     =   field(
		    default=0.9_9_9_9_9_5     ,				metadata={"help": "Decay of gumbel temperature during training."}							)



def  a__ (__lowercase							:ModelArguments					,  __lowercase							:TrainingArguments   )		-> int:
				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[str]   =      logging.WARNING
				if model_args.verbose_logging:
								_A	:				Union[str, Any]   =      logging.DEBUG
				elif trainer_utils.is_main_process(training_args.local_rank   ):
								_A	:				Tuple   =      logging.INFO
				logger.setLevel(__lowercase   )






@dataclass
class 						UpperCAmelCase__ :
		__snake_case      :       str     =   field(
		    default=__snake_case     ,				metadata={"help": "The name of the dataset to use (via the datasets library)."}							)
		__snake_case      :       Optional[str]     =   field(
		    default=__snake_case     ,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
		__snake_case      :       Optional[str]     =   field(
		    default="train"     ,				metadata={
		        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
		    }     ,				)
		__snake_case      :       Optional[str]     =   field(
		    default="validation"     ,				metadata={
		        "help": (
		            "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
		        )
		    }     ,				)
		__snake_case      :       Optional[str]     =   field(
		    default="file"     ,				metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}     ,				)
		__snake_case      :       bool     =   field(
		    default=__snake_case     ,				metadata={"help": "Overwrite the cached preprocessed datasets or not."}							)
		__snake_case      :       Optional[int]     =   field(
		    default=1     ,				metadata={
		        "help": "The percentage of the train set used as validation set in case there's no validation split"
		    }     ,				)
		__snake_case      :       Optional[int]     =   field(
		    default=__snake_case     ,				metadata={"help": "The number of processes to use for the preprocessing."}     ,				)
		__snake_case      :       Optional[float]     =   field(
		    default=2_0.0     ,				metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}							)






@dataclass
class 						UpperCAmelCase__ :
		__snake_case      :       WavaVecaForPreTraining
		__snake_case      :       WavaVecaFeatureExtractor
		__snake_case      :       Union[bool, str]     =   "longest"
		__snake_case      :       Optional[int]     =   None
		__snake_case      :       Optional[int]     =   None







		def __call__(					self       ,A__		):
						# reformat list to dict and set to pytorch format
						_A	:				Union[str, Any]   =      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'''       ,)
						_A	:				Tuple   =      self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]		)

						_A	:				List[Any]   =      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
										_A	:				Optional[int]   =      self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1		)		).to(
										    torch.long		)

										_A	:				int   =      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
										_A	:				List[str]   =      1
										_A	:				Optional[int]   =      attention_mask.flip([-1]		).cumsum(-1		).flip([-1]		).bool()

						# sample randomly masked indices
						_A	:				int   =      _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 						UpperCAmelCase__ (							__snake_case							):







		def __init__(					self       ,*A__       ,A__=1       ,A__=0       ,A__=1.0       ,**A__		):
						super().__init__(*A__       ,**A__		)
						_A	:				List[Any]   =      0
						_A	:				Optional[int]   =      max_gumbel_temp
						_A	:				List[Any]   =      min_gumbel_temp
						_A	:				Any   =      gumbel_temp_decay







		def  A__      (					self       ,A__       ,A__		):
						model.train()
						_A	:				str   =      self._prepare_inputs(A__		)

						if self.use_amp:
										with autocast():
														_A	:				Optional[int]   =      self.compute_loss(A__       ,A__		)
						else:
										_A	:				Optional[Any]   =      self.compute_loss(A__       ,A__		)

						if self.args.n_gpu > 1 or self.deepspeed:
										if model.module.config.ctc_loss_reduction == "mean":
														_A	:				Tuple   =      loss.mean()
										elif model.module.config.ctc_loss_reduction == "sum":
														_A	:				Optional[Any]   =      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:
										_A	:				Dict   =      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  a__ ()		-> Optional[Any]:
				# 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	:				Any   =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )

				_A  ,				_A  ,				_A	:				str   =      parser.parse_args_into_dataclasses()
				configure_logger(__lowercase					,  __lowercase   )

				# Downloading and loading a dataset from the hub.
				_A	:				List[str]   =      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"
								_A	:				Tuple   =      DatasetDict()
								_A	:				Optional[Any]   =      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					,  )
								_A	:				Tuple   =      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"
								_A	:				Optional[Any]   =      DatasetDict()
								_A	:				List[Any]   =      load_dataset(
								    data_args.dataset_name					,  data_args.dataset_config_name					,  split='''validation'''					,  cache_dir=model_args.cache_dir					,  )
								_A	:				Optional[int]   =      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
				_A	:				Any   =      WavaVecaFeatureExtractor.from_pretrained(
				    model_args.model_name_or_path					,  cache_dir=model_args.cache_dir					,  do_normalize=__lowercase   )

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

				# load audio files into numpy arrays
				_A	:				str   =      datasets.map(
				    __lowercase					,  num_proc=data_args.preprocessing_num_workers					,  remove_columns=datasets['''train'''].column_names   )

				# filter audio files that are too long
				_A	:				List[str]   =      vectorized_datasets.filter(
				    lambda __lowercase   : len(data['''speech''']   ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate   )   )

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

				# normalize and transform to `BatchFeatures`
				_A	:				List[Any]   =      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
				_A	:				Union[str, Any]   =      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\''''   )

				_A	:				Tuple   =      WavaVecaForPreTraining(__lowercase   )

				_A	:				Optional[int]   =      DataCollatorForWavaVecaPretraining(model=__lowercase					,  feature_extractor=__lowercase   )

				_A	:				int   =      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'))



