

"""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'):
				_lowercase					:     List[Any]				 =		True
				from torch.cuda.amp import autocast


_lowercase					:     str				 =		logging.getLogger(__name__)
@dataclass
class 			_UpperCAmelCase					:
	a__						:      str						       = field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
	a__						:      Optional[str]						       = field(
	    default=__lowercase						,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,  )
	a__						:      Optional[bool]						       = field(
	    default=__lowercase						,  metadata={"help": "Whether to freeze the feature extractor layers of the model."}    )
	a__						:      Optional[bool]						       = field(
	    default=__lowercase						,  metadata={"help": "Whether to log verbose messages or not."}						,  )
	a__						:      Optional[float]						       = field(
	    default=2.0						,  metadata={"help": "Maximum temperature for gumbel softmax."}    )
	a__						:      Optional[float]						       = field(
	    default=0.5						,  metadata={"help": "Minimum temperature for gumbel softmax."}    )
	a__						:      Optional[float]						       = field(
	    default=0.99_9995						,  metadata={"help": "Decay of gumbel temperature during training."}    )






def 	lowercase__		(    snake_case_					:ModelArguments       ,						snake_case_					:TrainingArguments  ):
	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(snake_case_  )
@dataclass
class 			_UpperCAmelCase					:
	a__						:      str						       = field(
	    default=__lowercase						,  metadata={"help": "The name of the dataset to use (via the datasets library)."}    )
	a__						:      Optional[str]						       = field(
	    default=__lowercase						,  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="file"						,  metadata={"help": "Column in the dataset that contains speech file path. Defaults to \'file\'"}						,  )
	a__						:      bool						       = field(
	    default=__lowercase						,  metadata={"help": "Overwrite the cached preprocessed datasets or not."}    )
	a__						:      Optional[int]						       = field(
	    default=1						,  metadata={
	        "help": "The percentage of the train set used as validation set in case there\'s no validation split"
	    }						,  )
	a__						:      Optional[int]						       = field(
	    default=__lowercase						,  metadata={"help": "The number of processes to use for the preprocessing."}						,  )
	a__						:      Optional[float]						       = field(
	    default=20.0						,  metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}    )






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



	def __call__(   self			:    Union[str, Any]       ,  _lowercase			:    List[Dict[str, Union[List[int], torch.Tensor]]]		):
		# reformat list to dict and set to pytorch format
		__UpperCAmelCase							=      self.feature_extractor.pad(
		    UpperCAmelCase__       ,  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=UpperCAmelCase__       ,  min_masks=2       ,  )

		return batch






class 			_UpperCAmelCase					(				__lowercase    ):



	def __init__(   self			:    Optional[int]       ,  *_lowercase			:    Optional[int]       ,  _lowercase			:    Optional[int]=1       ,  _lowercase			:    Dict=0       ,  _lowercase			:    List[str]=1.0       ,  **_lowercase			:    int		):
		super().__init__(*UpperCAmelCase__       ,  **UpperCAmelCase__		)
		__UpperCAmelCase							=      0
		__UpperCAmelCase							=      max_gumbel_temp
		__UpperCAmelCase							=      min_gumbel_temp
		__UpperCAmelCase							=      gumbel_temp_decay



	def     a   (   self			:    Any       ,  _lowercase			:    nn.Module       ,  _lowercase			:    Dict[str, Union[torch.Tensor, Any]]		):
		model.train()
		__UpperCAmelCase							=      self._prepare_inputs(UpperCAmelCase__		)

		if self.use_amp:
			with autocast():
				__UpperCAmelCase							=      self.compute_loss(UpperCAmelCase__       ,  UpperCAmelCase__		)
		else:
			__UpperCAmelCase							=      self.compute_loss(UpperCAmelCase__       ,  UpperCAmelCase__		)

		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(UpperCAmelCase__		).backward()
		elif self.use_apex:
			with amp.scale_loss(UpperCAmelCase__       ,  self.optimizer		) as scaled_loss:
				scaled_loss.backward()
		elif self.deepspeed:
			self.deepspeed.backward(UpperCAmelCase__		)
		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__		(    ):
	# 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.

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

	__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase							=      parser.parse_args_into_dataclasses()
	configure_logger(snake_case_       ,						snake_case_  )

	# 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=snake_case_  )

	def prepare_dataset(snake_case_					:Optional[Any]  ):
		# check that all files have the correct sampling rate
		__UpperCAmelCase						,				__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(
	    snake_case_       ,						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 snake_case_  : len(data['''speech''']  ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate  )  )

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

	# normalize and transform to `BatchFeatures`
	__UpperCAmelCase							=      vectorized_datasets.map(
	    snake_case_       ,						batched=snake_case_       ,						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(snake_case_  )

	__UpperCAmelCase							=      DataCollatorForWavaVecaPretraining(model=snake_case_       ,						feature_extractor=snake_case_  )

	__UpperCAmelCase							=      WavaVecaPreTrainer(
	    model=snake_case_       ,						data_collator=snake_case_       ,						args=snake_case_       ,						train_dataset=vectorized_datasets['''train''']       ,						eval_dataset=vectorized_datasets['''validation''']       ,						tokenizer=snake_case_       ,						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()




'''simple docstring'''




import os

__snake_case    						={"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}





def 	a_					( lowerCamelCase	:     str				):
   lowerCAmelCase     =			0

   lowerCAmelCase     =			0
   while index < len(lowerCamelCase				) - 1:
      lowerCAmelCase     =			SYMBOLS[numerals[index]]
      lowerCAmelCase     =			SYMBOLS[numerals[index + 1]]
      if current_value < next_value:
         total_value -= current_value
      else:
         total_value += current_value
      index += 1
   total_value += SYMBOLS[numerals[index]]

   return total_value





def 	a_					( lowerCamelCase	:     int				):
   lowerCAmelCase     =			''

   lowerCAmelCase     =			num // 1000
   numerals += m_count * "M"
   num %= 1000

   lowerCAmelCase     =			num // 100
   if c_count == 9:
      numerals += "CM"
      c_count -= 9
   elif c_count == 4:
      numerals += "CD"
      c_count -= 4
   if c_count >= 5:
      numerals += "D"
      c_count -= 5
   numerals += c_count * "C"
   num %= 100

   lowerCAmelCase     =			num // 10
   if x_count == 9:
      numerals += "XC"
      x_count -= 9
   elif x_count == 4:
      numerals += "XL"
      x_count -= 4
   if x_count >= 5:
      numerals += "L"
      x_count -= 5
   numerals += x_count * "X"
   num %= 10

   if num == 9:
      numerals += "IX"
      num -= 9
   elif num == 4:
      numerals += "IV"
      num -= 4
   if num >= 5:
      numerals += "V"
      num -= 5
   numerals += num * "I"

   return numerals





def 	a_					( lowerCamelCase	:     str = "/p089_roman.txt"				):
   lowerCAmelCase     =			0

   with open(os.path.dirname(lowerCamelCase				) + roman_numerals_filename				) as filea:
      lowerCAmelCase     =			filea.readlines()

   for line in lines:
      lowerCAmelCase     =			line.strip()
      lowerCAmelCase     =			parse_roman_numerals(lowerCamelCase				)
      lowerCAmelCase     =			generate_roman_numerals(lowerCamelCase				)
      savings += len(lowerCamelCase				) - len(lowerCamelCase				)

   return savings


if __name__ == "__main__":
       print(F'''{solution() = }''')





