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'''):
   __lowerCamelCase			: List[str]							   =		True
   from torch.cuda.amp import autocast


__lowerCamelCase			: Dict							   =		logging.getLogger(__name__)







@dataclass
class __snake_case				:
   lowerCAmelCase_														=							field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_   ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,		)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_   ,		metadata={"help": "Whether to freeze the feature extractor layers of the model."}		)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_   ,		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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       ModelArguments ,     __UpperCamelCase    :       TrainingArguments	)							->					str:







  """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	)] ,     )
  SCREAMING_SNAKE_CASE__										=      logging.WARNING
  if model_args.verbose_logging:
    SCREAMING_SNAKE_CASE__										=      logging.DEBUG
  elif trainer_utils.is_main_process(training_args.local_rank	):
    SCREAMING_SNAKE_CASE__										=      logging.INFO
  logger.setLevel(__UpperCamelCase	)







@dataclass
class __snake_case				:
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_   ,		metadata={"help": "The name of the dataset to use (via the datasets library)."}		)
   lowerCAmelCase_														=							field(
       default=lowerCamelCase_   ,		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=lowerCamelCase_   ,		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=lowerCamelCase_   ,		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 __snake_case				:
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							"longest"
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.feature_extractor.pad(
         _lowercase       ,	max_length=self.max_length       ,	padding=self.padding       ,	pad_to_multiple_of=self.pad_to_multiple_of       ,	return_tensors="""pt"""       ,	)
     SCREAMING_SNAKE_CASE__										=      self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]      )

     SCREAMING_SNAKE_CASE__										=      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
       SCREAMING_SNAKE_CASE__										=      self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1      )      ).to(
           torch.long      )

       SCREAMING_SNAKE_CASE__										=      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
       SCREAMING_SNAKE_CASE__										=      1
       SCREAMING_SNAKE_CASE__										=      attention_mask.flip([-1]      ).cumsum(-1      ).flip([-1]      ).bool()

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

     return batch







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				List[str]       ,	*_lowercase			:				List[str]       ,	_lowercase			:				str=1       ,	_lowercase			:				Optional[Any]=0       ,	_lowercase			:				Optional[int]=1.0       ,	**_lowercase			:				int      ):



     """simple docstring"""






     super().__init__(*_lowercase       ,	**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      0
     SCREAMING_SNAKE_CASE__										=      max_gumbel_temp
     SCREAMING_SNAKE_CASE__										=      min_gumbel_temp
     SCREAMING_SNAKE_CASE__										=      gumbel_temp_decay




   def 	__a						(    self			:				int       ,	_lowercase			:				nn.Module       ,	_lowercase			:				Dict[str, Union[torch.Tensor, Any]]      ):



     """simple docstring"""






     model.train()
     SCREAMING_SNAKE_CASE__										=      self._prepare_inputs(_lowercase      )

     if self.use_amp:
       with autocast():
         SCREAMING_SNAKE_CASE__										=      self.compute_loss(_lowercase       ,	_lowercase      )
     else:
       SCREAMING_SNAKE_CASE__										=      self.compute_loss(_lowercase       ,	_lowercase      )

     if self.args.n_gpu > 1 or self.deepspeed:
       if model.module.config.ctc_loss_reduction == "mean":
         SCREAMING_SNAKE_CASE__										=      loss.mean()
       elif model.module.config.ctc_loss_reduction == "sum":
         SCREAMING_SNAKE_CASE__										=      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:
       SCREAMING_SNAKE_CASE__										=      loss / self.args.gradient_accumulation_steps

     if self.use_amp:
       self.scaler.scale(_lowercase      ).backward()
     elif self.use_apex:
       with amp.scale_loss(_lowercase       ,	self.optimizer      ) as scaled_loss:
         scaled_loss.backward()
     elif self.deepspeed:
       self.deepspeed.backward(_lowercase      )
     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 	__SCREAMING_SNAKE_CASE  (					)							->					List[str]:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      parser.parse_args_into_dataclasses()
  configure_logger(__UpperCamelCase ,     __UpperCamelCase	)

  # Downloading and loading a dataset from the hub.
  SCREAMING_SNAKE_CASE__										=      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"
    SCREAMING_SNAKE_CASE__										=      DatasetDict()
    SCREAMING_SNAKE_CASE__										=      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 ,     )
    SCREAMING_SNAKE_CASE__										=      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"
    SCREAMING_SNAKE_CASE__										=      DatasetDict()
    SCREAMING_SNAKE_CASE__										=      load_dataset(
        data_args.dataset_name ,     data_args.dataset_config_name ,     split="""validation""" ,     cache_dir=model_args.cache_dir ,     )
    SCREAMING_SNAKE_CASE__										=      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
  SCREAMING_SNAKE_CASE__										=      WavaVecaFeatureExtractor.from_pretrained(
      model_args.model_name_or_path ,     cache_dir=model_args.cache_dir ,     do_normalize=__UpperCamelCase	)

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

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

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

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

  # normalize and transform to `BatchFeatures`
  SCREAMING_SNAKE_CASE__										=      vectorized_datasets.map(
      __UpperCamelCase ,     batched=__UpperCamelCase ,     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
  SCREAMING_SNAKE_CASE__										=      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'"""	)

  SCREAMING_SNAKE_CASE__										=      WavaVecaForPreTraining(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      DataCollatorForWavaVecaPretraining(model=__UpperCamelCase ,     feature_extractor=__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      WavaVecaPreTrainer(
      model=__UpperCamelCase ,     data_collator=__UpperCamelCase ,     args=__UpperCamelCase ,     train_dataset=vectorized_datasets["""train"""] ,     eval_dataset=vectorized_datasets["""validation"""] ,     tokenizer=__UpperCamelCase ,     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()

import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
   # Create universe of discourse in Python using linspace ()
   __lowerCamelCase			: List[Any]							   =		np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

   # Create two fuzzy sets by defining any membership function
   # (trapmf(), gbellmf(), gaussmf(), etc).
   __lowerCamelCase			: int							   =		[0, 25, 50]
   __lowerCamelCase			: Tuple							   =		[25, 50, 75]
   __lowerCamelCase			: List[str]							   =		fuzz.membership.trimf(X, abca)
   __lowerCamelCase			: Tuple							   =		fuzz.membership.trimf(X, abca)

   # Compute the different operations using inbuilt functions.
   __lowerCamelCase			: List[str]							   =		np.ones(75)
   __lowerCamelCase			: Tuple							   =		np.zeros((75,))
   # 1. Union = max(µA(x), µB(x))
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, young, X, middle_aged)[1]
   # 2. Intersection = min(µA(x), µB(x))
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, young, X, middle_aged)[1]
   # 3. Complement (A) = (1- min(µA(x))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_not(young)
   # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
   # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
   __lowerCamelCase			: List[Any]							   =		young + middle_aged - (young * middle_aged)
   # 6. Algebraic Product = (µA(x) * µB(x))
   __lowerCamelCase			: int							   =		young * middle_aged
   # 7. Bounded Sum = min[1,(µA(x), µB(x))]
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
   # 8. Bounded difference = min[0,(µA(x), µB(x))]
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

   # max-min composition
   # max-product composition

   # Plot each set A, set B and each operation result using plot() and subplot().
   from matplotlib import pyplot as plt

   plt.figure()

   plt.subplot(4, 3, 1)
   plt.plot(X, young)
   plt.title('''Young''')
   plt.grid(True)

   plt.subplot(4, 3, 2)
   plt.plot(X, middle_aged)
   plt.title('''Middle aged''')
   plt.grid(True)

   plt.subplot(4, 3, 3)
   plt.plot(X, union)
   plt.title('''union''')
   plt.grid(True)

   plt.subplot(4, 3, 4)
   plt.plot(X, intersection)
   plt.title('''intersection''')
   plt.grid(True)

   plt.subplot(4, 3, 5)
   plt.plot(X, complement_a)
   plt.title('''complement_a''')
   plt.grid(True)

   plt.subplot(4, 3, 6)
   plt.plot(X, difference)
   plt.title('''difference a/b''')
   plt.grid(True)

   plt.subplot(4, 3, 7)
   plt.plot(X, alg_sum)
   plt.title('''alg_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 8)
   plt.plot(X, alg_product)
   plt.title('''alg_product''')
   plt.grid(True)

   plt.subplot(4, 3, 9)
   plt.plot(X, bdd_sum)
   plt.title('''bdd_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 10)
   plt.plot(X, bdd_difference)
   plt.title('''bdd_difference''')
   plt.grid(True)

   plt.subplots_adjust(hspace=0.5)
   plt.show()
