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"""):
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		True
  from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE			:						Dict				  =		logging.getLogger(__name__)
@dataclass
class  A_			:
  _SCREAMING_SNAKE_CASE     					=      field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=lowerCamelCase__      ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,							)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=lowerCamelCase__      ,							metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}			)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=lowerCamelCase__      ,							metadata={"""help""": """Whether to log verbose messages or not."""}      ,							)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=2.0      ,							metadata={"""help""": """Maximum temperature for gumbel softmax."""}			)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=0.5      ,							metadata={"""help""": """Minimum temperature for gumbel softmax."""}			)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=0.9_9_9_9_9_5      ,							metadata={"""help""": """Decay of gumbel temperature during training."""}			)



def 	__A		(      _A				,					_A       ):



 """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							=	logging.WARNING
 if model_args.verbose_logging:
  __a							=	logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank       ):
  __a							=	logging.INFO
 logger.setLevel(_lowerCAmelCase       )
@dataclass
class  A_			:
  _SCREAMING_SNAKE_CASE     					=      field(
      default=lowerCamelCase__      ,							metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}			)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=lowerCamelCase__      ,							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="""file"""      ,							metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to \'file\'"""}      ,							)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=lowerCamelCase__      ,							metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}			)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=1      ,							metadata={
          """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
      }      ,							)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=lowerCamelCase__      ,							metadata={"""help""": """The number of processes to use for the preprocessing."""}      ,							)
  _SCREAMING_SNAKE_CASE     					=      field(
      default=2_0.0      ,							metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}			)
@dataclass
class  A_			:
  _SCREAMING_SNAKE_CASE     					=      42
  _SCREAMING_SNAKE_CASE     					=      42
  _SCREAMING_SNAKE_CASE     					=      """longest"""
  _SCREAMING_SNAKE_CASE     					=      None
  _SCREAMING_SNAKE_CASE     					=      None

  def __call__(		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Dict[str, Union[List[int], torch.Tensor]]]							):
   __a							=	self.feature_extractor.pad(
       __SCREAMING_SNAKE_CASE  ,				max_length=self.max_length  ,				padding=self.padding  ,				pad_to_multiple_of=self.pad_to_multiple_of  ,				return_tensors="pt"  ,				)
   __a							=	self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]							)

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

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

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

   return batch
class  A_			(							lowerCamelCase__			):

  def __init__(		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Any=1  ,				__SCREAMING_SNAKE_CASE			:   Dict=0  ,				__SCREAMING_SNAKE_CASE			:   Dict=1.0  ,				**__SCREAMING_SNAKE_CASE			:   Any							):
   super().__init__(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
   __a							=	0
   __a							=	max_gumbel_temp
   __a							=	min_gumbel_temp
   __a							=	gumbel_temp_decay

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   nn.Module  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, Union[torch.Tensor, Any]]							):
   model.train()
   __a							=	self._prepare_inputs(__SCREAMING_SNAKE_CASE							)

   if self.use_amp:
    with autocast():
     __a							=	self.compute_loss(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   else:
    __a							=	self.compute_loss(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

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

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



 """simple docstring"""


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

 __a							=	parser.parse_args_into_dataclasses()
 configure_logger(_lowerCAmelCase				,					_lowerCAmelCase       )

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

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

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

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

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

 # normalize and transform to `BatchFeatures`
 __a							=	vectorized_datasets.map(
     _lowerCAmelCase				,					batched=_lowerCAmelCase				,					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							=	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							=	WavaVecaForPreTraining(_lowerCAmelCase       )

 __a							=	DataCollatorForWavaVecaPretraining(model=_lowerCAmelCase				,					feature_extractor=_lowerCAmelCase       )

 __a							=	WavaVecaPreTrainer(
     model=_lowerCAmelCase				,					data_collator=_lowerCAmelCase				,					args=_lowerCAmelCase				,					train_dataset=vectorized_datasets["train"]				,					eval_dataset=vectorized_datasets["validation"]				,					tokenizer=_lowerCAmelCase				,					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 csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def   __snake_case    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  field(
         metadata={'''help''': '''The csv file to plot.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Disable logarithmic scale when plotting'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={
             '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
         }     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}     ,					)
     __UpperCamelCase            =  list_field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}				)




def   __snake_case    (       _lowerCAmelCase			:			int						)			->    Optional[Any]:
   try:
      int(_lowerCAmelCase						)
      return True
   except ValueError:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Union[str, Any]:
   try:
      float(_lowerCAmelCase						)
      return True
   except ValueError:
      return False


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Any      ,   snake_case							:List[Any]	):





        '''simple docstring'''




        A_       :					Any        =      args
        A_       :					Dict        =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

        with open(self.args.csv_file      ,   newline=""	) as csv_file:
           A_       :					Union[str, Any]        =      csv.DictReader(snake_case	)
           for row in reader:
              A_       :					Any        =      row["model"]
              self.result_dict[model_name]["bsz"].append(int(row["batch_size"]	)	)
              self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]	)	)
              if can_convert_to_int(row["result"]	):
                 # value is not None
                 A_       :					List[Any]        =      int(row["result"]	)
              elif can_convert_to_float(row["result"]	):
                 # value is not None
                 A_       :					Optional[Any]        =      float(row["result"]	)





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_   ,    A_       :					List[str]        =      plt.subplots()
        A_       :					List[str]        =      "Time usage" if self.args.is_time else "Memory usage"
        A_       :					Optional[Any]        =      title_str + " for training" if self.args.is_train else title_str + " for inference"

        if not self.args.no_log_scale:
           # set logarithm scales
           ax.set_xscale("log"	)
           ax.set_yscale("log"	)

        for axis in [ax.xaxis, ax.yaxis]:
           axis.set_major_formatter(ScalarFormatter()	)

        for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
           A_       :					int        =      sorted(set(self.result_dict[model_name]["bsz"]	)	)
           A_       :					List[str]        =      sorted(set(self.result_dict[model_name]["seq_len"]	)	)
           A_       :					Dict        =      self.result_dict[model_name]["result"]

           ((A_)   ,    (A_))       :					Optional[Any]        =      (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           A_       :					List[Any]        =      (
               model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
           )

           for inner_loop_value in inner_loop_array:
              if self.args.plot_along_batch:
                 A_       :					Dict        =      np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,   dtype=snake_case      ,   )
              else:
                 A_       :					Union[str, Any]        =      np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,   dtype=np.floataa      ,   )

              ((A_)   ,    (A_))       :					Optional[Any]        =      (
                  ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
              )

              A_       :					List[Any]        =      np.asarray(snake_case      ,   snake_case	)[: len(snake_case	)]
              plt.scatter(
                  snake_case      ,   snake_case      ,   label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"	)
              plt.plot(snake_case      ,   snake_case      ,   "--"	)

           title_str += f" {label_model_name} vs."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					List[str]        =      "Time in s" if self.args.is_time else "Memory in MB"

        # plot
        plt.title(snake_case	)
        plt.xlabel(snake_case	)
        plt.ylabel(snake_case	)
        plt.legend()

        if self.args.figure_png_file is not None:
           plt.savefig(self.args.figure_png_file	)
        else:
           plt.show()




def   __snake_case    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
