


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        =     True
       from torch.cuda.amp import autocast


__lowerCamelCase        =     logging.getLogger(__name__)


@dataclass
class   a__	:
       lowerCamelCase__:     str							    =     field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
       lowerCamelCase__:     Optional[str]							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,       )
       lowerCamelCase__:     Optional[bool]							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}    )
       lowerCamelCase__:     Optional[bool]							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to log verbose messages or not."""}						,       )
       lowerCamelCase__:     Optional[float]							    =     field(
           default=2.0						,       metadata={"""help""": """Maximum temperature for gumbel softmax."""}    )
       lowerCamelCase__:     Optional[float]							    =     field(
           default=0.5						,       metadata={"""help""": """Minimum temperature for gumbel softmax."""}    )
       lowerCamelCase__:     Optional[float]							    =     field(
           default=0.99_99_95						,       metadata={"""help""": """Decay of gumbel temperature during training."""}    )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     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_    :     Any										=    logging.INFO
     logger.setLevel(__UpperCamelCase )


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


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

       def __call__(   self      :     List[str]      ,							lowerCamelCase_      :     List[Dict[str, Union[List[int], torch.Tensor]]]    ):
            # reformat list to dict and set to pytorch format
            a_    :     List[Any]										=    self.feature_extractor.pad(
                lowerCamelCase_      ,							max_length=self.max_length      ,							padding=self.padding      ,							pad_to_multiple_of=self.pad_to_multiple_of      ,							return_tensors="""pt"""      ,							)
            a_    :     List[str]										=    self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]    )

            a_    :     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_    :     List[str]										=    self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1    )    ).to(
                     torch.long    )

                 a_    :     Tuple										=    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_    :     Any										=    1
                 a_    :     List[str]										=    attention_mask.flip([-1]    ).cumsum(-1    ).flip([-1]    ).bool()

            # sample randomly masked indices
            a_    :     Optional[Any]										=    _compute_mask_indices(
                (batch_size, mask_indices_seq_length)      ,							self.model.config.mask_time_prob      ,							self.model.config.mask_time_length      ,							attention_mask=lowerCamelCase_      ,							min_masks=2      ,							)

            return batch


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     Union[str, Any]      ,							*lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Any=1      ,							lowerCamelCase_      :     Optional[int]=0      ,							lowerCamelCase_      :     List[str]=1.0      ,							**lowerCamelCase_      :     Any    ):
            super().__init__(*lowerCamelCase_      ,							**lowerCamelCase_    )
            a_    :     str										=    0
            a_    :     List[Any]										=    max_gumbel_temp
            a_    :     Union[str, Any]										=    min_gumbel_temp
            a_    :     Optional[int]										=    gumbel_temp_decay

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     nn.Module      ,							lowerCamelCase_      :     Dict[str, Union[torch.Tensor, Any]]    ):
            model.train()
            a_    :     int										=    self._prepare_inputs(lowerCamelCase_    )

            if self.use_amp:
                 with autocast():
                      a_    :     List[str]										=    self.compute_loss(lowerCamelCase_      ,							lowerCamelCase_    )
            else:
                 a_    :     Any										=    self.compute_loss(lowerCamelCase_      ,							lowerCamelCase_    )

            if self.args.n_gpu > 1 or self.deepspeed:
                 if model.module.config.ctc_loss_reduction == "mean":
                      a_    :     Union[str, Any]										=    loss.mean()
                 elif model.module.config.ctc_loss_reduction == "sum":
                      a_    :     Optional[int]										=    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_    :     Optional[Any]										=    loss / self.args.gradient_accumulation_steps

            if self.use_amp:
                 self.scaler.scale(lowerCamelCase_    ).backward()
            elif self.use_apex:
                 with amp.scale_loss(lowerCamelCase_      ,							self.optimizer    ) as scaled_loss:
                      scaled_loss.backward()
            elif self.deepspeed:
                 self.deepspeed.backward(lowerCamelCase_    )
            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			(						):
     # 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_    :     Optional[Any]										=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

     a_			,			a_			,			a_    :     Tuple										=    parser.parse_args_into_dataclasses()
     configure_logger(__UpperCamelCase  ,  __UpperCamelCase )

     # Downloading and loading a dataset from the hub.
     a_    :     Dict										=    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_    :     Optional[Any]										=    DatasetDict()
          a_    :     Dict										=    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_    :     Union[str, 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  ,  )
     else:
          # make sure only "validation" and "train" keys remain"
          a_    :     Dict										=    DatasetDict()
          a_    :     Optional[Any]										=    load_dataset(
              data_args.dataset_name  ,  data_args.dataset_config_name  ,  split="""validation"""  ,  cache_dir=model_args.cache_dir  ,  )
          a_    :     str										=    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_    :     Dict										=    WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path  ,  cache_dir=model_args.cache_dir  ,  do_normalize=__UpperCamelCase )

     def prepare_dataset(__UpperCamelCase ):
          # 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_    :     int										=    datasets.map(
         __UpperCamelCase  ,  num_proc=data_args.preprocessing_num_workers  ,  remove_columns=datasets["""train"""].column_names )

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

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

     # normalize and transform to `BatchFeatures`
     a_    :     Optional[Any]										=    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
     a_    :     Tuple										=    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_    :     str										=    WavaVecaForPreTraining(__UpperCamelCase )

     a_    :     Union[str, Any]										=    DataCollatorForWavaVecaPretraining(model=__UpperCamelCase  ,  feature_extractor=__UpperCamelCase )

     a_    :     Optional[int]										=    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 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  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


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



def  _a			(						__UpperCamelCase ):
     try:
          int(__UpperCamelCase )
          return True
     except ValueError:
          return False



def  _a			(						__UpperCamelCase ):
     try:
          float(__UpperCamelCase )
          return True
     except ValueError:
          return False


class   a__	:

       def __init__(   self      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     Union[str, Any]										=    args
            a_    :     Optional[int]										=    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

            with open(self.args.csv_file      ,							newline=""""""    ) as csv_file:
                 a_    :     List[Any]										=    csv.DictReader(lowerCamelCase_    )
                 for row in reader:
                      a_    :     List[str]										=    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_    :     Optional[Any]										=    int(row["""result"""]    )
                      elif can_convert_to_float(row["""result"""]    ):
                           # value is not None
                           a_    :     Dict										=    float(row["""result"""]    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_			,			a_    :     Tuple										=    plt.subplots()
            a_    :     List[Any]										=    """Time usage""" if self.args.is_time else """Memory usage"""
            a_    :     Optional[int]										=    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_    :     List[Any]										=    sorted(set(self.result_dict[model_name]["""bsz"""]    )    )
                 a_    :     Union[str, Any]										=    sorted(set(self.result_dict[model_name]["""seq_len"""]    )    )
                 a_    :     List[str]										=    self.result_dict[model_name]["""result"""]

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

                 a_    :     str										=    (
                     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_    :     Optional[int]										=    np.asarray(
                               [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=lowerCamelCase_      ,							)
                      else:
                           a_    :     int										=    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_))    :     List[str]										=    (
                          ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                      )

                      a_    :     List[Any]										=    np.asarray(lowerCamelCase_      ,							lowerCamelCase_    )[: len(lowerCamelCase_    )]
                      plt.scatter(
                          lowerCamelCase_      ,							lowerCamelCase_      ,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''    )
                      plt.plot(lowerCamelCase_      ,							lowerCamelCase_      ,							"""--"""    )

                 title_str += F''' {label_model_name} vs.'''

            a_    :     int										=    title_str[:-4]
            a_    :     Tuple										=    """Time in s""" if self.args.is_time else """Memory in MB"""

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

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



def  _a			(						):
     a_    :     Tuple										=    HfArgumentParser(__UpperCamelCase )
     a_    :     Dict										=    parser.parse_args_into_dataclasses()[0]
     a_    :     Dict										=    Plot(args=__UpperCamelCase )
     plot.plot()


if __name__ == "__main__":
       main()



