

'''simple docstring'''




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_a				: Optional[int] 							=						logging.get_logger()



@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: nn.Module
  __lowerCAmelCase					: List[nn.Module]      =   field(default_factory=a )
  __lowerCAmelCase					: list      =   field(default_factory=a )
  __lowerCAmelCase					: Dict[str, nn.Module]      =   field(default_factory=a )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	len(list(m.modules()     )     ) == 1 or isinstance(a_   , nn.Convad     ) or isinstance(a_   , nn.BatchNormad     )
    if has_not_submodules:
      self.traced.append(a_     )
      UpperCAmelCase													=	m
  def __call__(     self   , a_     )							->				List[Any]:


    """simple docstring"""
    for name, m in self.module.named_modules():
      self.handles.append(m.register_forward_hook(partial(self._forward_hook   , name=a_     )     )     )
    self.module(a_     )
    [x.remove() for x in self.handles]
    return self





  @property
  def 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()     )     ) > 0}




class        lowercase_	(		nn.Module ):

  '''simple docstring'''





  def __init__(     self   , a_     )							->				Tuple:


    """simple docstring"""
    super().__init__()

    UpperCAmelCase													=	[]
    # - get the stem
    feature_blocks.append(('conv1', model.stem)     )
    # - get all the feature blocks
    for k, v in model.trunk_output.named_children():
      assert k.startswith('block'     ), F'''Unexpected layer name {k}'''
      UpperCAmelCase													=	len(a_     ) + 1
      feature_blocks.append((F'''res{block_index}''', v)     )

    UpperCAmelCase													=	nn.ModuleDict(a_     )





  def 						snake_case_			(     self   , a_     )							->				Optional[Any]:


    """simple docstring"""
    return get_trunk_forward_outputs(
        a_   , out_feat_keys=a_   , feature_blocks=self._feature_blocks   , )




class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str						):
  UpperCAmelCase													=	RegNetConfig(depths=[2, 7, 17, 1]       ,   hidden_sizes=[8, 8, 8, 8]       ,   groups_width=8						)
  if "in1k" in model_name:
    UpperCAmelCase													=	RegNetForImageClassification(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	RegNetModel(SCREAMING_SNAKE_CASE						)
  # create from model (with small weights)
  UpperCAmelCase													=	FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27       ,   group_width=1010       ,   w_a=1744       ,   w_a=620.83       ,   w_m=2.52						)						)						)

  with torch.no_grad():
    UpperCAmelCase													=	from_model.eval()
    UpperCAmelCase													=	our_model.eval()

    UpperCAmelCase													=	torch.randn((1, 3, 32, 32)						)
    # trace both
    UpperCAmelCase													=	Tracker(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	dest_tracker(SCREAMING_SNAKE_CASE						).parametrized

    pprint(dest_tracker.nameamodule						)
    UpperCAmelCase													=	Tracker(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	src_tracker(SCREAMING_SNAKE_CASE						).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(SCREAMING_SNAKE_CASE      :   Tuple						):
    UpperCAmelCase													=	OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        UpperCAmelCase													=	param
    return params_dict

  UpperCAmelCase													=	{}

  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	to_params_dict(SCREAMING_SNAKE_CASE						)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,   dst_state_dict.items()						):
    UpperCAmelCase													=	dest_key
    logger.info(f'''{src_key} -> {dest_key}'''						)
  # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  if "in1k" in model_name:
    UpperCAmelCase													=	'classifier.1.weight'
    UpperCAmelCase													=	'classifier.1.bias'

  return from_to_ours_keys


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Path       ,   SCREAMING_SNAKE_CASE      :   str = None       ,   SCREAMING_SNAKE_CASE      :   bool = True						):
  UpperCAmelCase													=	'imagenet-1k-id2label.json'
  UpperCAmelCase													=	1000

  UpperCAmelCase													=	'huggingface/label-files'
  UpperCAmelCase													=	num_labels
  UpperCAmelCase													=	json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   repo_type='dataset'						)						)       ,   'r'						)						)
  UpperCAmelCase													=	{int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}

  UpperCAmelCase													=	idalabel
  UpperCAmelCase													=	{v: k for k, v in idalabel.items()}

  UpperCAmelCase													=	partial(SCREAMING_SNAKE_CASE       ,   num_labels=SCREAMING_SNAKE_CASE       ,   idalabel=SCREAMING_SNAKE_CASE       ,   labelaid=SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	{
      'regnet-y-10b-seer': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
      # finetuned on imagenet
      'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]       ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280]       ,   groups_width=1010						),
  }

  # add seer weights logic
  def load_using_classy_vision(SCREAMING_SNAKE_CASE      :   str						) -> Tuple[Dict, Dict]:
    UpperCAmelCase													=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE       ,   model_dir=str(SCREAMING_SNAKE_CASE						)       ,   map_location='cpu'						)
    # check if we have a head, if yes add it
    UpperCAmelCase													=	files['classy_state_dict']['base_model']['model']
    return model_state_dict["trunk"], model_state_dict["heads"]

  UpperCAmelCase													=	{
      'regnet-y-10b-seer': partial(
          SCREAMING_SNAKE_CASE       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,   ),
      'regnet-y-10b-seer-in1k': partial(
          SCREAMING_SNAKE_CASE       ,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,   ),
  }

  UpperCAmelCase													=	get_from_to_our_keys(SCREAMING_SNAKE_CASE						)

  if not (save_directory / f'''{model_name}.pth''').exists():
    logger.info('Loading original state_dict.'						)
    UpperCAmelCase							,      UpperCAmelCase													=	names_to_from_model[model_name]()
    UpperCAmelCase													=	from_state_dict_trunk
    if "in1k" in model_name:
      # add the head
      UpperCAmelCase													=	{**from_state_dict_trunk, **from_state_dict_head}
    logger.info('Done!'						)

    UpperCAmelCase													=	{}

    UpperCAmelCase													=	list(from_state_dict.keys()						)
    UpperCAmelCase													=	R'\.block.-part.'
    # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
    for key in from_state_dict.keys():
      # remove the weird "block[0,1]-part" from the key
      UpperCAmelCase													=	re.sub(SCREAMING_SNAKE_CASE       ,   ''       ,   SCREAMING_SNAKE_CASE						)
      # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
      UpperCAmelCase													=	from_to_ours_keys[src_key]
      # store the parameter with our key
      UpperCAmelCase													=	from_state_dict[key]
      not_used_keys.remove(SCREAMING_SNAKE_CASE						)
    # check that all keys have been updated
    assert len(SCREAMING_SNAKE_CASE						) == 0, f'''Some keys where not used {','.join(SCREAMING_SNAKE_CASE						)}'''

    logger.info(f'''The following keys were not used: {','.join(SCREAMING_SNAKE_CASE						)}'''						)

    # save our state dict to disk
    torch.save(SCREAMING_SNAKE_CASE       ,   save_directory / f'''{model_name}.pth'''						)

    del converted_state_dict
  else:
    logger.info('The state_dict was already stored on disk.'						)
  if push_to_hub:
    logger.info(f'''Token is {os.environ['HF_TOKEN']}'''						)
    logger.info('Loading our model.'						)
    # create our model
    UpperCAmelCase													=	names_to_config[model_name]
    UpperCAmelCase													=	RegNetModel
    if "in1k" in model_name:
      UpperCAmelCase													=	RegNetForImageClassification
    UpperCAmelCase													=	our_model_func(SCREAMING_SNAKE_CASE						)
    # place our model to the meta device (so remove all the weights)
    our_model.to(torch.device('meta'						)						)
    logger.info('Loading state_dict in our model.'						)
    # load state dict
    UpperCAmelCase													=	our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   [save_directory / f'''{model_name}.pth''']						)
    logger.info('Finally, pushing!'						)
    # push it to hub
    our_model.push_to_hub(
        repo_path_or_name=save_directory / model_name       ,   commit_message='Add model'       ,   output_dir=save_directory / model_name       ,   )
    UpperCAmelCase													=	384
    # we can use the convnext one
    UpperCAmelCase													=	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,   size=SCREAMING_SNAKE_CASE						)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / model_name       ,   commit_message='Add image processor'       ,   output_dir=save_directory / model_name       ,   )


if __name__ == "__main__":
  _a				: Dict 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help=(
          'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
          ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=Path,
      required=True,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      default=True,
      type=bool,
      required=False,
      help='If True, push model and image processor to the hub.',
  )

  _a				: Union[str, Any] 							=						parser.parse_args()

  _a				: Path 							=						args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''




import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
  from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
  _a				: int 							=						True
  from torch.cuda.amp import autocast


_a				: Optional[int] 							=						logging.getLogger(__name__)



@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: str      =   field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
  __lowerCAmelCase					: Optional[str]      =   field(
      default=a ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,       )
  __lowerCAmelCase					: Optional[bool]      =   field(
      default=a ,       metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
  __lowerCAmelCase					: Optional[bool]      =   field(
      default=a ,       metadata={"help": "Whether to log verbose messages or not."} ,       )




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   ModelArguments       ,   SCREAMING_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(SCREAMING_SNAKE_CASE						)



@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: str      =   field(
      default=a ,       metadata={"help": "The name of the dataset to use (via the datasets library)."} )
  __lowerCAmelCase					: Optional[str]      =   field(
      default=a ,       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="text" ,       metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"} ,       )
  __lowerCAmelCase					: Optional[str]      =   field(
      default="file" ,       metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,       )
  __lowerCAmelCase					: Optional[bool]      =   field(
      default=a ,       metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."} ,       )
  __lowerCAmelCase					: Optional[float]      =   field(
      default=a ,       metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."} ,       )
  __lowerCAmelCase					: Optional[str]      =   field(
      default="librispeech" ,       metadata={
          "help": (
              "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
              " 'buckwalter'."
          )
      } ,       )
  __lowerCAmelCase					: bool      =   field(
      default=a ,       metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
  __lowerCAmelCase					: Optional[int]      =   field(
      default=a ,       metadata={"help": "The number of processes to use for the preprocessing."} ,       )




@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: bool      =   False
  __lowerCAmelCase					: Optional[str]      =   None
  __lowerCAmelCase					: Optional[str]      =   "|"
  __lowerCAmelCase					: Optional[Dict[str, str]]      =   field(default_factory=a )
  __lowerCAmelCase					: Optional[Set[str]]      =   field(default_factory=a )
  __lowerCAmelCase					: Optional[Callable[[str], str]]      =   None
  @classmethod
  def 						snake_case_			(     cls   , a_     )							->				Optional[Any]:


    """simple docstring"""
    if name == "librispeech":
      return cls()
    if name == "timit":
      return cls(
          do_lower_case=a_   , translation_table=str.maketrans({'-': ' '}     )   , )
    if name == "buckwalter":
      UpperCAmelCase													=	{
          '-': ' ',  # sometimes used to represent pauses
          '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
      }
      return cls(
          vocab_file=pathlib.Path(a_     ).parent.joinpath('vocab/buckwalter.json'     )   , word_delimiter_token='/'   , translation_table=str.maketrans(a_     )   , words_to_remove={'sil'}   , untransliterator=arabic.buckwalter.untransliterate   , )
    raise ValueError(F'''Unsupported orthography: \'{name}\'.'''     )
  def 						snake_case_			(     self   , a_     )							->				str:


    """simple docstring"""
    # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
    if len(self.translation_table     ) > 0:
      UpperCAmelCase													=	text.translate(self.translation_table     )
    if len(self.words_to_remove     ) == 0:
      UpperCAmelCase													=	' '.join(text.split()     )  # clean up whitespaces
    else:
      UpperCAmelCase													=	' '.join(w for w in text.split() if w not in self.words_to_remove     )  # and clean up whilespaces
    return text





  def 						snake_case_			(     self   , a_     )							->				WavaVecaProcessor:


    """simple docstring"""
    UpperCAmelCase													=	WavaVecaFeatureExtractor.from_pretrained(
        model_args.model_name_or_path   , cache_dir=model_args.cache_dir     )
    if self.vocab_file:
      UpperCAmelCase													=	WavaVecaCTCTokenizer(
          self.vocab_file   , cache_dir=model_args.cache_dir   , do_lower_case=self.do_lower_case   , word_delimiter_token=self.word_delimiter_token   , )
    else:
      UpperCAmelCase													=	WavaVecaCTCTokenizer.from_pretrained(
          model_args.model_name_or_path   , cache_dir=model_args.cache_dir   , do_lower_case=self.do_lower_case   , word_delimiter_token=self.word_delimiter_token   , )
    return WavaVecaProcessor(a_   , a_     )




@dataclass
class        lowercase_	:

  '''simple docstring'''





  __lowerCAmelCase					: WavaVecaProcessor
  __lowerCAmelCase					: Union[bool, str]      =   True
  __lowerCAmelCase					: Optional[int]      =   None
  __lowerCAmelCase					: Optional[int]      =   None
  __lowerCAmelCase					: Optional[int]      =   None
  __lowerCAmelCase					: Optional[int]      =   None
  def __call__(     self   , a_     )							->				Dict[str, torch.Tensor]:


    """simple docstring"""
    # split inputs and labels since they have to be of different lenghts and need
    # different padding methods
    UpperCAmelCase													=	[{'input_values': feature['input_values']} for feature in features]
    UpperCAmelCase													=	[{'input_ids': feature['labels']} for feature in features]

    UpperCAmelCase													=	self.processor.pad(
        a_   , padding=self.padding   , max_length=self.max_length   , pad_to_multiple_of=self.pad_to_multiple_of   , return_tensors='pt'   , )
    UpperCAmelCase													=	self.processor.pad(
        labels=a_   , padding=self.padding   , max_length=self.max_length_labels   , pad_to_multiple_of=self.pad_to_multiple_of_labels   , return_tensors='pt'   , )

    # replace padding with -100 to ignore loss correctly
    UpperCAmelCase													=	labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1     )   , -1_0_0     )

    UpperCAmelCase													=	labels

    return batch




class        lowercase_	(		a ):

  '''simple docstring'''





  def 						snake_case_			(     self   , a_   , a_     )							->				torch.Tensor:


    """simple docstring"""
    model.train()
    UpperCAmelCase													=	self._prepare_inputs(a_     )

    if self.use_amp:
      with autocast():
        UpperCAmelCase													=	self.compute_loss(a_   , a_     )
    else:
      UpperCAmelCase													=	self.compute_loss(a_   , a_     )

    if self.args.n_gpu > 1:
      if model.module.config.ctc_loss_reduction == "mean":
        UpperCAmelCase													=	loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
        UpperCAmelCase													=	loss.sum() / (inputs['labels'] >= 0).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(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()

    return loss.detach()




def       lowerCamelCase__		(						):
  # 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(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	Orthography.from_name(data_args.orthography.lower()						)
  UpperCAmelCase													=	orthography.create_processor(SCREAMING_SNAKE_CASE						)
  UpperCAmelCase													=	WavaVecaForCTC.from_pretrained(
      model_args.model_name_or_path       ,   cache_dir=model_args.cache_dir       ,   gradient_checkpointing=training_args.gradient_checkpointing       ,   vocab_size=len(processor.tokenizer						)       ,   )

  UpperCAmelCase													=	datasets.load_dataset(
      data_args.dataset_name       ,   data_args.dataset_config_name       ,   split=data_args.train_split_name						)
  UpperCAmelCase													=	datasets.load_dataset(
      data_args.dataset_name       ,   data_args.dataset_config_name       ,   split=data_args.validation_split_name						)

  UpperCAmelCase													=	datasets.load_metric('wer'						)
  UpperCAmelCase													=	processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
  UpperCAmelCase													=	''.join(t for t in processor.tokenizer.get_vocab().keys() if len(SCREAMING_SNAKE_CASE						) == 1						)
  UpperCAmelCase													=	re.compile(  # remove characters not in vocabulary
      Rf'''[^\s{re.escape(SCREAMING_SNAKE_CASE						)}]'''       ,   flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0       ,   )
  UpperCAmelCase													=	[]

  def prepare_example(SCREAMING_SNAKE_CASE      :   Dict						):  # TODO(elgeish) make use of multiprocessing?
    UpperCAmelCase							,      UpperCAmelCase													=	librosa.load(example[data_args.speech_file_column]       ,   sr=SCREAMING_SNAKE_CASE						)
    if data_args.max_duration_in_seconds is not None:
      UpperCAmelCase													=	len(example['speech']						) / example['sampling_rate']
    # Normalize and clean up text; order matters!
    UpperCAmelCase													=	orthography.preprocess_for_training(example[data_args.target_text_column]						)
    UpperCAmelCase													=	vocabulary_text_cleaner.sub(''       ,   SCREAMING_SNAKE_CASE						)
    if updated_text != example[data_args.target_text_column]:
      text_updates.append((example[data_args.target_text_column], updated_text)						)
      UpperCAmelCase													=	updated_text
    return example

  UpperCAmelCase													=	train_dataset.map(SCREAMING_SNAKE_CASE       ,   remove_columns=[data_args.speech_file_column]						)
  UpperCAmelCase													=	val_dataset.map(SCREAMING_SNAKE_CASE       ,   remove_columns=[data_args.speech_file_column]						)

  if data_args.max_duration_in_seconds is not None:

    def filter_by_max_duration(SCREAMING_SNAKE_CASE      :   int						):
      return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

    UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)
    UpperCAmelCase													=	train_dataset.filter(SCREAMING_SNAKE_CASE       ,   remove_columns=['duration_in_seconds']						)
    UpperCAmelCase													=	val_dataset.filter(SCREAMING_SNAKE_CASE       ,   remove_columns=['duration_in_seconds']						)
    if len(SCREAMING_SNAKE_CASE						) > old_train_size:
      logger.warning(
          f'''Filtered out {len(SCREAMING_SNAKE_CASE						) - old_train_size} train example(s) longer than'''
          f''' {data_args.max_duration_in_seconds} second(s).'''						)
    if len(SCREAMING_SNAKE_CASE						) > old_val_size:
      logger.warning(
          f'''Filtered out {len(SCREAMING_SNAKE_CASE						) - old_val_size} validation example(s) longer than'''
          f''' {data_args.max_duration_in_seconds} second(s).'''						)
  logger.info(f'''Split sizes: {len(SCREAMING_SNAKE_CASE						)} train and {len(SCREAMING_SNAKE_CASE						)} validation.'''						)

  logger.warning(f'''Updated {len(SCREAMING_SNAKE_CASE						)} transcript(s) using \'{data_args.orthography}\' orthography rules.'''						)
  if logger.isEnabledFor(logging.DEBUG						):
    for original_text, updated_text in text_updates:
      logger.debug(f'''Updated text: "{original_text}" -> "{updated_text}"'''						)
  UpperCAmelCase													=	None

  def prepare_dataset(SCREAMING_SNAKE_CASE      :   Optional[Any]						):
    # check that all files have the correct sampling rate
    assert (
        len(set(batch['sampling_rate']						)						) == 1
    ), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

    UpperCAmelCase													=	processor(
        audio=batch['speech']       ,   text=batch[data_args.target_text_column]       ,   sampling_rate=batch['sampling_rate'][0]						)
    batch.update(SCREAMING_SNAKE_CASE						)
    return batch

  UpperCAmelCase													=	train_dataset.map(
      SCREAMING_SNAKE_CASE       ,   batch_size=training_args.per_device_train_batch_size       ,   batched=SCREAMING_SNAKE_CASE       ,   num_proc=data_args.preprocessing_num_workers       ,   )
  UpperCAmelCase													=	val_dataset.map(
      SCREAMING_SNAKE_CASE       ,   batch_size=training_args.per_device_train_batch_size       ,   batched=SCREAMING_SNAKE_CASE       ,   num_proc=data_args.preprocessing_num_workers       ,   )

  UpperCAmelCase													=	DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE       ,   padding=SCREAMING_SNAKE_CASE						)

  def compute_metrics(SCREAMING_SNAKE_CASE      :   Optional[int]						):
    UpperCAmelCase													=	pred.predictions
    UpperCAmelCase													=	np.argmax(SCREAMING_SNAKE_CASE       ,   axis=-1						)

    UpperCAmelCase													=	processor.tokenizer.pad_token_id

    UpperCAmelCase													=	processor.batch_decode(SCREAMING_SNAKE_CASE						)
    # we do not want to group tokens when computing the metrics
    UpperCAmelCase													=	processor.batch_decode(pred.label_ids       ,   group_tokens=SCREAMING_SNAKE_CASE						)
    if logger.isEnabledFor(logging.DEBUG						):
      for reference, predicted in zip(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
        logger.debug(f'''reference: "{reference}"'''						)
        logger.debug(f'''predicted: "{predicted}"'''						)
        if orthography.untransliterator is not None:
          logger.debug(f'''reference (untransliterated): "{orthography.untransliterator(SCREAMING_SNAKE_CASE						)}"'''						)
          logger.debug(f'''predicted (untransliterated): "{orthography.untransliterator(SCREAMING_SNAKE_CASE						)}"'''						)

    UpperCAmelCase													=	wer_metric.compute(predictions=SCREAMING_SNAKE_CASE       ,   references=SCREAMING_SNAKE_CASE						)

    return {"wer": wer}

  if model_args.freeze_feature_extractor:
    model.freeze_feature_extractor()

  UpperCAmelCase													=	CTCTrainer(
      model=SCREAMING_SNAKE_CASE       ,   data_collator=SCREAMING_SNAKE_CASE       ,   args=SCREAMING_SNAKE_CASE       ,   compute_metrics=SCREAMING_SNAKE_CASE       ,   train_dataset=SCREAMING_SNAKE_CASE       ,   eval_dataset=SCREAMING_SNAKE_CASE       ,   tokenizer=processor.feature_extractor       ,   )

  trainer.train()


if __name__ == "__main__":
  main()


