





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
    import torch







class 		_lowerCAmelCase:

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase=14    ,							UpperCAmelCase=7    ,							UpperCAmelCase=True    ,							UpperCAmelCase=True    ,							UpperCAmelCase=False    ,							UpperCAmelCase=True    ,							UpperCAmelCase=99    ,							UpperCAmelCase=32    ,							UpperCAmelCase=5    ,							UpperCAmelCase=4    ,							UpperCAmelCase=37    ,							UpperCAmelCase="gelu"    ,							UpperCAmelCase=0.1    ,							UpperCAmelCase=0.1    ,							UpperCAmelCase=5_12    ,							UpperCAmelCase=0.02    ,							)->   int:
     __A    =  parent
     __A    =  batch_size
     __A    =  seq_length
     __A    =  is_training
     __A    =  use_input_mask
     __A    =  use_token_type_ids
     __A    =  use_labels
     __A    =  vocab_size
     __A    =  hidden_size
     __A    =  num_hidden_layers
     __A    =  num_attention_heads
     __A    =  intermediate_size
     __A    =  hidden_act
     __A    =  hidden_dropout_prob
     __A    =  attention_probs_dropout_prob
     __A    =  max_position_embeddings
     __A    =  initializer_range
     __A    =  None
     __A    =  vocab_size - 1
     __A    =  vocab_size - 1
     __A    =  vocab_size - 1

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     __A    =  ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size      )

     __A    =  None
     if self.use_input_mask:
        __A    =  random_attention_mask([self.batch_size, self.seq_length]      )

     __A    =  GPTaConfig(
         vocab_size=self.vocab_size    ,							n_embd=self.hidden_size    ,							n_layer=self.num_hidden_layers    ,							n_head=self.num_attention_heads    ,							n_positions=self.max_position_embeddings    ,							use_cache=UpperCAmelCase    ,							bos_token_id=self.bos_token_id    ,							eos_token_id=self.eos_token_id    ,							pad_token_id=self.pad_token_id    ,							)

     return (config, input_ids, input_mask)

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     __A    =  self.prepare_config_and_inputs()
     __A   ,		__A   ,		__A    =  config_and_inputs
     __A    =  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Any:
     __A   ,		__A   ,		__A    =  self.prepare_config_and_inputs()

     __A    =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
     __A    =  ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2      )

     return (
         config,
         input_ids,
         attention_mask,
         encoder_hidden_states,
         encoder_attention_mask,
     )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   List[str]:
     __A    =  20
     __A    =  model_class_name(UpperCAmelCase      )

     __A    =  model.init_cache(input_ids.shape[0]    ,							UpperCAmelCase      )
     __A    =  jnp.ones((input_ids.shape[0], max_decoder_length)    ,							dtype='''i4'''      )

     __A    =  jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1      )[None, :]    ,							(input_ids.shape[0], input_ids.shape[-1] - 1)      )
     __A    =  model(
         input_ids[:, :-1]    ,							attention_mask=UpperCAmelCase    ,							past_key_values=UpperCAmelCase    ,							position_ids=UpperCAmelCase    ,							)

     __A    =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,							dtype='''i4'''      )
     __A    =  model(
         input_ids[:, -1:]    ,							attention_mask=UpperCAmelCase    ,							past_key_values=outputs_cache.past_key_values    ,							position_ids=UpperCAmelCase    ,							)

     __A    =  model(UpperCAmelCase      )

     __A    =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
     self.parent.assertTrue(diff < 1e-3    ,							msg=f"Max diff is {diff}"      )





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   List[Any]:
     __A    =  20
     __A    =  model_class_name(UpperCAmelCase      )

     __A    =  jnp.concatenate(
         [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]    ,							axis=-1    ,							)

     __A    =  model.init_cache(input_ids.shape[0]    ,							UpperCAmelCase      )
     __A    =  jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1      )[None, :]    ,							(input_ids.shape[0], input_ids.shape[-1] - 1)      )

     __A    =  model(
         input_ids[:, :-1]    ,							attention_mask=UpperCAmelCase    ,							past_key_values=UpperCAmelCase    ,							position_ids=UpperCAmelCase    ,							)
     __A    =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,							dtype='''i4'''      )
     __A    =  model(
         input_ids[:, -1:]    ,							past_key_values=outputs_cache.past_key_values    ,							attention_mask=UpperCAmelCase    ,							position_ids=UpperCAmelCase    ,							)

     __A    =  model(UpperCAmelCase    ,							attention_mask=UpperCAmelCase      )

     __A    =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
     self.parent.assertTrue(diff < 1e-3    ,							msg=f"Max diff is {diff}"      )







@require_flax
class 		_lowerCAmelCase(		_a						,       _a						,       unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__     	=       (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  lowerCamelCase__     	=       (FlaxGPTaLMHeadModel,) if is_flax_available() else ()

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     __A    =  FlaxGPTaModelTester(self      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     for model_class_name in self.all_model_classes:
        __A   ,		__A   ,		__A    =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward(UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Any:
     for model_class_name in self.all_model_classes:
        __A   ,		__A   ,		__A    =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_use_cache_forward_with_attn_mask(
            UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )

  @slow
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     __A    =  GPTaTokenizer.from_pretrained('''gpt2'''    ,							pad_token='''</s>'''    ,							padding_side='''left'''      )
     __A    =  tokenizer(['''Hello this is a long string''', '''Hey''']    ,							return_tensors='''np'''    ,							padding=UpperCAmelCase    ,							truncation=UpperCAmelCase      )

     __A    =  FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''      )
     __A    =  False
     __A    =  model.config.eos_token_id

     __A    =  jax.jit(model.generate      )

     __A    =  jit_generate(inputs['''input_ids''']    ,							attention_mask=inputs['''attention_mask''']      ).sequences

     __A    =  tokenizer.batch_decode(UpperCAmelCase    ,							skip_special_tokens=UpperCAmelCase      )

     __A    =  [
         '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
         '''Hey, I\'m not sure if I\'m going to be able to do''',
     ]

     self.assertListEqual(UpperCAmelCase    ,							UpperCAmelCase      )

  @is_pt_flax_cross_test
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     __A   ,		__A    =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__      ):
           # prepare inputs
           __A    =  self._prepare_for_class(UpperCAmelCase    ,							UpperCAmelCase      )
           __A    =  {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

           # load corresponding PyTorch class
           __A    =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
           __A    =  getattr(UpperCAmelCase    ,							UpperCAmelCase      )

           __A   ,		__A    =  pt_inputs['''input_ids'''].shape
           __A    =  np.random.randint(0    ,							seq_length - 1    ,							size=(batch_size,)      )
           for batch_idx, start_index in enumerate(UpperCAmelCase      ):
              __A    =  0
              __A    =  1
              __A    =  0
              __A    =  1
           __A    =  pt_model_class(UpperCAmelCase      ).eval()
           __A    =  model_class(UpperCAmelCase    ,							dtype=jnp.floataa      )

           __A    =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,							UpperCAmelCase      )
           __A    =  fx_state

           with torch.no_grad():
              __A    =  pt_model(**UpperCAmelCase      ).to_tuple()

           __A    =  fx_model(**UpperCAmelCase      ).to_tuple()
           self.assertEqual(len(UpperCAmelCase      )    ,							len(UpperCAmelCase      )    ,							'''Output lengths differ between Flax and PyTorch'''      )
           for fx_output, pt_output in zip(UpperCAmelCase    ,							UpperCAmelCase      ):
              self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4e-2      )

           with tempfile.TemporaryDirectory() as tmpdirname:
              pt_model.save_pretrained(UpperCAmelCase      )
              __A    =  model_class.from_pretrained(UpperCAmelCase    ,							from_pt=UpperCAmelCase      )

           __A    =  fx_model_loaded(**UpperCAmelCase      ).to_tuple()
           self.assertEqual(
               len(UpperCAmelCase      )    ,							len(UpperCAmelCase      )    ,							'''Output lengths differ between Flax and PyTorch'''      )
           for fx_output_loaded, pt_output in zip(UpperCAmelCase    ,							UpperCAmelCase      ):
              self.assert_almost_equals(fx_output_loaded[:, -1]    ,							pt_output[:, -1].numpy()    ,							4e-2      )

  @is_pt_flax_cross_test
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     __A   ,		__A    =  self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__      ):
           # prepare inputs
           __A    =  self._prepare_for_class(UpperCAmelCase    ,							UpperCAmelCase      )
           __A    =  {k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

           # load corresponding PyTorch class
           __A    =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
           __A    =  getattr(UpperCAmelCase    ,							UpperCAmelCase      )

           __A    =  pt_model_class(UpperCAmelCase      ).eval()
           __A    =  model_class(UpperCAmelCase    ,							dtype=jnp.floataa      )

           __A    =  load_flax_weights_in_pytorch_model(UpperCAmelCase    ,							fx_model.params      )
           __A   ,		__A    =  pt_inputs['''input_ids'''].shape
           __A    =  np.random.randint(0    ,							seq_length - 1    ,							size=(batch_size,)      )
           for batch_idx, start_index in enumerate(UpperCAmelCase      ):
              __A    =  0
              __A    =  1
              __A    =  0
              __A    =  1

           # make sure weights are tied in PyTorch
           pt_model.tie_weights()

           with torch.no_grad():
              __A    =  pt_model(**UpperCAmelCase      ).to_tuple()

           __A    =  fx_model(**UpperCAmelCase      ).to_tuple()
           self.assertEqual(len(UpperCAmelCase      )    ,							len(UpperCAmelCase      )    ,							'''Output lengths differ between Flax and PyTorch'''      )
           for fx_output, pt_output in zip(UpperCAmelCase    ,							UpperCAmelCase      ):
              self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4e-2      )

           with tempfile.TemporaryDirectory() as tmpdirname:
              fx_model.save_pretrained(UpperCAmelCase      )
              __A    =  pt_model_class.from_pretrained(UpperCAmelCase    ,							from_flax=UpperCAmelCase      )

           with torch.no_grad():
              __A    =  pt_model_loaded(**UpperCAmelCase      ).to_tuple()

           self.assertEqual(
               len(UpperCAmelCase      )    ,							len(UpperCAmelCase      )    ,							'''Output lengths differ between Flax and PyTorch'''      )
           for fx_output, pt_output in zip(UpperCAmelCase    ,							UpperCAmelCase      ):
              self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4e-2      )





  @slow
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     for model_class_name in self.all_model_classes:
        __A    =  model_class_name.from_pretrained('''gpt2'''    ,							from_pt=UpperCAmelCase      )
        __A    =  model(np.ones((1, 1)      )      )
        self.assertIsNotNone(UpperCAmelCase      )








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


_UpperCamelCase       :				Optional[Any]           =			logging.getLogger(__name__)







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''})
  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''Whether to log verbose messages or not.'''}						,       )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		int:


   '''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(snake_case      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







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







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       False
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       "|"
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       None

  @classmethod
  def 						SCREAMING_SNAKE_CASE__			(   cls    ,							UpperCAmelCase      )->   Optional[int]:
     if name == "librispeech":
        return cls()
     if name == "timit":
        return cls(
            do_lower_case=UpperCAmelCase    ,							translation_table=str.maketrans({'''-''': ''' '''}      )    ,							)
     if name == "buckwalter":
        __A    =  {
            '''-''': ''' ''',  # sometimes used to represent pauses
            '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
        }
        return cls(
            vocab_file=pathlib.Path(UpperCAmelCase      ).parent.joinpath('''vocab/buckwalter.json'''      )    ,							word_delimiter_token='''/'''    ,							translation_table=str.maketrans(UpperCAmelCase      )    ,							words_to_remove={'''sil'''}    ,							untransliterator=arabic.buckwalter.untransliterate    ,							)
     raise ValueError(f"Unsupported orthography: '{name}'."      )

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





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   WavaVecaProcessor:
     __A    =  WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir      )
     if self.vocab_file:
        __A    =  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:
        __A    =  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(UpperCAmelCase    ,							UpperCAmelCase      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       42
  lowerCamelCase__     	=       True
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None

  def __call__(   self    ,							UpperCAmelCase      )->   Dict[str, torch.Tensor]:
     # split inputs and labels since they have to be of different lenghts and need
     # different padding methods
     __A    =  [{'''input_values''': feature['''input_values''']} for feature in features]
     __A    =  [{'''input_ids''': feature['''labels''']} for feature in features]

     __A    =  self.processor.pad(
         UpperCAmelCase    ,							padding=self.padding    ,							max_length=self.max_length    ,							pad_to_multiple_of=self.pad_to_multiple_of    ,							return_tensors='''pt'''    ,							)
     __A    =  self.processor.pad(
         labels=UpperCAmelCase    ,							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
     __A    =  labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1      )    ,							-1_00      )

     __A    =  labels

     return batch







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   torch.Tensor:
     model.train()
     __A    =  self._prepare_inputs(UpperCAmelCase      )

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

     if self.args.n_gpu > 1:
        if model.module.config.ctc_loss_reduction == "mean":
           __A    =  loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
           __A    =  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:
        __A    =  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()

     return loss.detach()
def  __UpperCamelCase    (						)    ->		Any:


   '''simple docstring'''







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

   __A   ,		__A   ,		__A    =  parser.parse_args_into_dataclasses()
   configure_logger(snake_case							,			snake_case      )

   __A    =  Orthography.from_name(data_args.orthography.lower()      )
   __A    =  orthography.create_processor(snake_case      )
   __A    =  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      )							,			)

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

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

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

   __A    =  train_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )
   __A    =  val_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )

   if data_args.max_duration_in_seconds is not None:

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

      __A    =  len(snake_case      )
      __A    =  len(snake_case      )
      __A    =  train_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      __A    =  val_dataset.filter(snake_case							,			remove_columns=['''duration_in_seconds''']      )
      if len(snake_case      ) > old_train_size:
         logger.warning(
             F"Filtered out {len(snake_case      ) - old_train_size} train example(s) longer than"
             F" {data_args.max_duration_in_seconds} second(s)."      )
      if len(snake_case      ) > old_val_size:
         logger.warning(
             F"Filtered out {len(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(snake_case      )} train and {len(snake_case      )} validation."      )

   logger.warning(F"Updated {len(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}\""      )
   __A    =  None

   def prepare_dataset(snake_case      ):
      # 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}."

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

   __A    =  train_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)
   __A    =  val_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)

   __A    =  DataCollatorCTCWithPadding(processor=snake_case							,			padding=snake_case      )

   def compute_metrics(snake_case      ):
      __A    =  pred.predictions
      __A    =  np.argmax(snake_case							,			axis=-1      )

      __A    =  processor.tokenizer.pad_token_id

      __A    =  processor.batch_decode(snake_case      )
      # we do not want to group tokens when computing the metrics
      __A    =  processor.batch_decode(pred.label_ids							,			group_tokens=snake_case      )
      if logger.isEnabledFor(logging.DEBUG      ):
         for reference, predicted in zip(snake_case							,			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(snake_case      )}\""      )
               logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )

      __A    =  wer_metric.compute(predictions=snake_case							,			references=snake_case      )

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   __A    =  CTCTrainer(
       model=snake_case							,			data_collator=snake_case							,			args=snake_case							,			compute_metrics=snake_case							,			train_dataset=snake_case							,			eval_dataset=snake_case							,			tokenizer=processor.feature_extractor							,			)

   trainer.train()


if __name__ == "__main__":
    main()

