code: 
  
  import argparse
  
  import torch
  from clip import load
  
  from transformers import CLIPConfig, CLIPModel
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     List[Any]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			pt_attn_layer.in_proj_weight.chunk(3       ,   dim=0      )
      __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			pt_attn_layer.in_proj_bias.chunk(3       ,   dim=0      )
  
      __UpperCAmelCase									=			pt_attn_layer.out_proj.weight
      __UpperCAmelCase									=			pt_attn_layer.out_proj.bias
  
      __UpperCAmelCase									=			q_proj
      __UpperCAmelCase									=			q_proj_bias
  
      __UpperCAmelCase									=			k_proj
      __UpperCAmelCase									=			k_proj_bias
  
      __UpperCAmelCase									=			v_proj
      __UpperCAmelCase									=			v_proj_bias
  
      __UpperCAmelCase									=			out_proj_weights
      __UpperCAmelCase									=			out_proj_bias
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
      copy_linear(hf_mlp.fca       ,   pt_mlp.c_fc      )
      copy_linear(hf_mlp.fca       ,   pt_mlp.c_proj      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     str:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			pt_linear.weight
      __UpperCAmelCase									=			pt_linear.bias
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     str:
  
  
  
  
  
  
      '''simple docstring'''
      # copy layer norms
      copy_linear(hf_layer.layer_norma       ,   pt_layer.ln_a      )
      copy_linear(hf_layer.layer_norma       ,   pt_layer.ln_a      )
  
      # copy MLP
      copy_mlp(hf_layer.mlp       ,   pt_layer.mlp      )
  
      # copy attn
      copy_attn_layer(hf_layer.self_attn       ,   pt_layer.attn      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
      for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
          copy_layer(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
      # copy  embeds
      __UpperCAmelCase									=			pt_model.token_embedding.weight
      __UpperCAmelCase									=			pt_model.positional_embedding
  
      # copy layer norm
      copy_linear(hf_encoder.final_layer_norm       ,   pt_model.ln_final      )
  
      # copy hidden layers
      copy_layers(hf_encoder.encoder.layers       ,   pt_model.transformer.resblocks      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      # copy projection
      __UpperCAmelCase									=			pt_model.text_projection.data.T
  
      # copy text encoder
      copy_encoder(hf_model.text_model       ,   SCREAMING_SNAKE_CASE      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Any:
  
  
  
  
  
  
      '''simple docstring'''
      # copy projection
      __UpperCAmelCase									=			pt_model.visual.proj.data.T
  
      # copy layer norms
      copy_linear(hf_model.vision_model.pre_layrnorm       ,   pt_model.visual.ln_pre      )
      copy_linear(hf_model.vision_model.post_layernorm       ,   pt_model.visual.ln_post      )
  
      # copy embeds
      __UpperCAmelCase									=			pt_model.visual.conva.weight.data
      __UpperCAmelCase									=			pt_model.visual.class_embedding
      __UpperCAmelCase									=			pt_model.visual.positional_embedding.data
  
      # copy encoder
      copy_layers(hf_model.vision_model.encoder.layers       ,   pt_model.visual.transformer.resblocks      )
  
  
  @torch.no_grad()
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE=None      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      if config_path is not None:
          __UpperCAmelCase									=			CLIPConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
      else:
          __UpperCAmelCase									=			CLIPConfig(projection_dim=5_1_2       ,   text_config={}       ,   vision_config={}      )
  
      __UpperCAmelCase									=			CLIPModel(SCREAMING_SNAKE_CASE      ).eval()
  
      __UpperCAmelCase					,     __UpperCAmelCase									=			load(SCREAMING_SNAKE_CASE       ,   device='''cpu'''       ,   jit=SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			pt_model.eval()
  
      copy_text_model_and_projection(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
      copy_vison_model_and_projection(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			pt_model.logit_scale
  
      __UpperCAmelCase									=			torch.arange(0       ,   7_7      ).unsqueeze(0      )
      __UpperCAmelCase									=			torch.randn(1       ,   3       ,   2_2_4       ,   2_2_4      )
  
      __UpperCAmelCase									=			hf_model(input_ids=SCREAMING_SNAKE_CASE       ,   pixel_values=SCREAMING_SNAKE_CASE       ,   return_dict=SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			hf_outputs.logits_per_image
      __UpperCAmelCase									=			hf_outputs.logits_per_text
      __UpperCAmelCase					,     __UpperCAmelCase									=			pt_model(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
      assert torch.allclose(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   atol=1e-3      )
      assert torch.allclose(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   atol=1e-3      )
  
      hf_model.save_pretrained(SCREAMING_SNAKE_CASE      )
  
  
  if __name__ == "__main__":
         A_		:			Any										=   argparse.ArgumentParser()
         parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
         parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
         parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
         A_		:			Dict										=   parser.parse_args()
  
         convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
  
  
  
  
  
style_context: 
  
  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_		:			Optional[int]										=   True
         from torch.cuda.amp import autocast
  
  
  A_		:			List[Any]										=   logging.getLogger(__name__)
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Whether to freeze the feature extractor layers of the model."}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Whether to log verbose messages or not."} ,     )
  
  
  
  
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     str:
  
  
  
  
  
  
      '''simple docstring'''
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,   datefmt='''%m/%d/%Y %H:%M:%S'''       ,   handlers=[logging.StreamHandler(sys.stdout      )]       ,   )
      __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 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       field(
          default=_a ,     metadata={"help": "The name of the dataset to use (via the datasets library)."}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
      a__     							=       field(
          default="train" ,     metadata={
              "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
          } ,     )
      a__     							=       field(
          default="validation" ,     metadata={
              "help": (
                  "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
              )
          } ,     )
      a__     							=       field(
          default="text" ,     metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"} ,     )
      a__     							=       field(
          default="file" ,     metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."} ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."} ,     )
      a__     							=       field(
          default="librispeech" ,     metadata={
              "help": (
                  "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
                  " 'buckwalter'."
              )
          } ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Overwrite the cached preprocessed datasets or not."}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "The number of processes to use for the preprocessing."} ,     )
  
  
  
  
  
  
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       False
      a__     							=       None
      a__     							=       "|"
      a__     							=       field(default_factory=_a						)
      a__     							=       field(default_factory=_a						)
      a__     							=       None
  
  
      @classmethod
      def      lowerCAmelCase_						(cls     ,      lowercase__  )     ->      Union[str, Any]:
          if name == "librispeech":
              return cls()
          if name == "timit":
              return cls(
                  do_lower_case=lowercase__     ,      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(lowercase__  ).parent.joinpath('''vocab/buckwalter.json'''  )     ,      word_delimiter_token='''/'''     ,      translation_table=str.maketrans(lowercase__  )     ,      words_to_remove={'''sil'''}     ,      untransliterator=arabic.buckwalter.untransliterate     ,      )
          raise ValueError(F'''Unsupported orthography: \'{name}\'.'''  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      str:
          # 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      lowerCAmelCase_						(self     ,      lowercase__  )     ->      WavaVecaProcessor:
          __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(lowercase__     ,      lowercase__  )
  
  
  
  
  
  
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       42
      a__     							=       True
      a__     							=       None
      a__     							=       None
      a__     							=       None
      a__     							=       None
  
  
      def __call__(self     ,      lowercase__  )     ->      Dict[str, torch.Tensor]:
          # 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(
              lowercase__     ,      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=lowercase__     ,      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  )     ,      -100  )
  
          __UpperCAmelCase									=			labels
  
          return batch
  
  
  
  
  
  
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      torch.Tensor:
          model.train()
          __UpperCAmelCase									=			self._prepare_inputs(lowercase__  )
  
          if self.use_amp:
              with autocast():
                  __UpperCAmelCase									=			self.compute_loss(lowercase__     ,      lowercase__  )
          else:
              __UpperCAmelCase									=			self.compute_loss(lowercase__     ,      lowercase__  )
  
          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(lowercase__  ).backward()
          elif self.use_apex:
              with amp.scale_loss(lowercase__     ,      self.optimizer  ) as scaled_loss:
                  scaled_loss.backward()
          elif self.deepspeed:
              self.deepspeed.backward(lowercase__  )
          else:
              loss.backward()
  
          return loss.detach()
  
  
  
  
  
  
  def       __a       (   )      ->     int:
  
  
  
  
  
  
      '''simple docstring'''
      # 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      ):  # 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      ):
              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      ):
          # 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      ):
          __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()
  
  
  
  
  