



from __future__ import annotations

import unittest
from collections.abc import Iterator







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     str  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Node | None							)	->      None:
       UpperCAmelCase_     :       Tuple  		=					label
       UpperCAmelCase_     :       Dict  		=					parent
       UpperCAmelCase_     :       Node | None  		=					None
       UpperCAmelCase_     :       Node | None  		=					None







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Union[str, Any]							)	->      None:
       UpperCAmelCase_     :       Node | None  		=					None


   def       A__    (    self:     Tuple							)	->      None:
       UpperCAmelCase_     :       List[str]  		=					None


   def       A__    (    self:     Union[str, Any]							)	->      bool:
       return self.root is None


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     int							)	->      None:
       UpperCAmelCase_     :       List[Any]  		=					self._put(self.root  ,lowerCamelCase_							)


   def       A__    (    self:     int  ,lowerCamelCase_:     Node | None  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Node | None = None							)	->      Node:
       if node is None:
           UpperCAmelCase_     :       Tuple  		=					Node(lowerCamelCase_  ,lowerCamelCase_							)
       else:
           if label < node.label:
               UpperCAmelCase_     :       Tuple  		=					self._put(node.left  ,lowerCamelCase_  ,lowerCamelCase_							)
           elif label > node.label:
               UpperCAmelCase_     :       int  		=					self._put(node.right  ,lowerCamelCase_  ,lowerCamelCase_							)
           else:
               UpperCAmelCase_     :       Optional[int]  		=					F'''Node with label {label} already exists'''
               raise Exception(lowerCamelCase_							)

       return node


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     int							)	->      Node:
       return self._search(self.root  ,lowerCamelCase_							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Node | None  ,lowerCamelCase_:     int							)	->      Node:
       if node is None:
           UpperCAmelCase_     :       Dict  		=					F'''Node with label {label} does not exist'''
           raise Exception(lowerCamelCase_							)
       else:
           if label < node.label:
               UpperCAmelCase_     :       Optional[Any]  		=					self._search(node.left  ,lowerCamelCase_							)
           elif label > node.label:
               UpperCAmelCase_     :       str  		=					self._search(node.right  ,lowerCamelCase_							)

       return node


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     int							)	->      None:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.search(lowerCamelCase_							)
       if node.right and node.left:
           UpperCAmelCase_     :       List[Any]  		=					self._get_lowest_node(node.right							)
           UpperCAmelCase_     :       str  		=					node.left
           UpperCAmelCase_     :       Any  		=					node.right
           UpperCAmelCase_     :       List[str]  		=					lowest_node
           if node.right:
               UpperCAmelCase_     :       Tuple  		=					lowest_node
           self._reassign_nodes(lowerCamelCase_  ,lowerCamelCase_							)
       elif not node.right and node.left:
           self._reassign_nodes(lowerCamelCase_  ,node.left							)
       elif node.right and not node.left:
           self._reassign_nodes(lowerCamelCase_  ,node.right							)
       else:
           self._reassign_nodes(lowerCamelCase_  ,lowerCamelCase_							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Node  ,lowerCamelCase_:     Node | None							)	->      None:
       if new_children:
           UpperCAmelCase_     :       Dict  		=					node.parent

       if node.parent:
           if node.parent.right == node:
               UpperCAmelCase_     :       Tuple  		=					new_children
           else:
               UpperCAmelCase_     :       Optional[int]  		=					new_children
       else:
           UpperCAmelCase_     :       List[str]  		=					new_children


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     Node							)	->      Node:
       if node.left:
           UpperCAmelCase_     :       Any  		=					self._get_lowest_node(node.left							)
       else:
           UpperCAmelCase_     :       List[Any]  		=					node
           self._reassign_nodes(lowerCamelCase_  ,node.right							)

       return lowest_node


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     int							)	->      bool:
       try:
           self.search(lowerCamelCase_							)
           return True
       except Exception:
           return False


   def       A__    (    self:     Optional[int]							)	->      int:
       if self.root is None:
           raise Exception("""Binary search tree is empty"""							)

       UpperCAmelCase_     :       List[Any]  		=					self.root
       while node.right is not None:
           UpperCAmelCase_     :       Any  		=					node.right

       return node.label


   def       A__    (    self:     str							)	->      int:
       if self.root is None:
           raise Exception("""Binary search tree is empty"""							)

       UpperCAmelCase_     :       Dict  		=					self.root
       while node.left is not None:
           UpperCAmelCase_     :       Optional[int]  		=					node.left

       return node.label


   def       A__    (    self:     List[Any]							)	->      Iterator[Node]:
       return self._inorder_traversal(self.root							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     Node | None							)	->      Iterator[Node]:
       if node is not None:
           yield from self._inorder_traversal(node.left							)
           yield node
           yield from self._inorder_traversal(node.right							)


   def       A__    (    self:     List[str]							)	->      Iterator[Node]:
       return self._preorder_traversal(self.root							)






   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Node | None							)	->      Iterator[Node]:
       if node is not None:
           yield node
           yield from self._preorder_traversal(node.left							)
           yield from self._preorder_traversal(node.right							)







class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   @staticmethod
   def       A__    (    )	->      BinarySearchTree:
       UpperCAmelCase_     :       Tuple  		=					BinarySearchTree()
       t.put(8							)
       t.put(3							)
       t.put(6							)
       t.put(1							)
       t.put(10							)
       t.put(14							)
       t.put(13							)
       t.put(4							)
       t.put(7							)
       t.put(5							)

       return t


   def       A__    (    self:     int							)	->      None:
       UpperCAmelCase_     :       List[str]  		=					BinarySearchTree()
       assert t.is_empty()

       t.put(8							)
       assert t.root is not None
       assert t.root.parent is None
       assert t.root.label == 8

       t.put(10							)
       assert t.root.right is not None
       assert t.root.right.parent == t.root
       assert t.root.right.label == 10

       t.put(3							)
       assert t.root.left is not None
       assert t.root.left.parent == t.root
       assert t.root.left.label == 3

       t.put(6							)
       assert t.root.left.right is not None
       assert t.root.left.right.parent == t.root.left
       assert t.root.left.right.label == 6

       t.put(1							)
       assert t.root.left.left is not None
       assert t.root.left.left.parent == t.root.left
       assert t.root.left.left.label == 1

       with self.assertRaises(lowerCamelCase_							):  # noqa: B017
           t.put(1							)


   def       A__    (    self:     Dict							)	->      None:
       UpperCAmelCase_     :       List[Any]  		=					self._get_binary_search_tree()

       UpperCAmelCase_     :       Dict  		=					t.search(6							)
       assert node.label == 6

       UpperCAmelCase_     :       Any  		=					t.search(13							)
       assert node.label == 13

       with self.assertRaises(lowerCamelCase_							):  # noqa: B017
           t.search(2							)


   def       A__    (    self:     Union[str, Any]							)	->      None:
       UpperCAmelCase_     :       Optional[int]  		=					self._get_binary_search_tree()

       t.remove(13							)
       assert t.root is not None
       assert t.root.right is not None
       assert t.root.right.right is not None
       assert t.root.right.right.right is None
       assert t.root.right.right.left is None

       t.remove(7							)
       assert t.root.left is not None
       assert t.root.left.right is not None
       assert t.root.left.right.left is not None
       assert t.root.left.right.right is None
       assert t.root.left.right.left.label == 4

       t.remove(6							)
       assert t.root.left.left is not None
       assert t.root.left.right.right is not None
       assert t.root.left.left.label == 1
       assert t.root.left.right.label == 4
       assert t.root.left.right.right.label == 5
       assert t.root.left.right.left is None
       assert t.root.left.left.parent == t.root.left
       assert t.root.left.right.parent == t.root.left

       t.remove(3							)
       assert t.root is not None
       assert t.root.left.label == 4
       assert t.root.left.right.label == 5
       assert t.root.left.left.label == 1
       assert t.root.left.parent == t.root
       assert t.root.left.left.parent == t.root.left
       assert t.root.left.right.parent == t.root.left

       t.remove(4							)
       assert t.root.left is not None
       assert t.root.left.left is not None
       assert t.root.left.label == 5
       assert t.root.left.right is None
       assert t.root.left.left.label == 1
       assert t.root.left.parent == t.root
       assert t.root.left.left.parent == t.root.left


   def       A__    (    self:     Tuple							)	->      None:
       UpperCAmelCase_     :       Tuple  		=					self._get_binary_search_tree()

       t.remove(3							)
       assert t.root is not None
       assert t.root.left is not None
       assert t.root.left.left is not None
       assert t.root.left.right is not None
       assert t.root.left.right.left is not None
       assert t.root.left.right.right is not None
       assert t.root.left.label == 4
       assert t.root.left.right.label == 6
       assert t.root.left.left.label == 1
       assert t.root.left.right.right.label == 7
       assert t.root.left.right.left.label == 5
       assert t.root.left.parent == t.root
       assert t.root.left.right.parent == t.root.left
       assert t.root.left.left.parent == t.root.left
       assert t.root.left.right.left.parent == t.root.left.right


   def       A__    (    self:     Optional[int]							)	->      None:
       UpperCAmelCase_     :       Optional[int]  		=					self._get_binary_search_tree()
       t.empty()
       assert t.root is None


   def       A__    (    self:     Union[str, Any]							)	->      None:
       UpperCAmelCase_     :       Optional[int]  		=					self._get_binary_search_tree()
       assert not t.is_empty()

       t.empty()
       assert t.is_empty()


   def       A__    (    self:     List[str]							)	->      None:
       UpperCAmelCase_     :       Any  		=					self._get_binary_search_tree()

       assert t.exists(6							)
       assert not t.exists(-1							)


   def       A__    (    self:     int							)	->      None:
       UpperCAmelCase_     :       Optional[Any]  		=					self._get_binary_search_tree()

       assert t.get_max_label() == 14

       t.empty()
       with self.assertRaises(lowerCamelCase_							):  # noqa: B017
           t.get_max_label()


   def       A__    (    self:     Tuple							)	->      None:
       UpperCAmelCase_     :       Optional[int]  		=					self._get_binary_search_tree()

       assert t.get_min_label() == 1

       t.empty()
       with self.assertRaises(lowerCamelCase_							):  # noqa: B017
           t.get_min_label()


   def       A__    (    self:     List[Any]							)	->      None:
       UpperCAmelCase_     :       Union[str, Any]  		=					self._get_binary_search_tree()

       UpperCAmelCase_     :       Dict  		=					[i.label for i in t.inorder_traversal()]
       assert inorder_traversal_nodes == [1, 3, 4, 5, 6, 7, 8, 10, 13, 14]






   def       A__    (    self:     Union[str, Any]							)	->      None:
       UpperCAmelCase_     :       List[Any]  		=					self._get_binary_search_tree()

       UpperCAmelCase_     :       Union[str, Any]  		=					[i.label for i in t.preorder_traversal()]
       assert preorder_traversal_nodes == [8, 3, 1, 6, 4, 5, 7, 10, 14, 13]







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Tuple  		=					BinarySearchTree()
    t.put(8    )
    t.put(3    )
    t.put(6    )
    t.put(1    )
    t.put(10    )
    t.put(14    )
    t.put(13    )
    t.put(4    )
    t.put(7    )
    t.put(5    )

    print(
        """
            8
           / \\
          3   10
         / \\    \\
        1   6    14
           / \\   /
          4   7 13
           \\
            5
        """    )

    print("""Label 6 exists:"""   ,		t.exists(6    )    )
    print("""Label 13 exists:"""   ,		t.exists(13    )    )
    print("""Label -1 exists:"""   ,		t.exists(-1    )    )
    print("""Label 12 exists:"""   ,		t.exists(12    )    )

    # Prints all the elements of the list in inorder traversal
    UpperCAmelCase_     :       Optional[Any]  		=					[i.label for i in t.inorder_traversal()]
    print("""Inorder traversal:"""   ,		_a    )

    # Prints all the elements of the list in preorder traversal
    UpperCAmelCase_     :       List[Any]  		=					[i.label for i in t.preorder_traversal()]
    print("""Preorder traversal:"""   ,		_a    )

    print("""Max. label:"""   ,		t.get_max_label()    )
    print("""Min. label:"""   ,		t.get_min_label()    )

    # Delete elements
    print("""\nDeleting elements 13, 10, 8, 3, 6, 14"""    )
    print(
        """
          4
         / \\
        1   7
             \\
              5
        """    )
    t.remove(13    )
    t.remove(10    )
    t.remove(8    )
    t.remove(3    )
    t.remove(6    )
    t.remove(14    )

    # Prints all the elements of the list in inorder traversal after delete
    UpperCAmelCase_     :       Optional[Any]  		=					[i.label for i in t.inorder_traversal()]
    print("""Inorder traversal after delete:"""   ,		_a    )

    # Prints all the elements of the list in preorder traversal after delete
    UpperCAmelCase_     :       Optional[int]  		=					[i.label for i in t.preorder_traversal()]
    print("""Preorder traversal after delete:"""   ,		_a    )

    print("""Max. label:"""   ,		t.get_max_label()    )
    print("""Min. label:"""   ,		t.get_min_label()    )


if __name__ == "__main__":
      binary_search_tree_example()





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_									=   True
      from torch.cuda.amp import autocast


UpperCamelCase_									=   logging.getLogger(__name__)







@dataclass
class       _snake_case				:






   '''simple docstring'''






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







def       lowerCamelCase_  (    _a       :						ModelArguments   ,		_a       :						TrainingArguments    ):



    '''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_     :       List[Any]  		=					logging.WARNING
    if model_args.verbose_logging:
        UpperCAmelCase_     :       int  		=					logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank    ):
        UpperCAmelCase_     :       Union[str, Any]  		=					logging.INFO
    logger.setLevel(_a    )







@dataclass
class       _snake_case				:






   '''simple docstring'''






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







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : bool       =						False
   A__      : Optional[str]       =						None
   A__      : Optional[str]       =						"|"
   A__      : Optional[Dict[str, str]]       =						field(default_factory=__snake_case )
   A__      : Optional[Set[str]]       =						field(default_factory=__snake_case )
   A__      : Optional[Callable[[str], str]]       =						None


   @classmethod
   def       A__    (    cls:     List[Any]  ,lowerCamelCase_:     str							)	->      Union[str, Any]:
       if name == "librispeech":
           return cls()
       if name == "timit":
           return cls(
               do_lower_case=lowerCamelCase_  ,translation_table=str.maketrans({"""-""": """ """}							)  ,)
       if name == "buckwalter":
           UpperCAmelCase_     :       Dict  		=					{
               """-""": """ """,  # sometimes used to represent pauses
               """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
           }
           return cls(
               vocab_file=pathlib.Path(lowerCamelCase_							).parent.joinpath("""vocab/buckwalter.json"""							)  ,word_delimiter_token="""/"""  ,translation_table=str.maketrans(lowerCamelCase_							)  ,words_to_remove={"""sil"""}  ,untransliterator=arabic.buckwalter.untransliterate  ,)
       raise ValueError(F'''Unsupported orthography: \'{name}\'.'''							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     str							)	->      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_     :       Optional[int]  		=					text.translate(self.translation_table							)
       if len(self.words_to_remove							) == 0:
           UpperCAmelCase_     :       List[Any]  		=					""" """.join(text.split()							)  # clean up whitespaces
       else:
           UpperCAmelCase_     :       List[str]  		=					""" """.join(w for w in text.split() if w not in self.words_to_remove							)  # and clean up whilespaces
       return text






   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     ModelArguments							)	->      WavaVecaProcessor:
       UpperCAmelCase_     :       List[Any]  		=					WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path  ,cache_dir=model_args.cache_dir							)
       if self.vocab_file:
           UpperCAmelCase_     :       Tuple  		=					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_     :       Tuple  		=					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(lowerCamelCase_  ,lowerCamelCase_							)







@dataclass
class       _snake_case				:






   '''simple docstring'''






   A__      : WavaVecaProcessor
   A__      : Union[bool, str]       =						True
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None
   A__      : Optional[int]       =						None


   def __call__(    self:     Tuple  ,lowerCamelCase_:     List[Dict[str, Union[List[int], torch.Tensor]]]							)	->      Dict[str, torch.Tensor]:
       # split inputs and labels since they have to be of different lenghts and need
       # different padding methods
       UpperCAmelCase_     :       Union[str, Any]  		=					[{"""input_values""": feature["""input_values"""]} for feature in features]
       UpperCAmelCase_     :       List[Any]  		=					[{"""input_ids""": feature["""labels"""]} for feature in features]

       UpperCAmelCase_     :       List[str]  		=					self.processor.pad(
           lowerCamelCase_  ,padding=self.padding  ,max_length=self.max_length  ,pad_to_multiple_of=self.pad_to_multiple_of  ,return_tensors="""pt"""  ,)
       UpperCAmelCase_     :       Optional[Any]  		=					self.processor.pad(
           labels=lowerCamelCase_  ,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_     :       Any  		=					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1							)  ,-100							)

       UpperCAmelCase_     :       List[str]  		=					labels

       return batch







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






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

       if self.use_amp:
           with autocast():
               UpperCAmelCase_     :       int  		=					self.compute_loss(lowerCamelCase_  ,lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Tuple  		=					self.compute_loss(lowerCamelCase_  ,lowerCamelCase_							)

       if self.args.n_gpu > 1:
           if model.module.config.ctc_loss_reduction == "mean":
               UpperCAmelCase_     :       List[str]  		=					loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
               UpperCAmelCase_     :       List[str]  		=					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_     :       Union[str, 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()

       return loss.detach()







def       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					parser.parse_args_into_dataclasses()
    configure_logger(_a   ,		_a    )

    UpperCAmelCase_     :       Tuple  		=					Orthography.from_name(data_args.orthography.lower()    )
    UpperCAmelCase_     :       int  		=					orthography.create_processor(_a    )
    UpperCAmelCase_     :       Dict  		=					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_     :       Dict  		=					datasets.load_dataset(
        data_args.dataset_name   ,		data_args.dataset_config_name   ,		split=data_args.train_split_name    )
    UpperCAmelCase_     :       int  		=					datasets.load_dataset(
        data_args.dataset_name   ,		data_args.dataset_config_name   ,		split=data_args.validation_split_name    )

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

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

    UpperCAmelCase_     :       Any  		=					train_dataset.map(_a   ,		remove_columns=[data_args.speech_file_column]    )
    UpperCAmelCase_     :       Optional[Any]  		=					val_dataset.map(_a   ,		remove_columns=[data_args.speech_file_column]    )

    if data_args.max_duration_in_seconds is not None:

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

        UpperCAmelCase_     :       Union[str, Any]  		=					len(_a    )
        UpperCAmelCase_     :       int  		=					len(_a    )
        UpperCAmelCase_     :       Optional[Any]  		=					train_dataset.filter(_a   ,		remove_columns=["""duration_in_seconds"""]    )
        UpperCAmelCase_     :       Tuple  		=					val_dataset.filter(_a   ,		remove_columns=["""duration_in_seconds"""]    )
        if len(_a    ) > old_train_size:
            logger.warning(
                F'''Filtered out {len(_a    ) - old_train_size} train example(s) longer than'''
                F''' {data_args.max_duration_in_seconds} second(s).'''    )
        if len(_a    ) > old_val_size:
            logger.warning(
                F'''Filtered out {len(_a    ) - old_val_size} validation example(s) longer than'''
                F''' {data_args.max_duration_in_seconds} second(s).'''    )
    logger.info(F'''Split sizes: {len(_a    )} train and {len(_a    )} validation.'''    )

    logger.warning(F'''Updated {len(_a    )} 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_     :       Dict  		=					None

    def prepare_dataset(_a       :						Optional[int]    ):
        # 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_     :       List[str]  		=					processor(
            audio=batch["""speech"""]   ,		text=batch[data_args.target_text_column]   ,		sampling_rate=batch["""sampling_rate"""][0]    )
        batch.update(_a    )
        return batch

    UpperCAmelCase_     :       int  		=					train_dataset.map(
        _a   ,		batch_size=training_args.per_device_train_batch_size   ,		batched=_a   ,		num_proc=data_args.preprocessing_num_workers   ,		)
    UpperCAmelCase_     :       Optional[Any]  		=					val_dataset.map(
        _a   ,		batch_size=training_args.per_device_train_batch_size   ,		batched=_a   ,		num_proc=data_args.preprocessing_num_workers   ,		)

    UpperCAmelCase_     :       Optional[int]  		=					DataCollatorCTCWithPadding(processor=_a   ,		padding=_a    )

    def compute_metrics(_a       :						int    ):
        UpperCAmelCase_     :       List[Any]  		=					pred.predictions
        UpperCAmelCase_     :       Optional[int]  		=					np.argmax(_a   ,		axis=-1    )

        UpperCAmelCase_     :       List[str]  		=					processor.tokenizer.pad_token_id

        UpperCAmelCase_     :       str  		=					processor.batch_decode(_a    )
        # we do not want to group tokens when computing the metrics
        UpperCAmelCase_     :       List[Any]  		=					processor.batch_decode(pred.label_ids   ,		group_tokens=_a    )
        if logger.isEnabledFor(logging.DEBUG    ):
            for reference, predicted in zip(_a   ,		_a    ):
                logger.debug(F'''reference: "{reference}"'''    )
                logger.debug(F'''predicted: "{predicted}"'''    )
                if orthography.untransliterator is not None:
                    logger.debug(F'''reference (untransliterated): "{orthography.untransliterator(_a    )}"'''    )
                    logger.debug(F'''predicted (untransliterated): "{orthography.untransliterator(_a    )}"'''    )

        UpperCAmelCase_     :       Dict  		=					wer_metric.compute(predictions=_a   ,		references=_a    )

        return {"wer": wer}

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    UpperCAmelCase_     :       Union[str, Any]  		=					CTCTrainer(
        model=_a   ,		data_collator=_a   ,		args=_a   ,		compute_metrics=_a   ,		train_dataset=_a   ,		eval_dataset=_a   ,		tokenizer=processor.feature_extractor   ,		)

    trainer.train()


if __name__ == "__main__":
      main()
