

import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    ElectraForCausalLM,
	    ElectraForMaskedLM,
	    ElectraForMultipleChoice,
	    ElectraForPreTraining,
	    ElectraForQuestionAnswering,
	    ElectraForSequenceClassification,
	    ElectraForTokenClassification,
	    ElectraModel,
	)
	from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST



class    A__:
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase=13       ,     _lowercase=7       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase=99       ,     _lowercase=32       ,     _lowercase=5       ,     _lowercase=4       ,     _lowercase=37       ,     _lowercase="gelu"       ,     _lowercase=0.1       ,     _lowercase=0.1       ,     _lowercase=512       ,     _lowercase=16       ,     _lowercase=2       ,     _lowercase=0.0_2       ,     _lowercase=3       ,     _lowercase=4       ,     _lowercase=None       ,     )      ->				str:
									a_		:		Dict				    =				parent
									a_		:		Tuple				    =				batch_size
									a_		:		Tuple				    =				seq_length
									a_		:		Union[str, Any]				    =				is_training
									a_		:		int				    =				use_input_mask
									a_		:		Optional[int]				    =				use_token_type_ids
									a_		:		List[Any]				    =				use_labels
									a_		:		Optional[int]				    =				vocab_size
									a_		:		int				    =				hidden_size
									a_		:		List[str]				    =				num_hidden_layers
									a_		:		List[Any]				    =				num_attention_heads
									a_		:		Optional[Any]				    =				intermediate_size
									a_		:		Any				    =				hidden_act
									a_		:		Any				    =				hidden_dropout_prob
									a_		:		List[Any]				    =				attention_probs_dropout_prob
									a_		:		str				    =				max_position_embeddings
									a_		:		Optional[Any]				    =				type_vocab_size
									a_		:		str				    =				type_sequence_label_size
									a_		:		str				    =				initializer_range
									a_		:		Dict				    =				num_labels
									a_		:		Any				    =				num_choices
									a_		:		Optional[Any]				    =				scope





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		List[str]				    =				ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size  )

									a_		:		List[Any]				    =				None
									if self.use_input_mask:
												a_		:		str				    =				random_attention_mask([self.batch_size, self.seq_length]  )

									a_		:		int				    =				None
									if self.use_token_type_ids:
												a_		:		Any				    =				ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size  )

									a_		:		Union[str, Any]				    =				None
									a_		:		Dict				    =				None
									a_		:		Union[str, Any]				    =				None
									if self.use_labels:
												a_		:		Dict				    =				ids_tensor([self.batch_size]       ,     self.type_sequence_label_size  )
												a_		:		Optional[Any]				    =				ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels  )
												a_		:		int				    =				ids_tensor([self.batch_size]       ,     self.num_choices  )
												a_		:		Dict				    =				ids_tensor([self.batch_size, self.seq_length]       ,     1  )

									a_		:		List[Any]				    =				self.get_config()

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_mask,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    fake_token_labels,
									)





						def 	UpperCamelCase__			(  self  )      ->				str:
									return ElectraConfig(
									    vocab_size=self.vocab_size       ,     hidden_size=self.hidden_size       ,     num_hidden_layers=self.num_hidden_layers       ,     num_attention_heads=self.num_attention_heads       ,     intermediate_size=self.intermediate_size       ,     hidden_act=self.hidden_act       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     is_decoder=_lowercase       ,     initializer_range=self.initializer_range       ,     )





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									(
									    a_			
									)		:		Any				    =				self.prepare_config_and_inputs()

									a_		:		Any				    =				True
									a_		:		str				    =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									a_		:		Optional[Any]				    =				ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2  )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_mask,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				Dict:
									a_		:		Optional[int]				    =				ElectraModel(config=_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		Tuple				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase  )
									a_		:		str				    =				model(_lowercase       ,     token_type_ids=_lowercase  )
									a_		:		Tuple				    =				model(_lowercase  )
									self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				Tuple:
									a_		:		List[Any]				    =				True
									a_		:		List[str]				    =				ElectraModel(_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		Optional[int]				    =				model(
									    _lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     encoder_hidden_states=_lowercase       ,     encoder_attention_mask=_lowercase       ,     )
									a_		:		Optional[Any]				    =				model(
									    _lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     encoder_hidden_states=_lowercase       ,     )
									a_		:		str				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase  )
									self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				Union[str, Any]:
									a_		:		Optional[int]				    =				ElectraForMaskedLM(config=_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		List[str]				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     labels=_lowercase  )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				List[Any]:
									a_		:		Tuple				    =				ElectraForCausalLM(config=_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		Dict				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     labels=_lowercase  )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				List[Any]:
									a_		:		List[str]				    =				self.num_labels
									a_		:		Tuple				    =				ElectraForTokenClassification(config=_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		List[Any]				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     labels=_lowercase  )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				Dict:
									a_		:		List[str]				    =				self.num_labels
									a_		:		Optional[int]				    =				ElectraForPreTraining(config=_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		Optional[Any]				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     labels=_lowercase  )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				List[str]:
									a_		:		Optional[int]				    =				self.num_labels
									a_		:		Union[str, Any]				    =				ElectraForSequenceClassification(_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		Tuple				    =				model(_lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     labels=_lowercase  )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				List[Any]:
									a_		:		Any				    =				ElectraForQuestionAnswering(config=_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		Any				    =				model(
									    _lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     start_positions=_lowercase       ,     end_positions=_lowercase       ,     )
									self.parent.assertEqual(result.start_logits.shape       ,     (self.batch_size, self.seq_length)  )
									self.parent.assertEqual(result.end_logits.shape       ,     (self.batch_size, self.seq_length)  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     )      ->				List[Any]:
									a_		:		Any				    =				self.num_choices
									a_		:		List[str]				    =				ElectraForMultipleChoice(config=_lowercase  )
									model.to(_lowercase  )
									model.eval()
									a_		:		Union[str, Any]				    =				input_ids.unsqueeze(1  ).expand(-1       ,     self.num_choices       ,     -1  ).contiguous()
									a_		:		Tuple				    =				token_type_ids.unsqueeze(1  ).expand(-1       ,     self.num_choices       ,     -1  ).contiguous()
									a_		:		Any				    =				input_mask.unsqueeze(1  ).expand(-1       ,     self.num_choices       ,     -1  ).contiguous()
									a_		:		Any				    =				model(
									    _lowercase       ,     attention_mask=_lowercase       ,     token_type_ids=_lowercase       ,     labels=_lowercase       ,     )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Dict				    =				self.prepare_config_and_inputs()
									(
									    a_			
									)		:		Any				    =				config_and_inputs
									a_		:		int				    =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
									return config, inputs_dict







@require_torch
class    A__(a_,       a_,       unittest.TestCase    ):
						"""simple docstring"""






						_A  :							Optional[int]   			=	(
						    (
						        ElectraModel,
						        ElectraForPreTraining,
						        ElectraForMaskedLM,
						        ElectraForCausalLM,
						        ElectraForMultipleChoice,
						        ElectraForTokenClassification,
						        ElectraForSequenceClassification,
						        ElectraForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						_A  :							Any   			=	(
						    {
						        '''feature-extraction''': ElectraModel,
						        '''fill-mask''': ElectraForMaskedLM,
						        '''question-answering''': ElectraForQuestionAnswering,
						        '''text-classification''': ElectraForSequenceClassification,
						        '''text-generation''': ElectraForCausalLM,
						        '''token-classification''': ElectraForTokenClassification,
						        '''zero-shot''': ElectraForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_A  :							Union[str, Any]   			=	True





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=False  )      ->				List[Any]:
									a_		:		Optional[int]				    =				super()._prepare_for_class(_lowercase       ,     _lowercase       ,     return_labels=_lowercase  )

									if return_labels:
												if model_class in get_values(_lowercase  ):
															a_		:		List[Any]				    =				torch.zeros(
															    (self.model_tester.batch_size, self.model_tester.seq_length)       ,     dtype=torch.long       ,     device=_lowercase  )
									return inputs_dict





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									a_		:		int				    =				ElectraModelTester(self  )
									a_		:		Union[str, Any]				    =				ConfigTester(self       ,     config_class=_lowercase       ,     hidden_size=37  )





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									self.config_tester.run_common_tests()





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Optional[int]				    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_electra_model(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		Dict				    =				self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_electra_model_as_decoder(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		Dict				    =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
												a_		:		Dict				    =				type
												self.model_tester.create_and_check_electra_model(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		str				    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_electra_for_masked_lm(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		Optional[int]				    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_electra_for_token_classification(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		int				    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_electra_for_pretraining(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_		:		Dict				    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_electra_for_sequence_classification(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									a_		:		int				    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_electra_for_question_answering(*_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		Optional[Any]				    =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_electra_for_multiple_choice(*_lowercase  )





						@slow
						def 	UpperCamelCase__			(  self  )      ->				str:
									for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a_		:		List[str]				    =				ElectraModel.from_pretrained(_lowercase  )
												self.assertIsNotNone(_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									a_		:		List[str]				    =				self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_electra_for_causal_lm(*_lowercase  )







@require_torch
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						@slow
						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									a_		:		List[str]				    =				ElectraModel.from_pretrained("""google/electra-small-discriminator"""  )
									a_		:		Union[str, Any]				    =				torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]  )
									a_		:		List[str]				    =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
									a_		:		Any				    =				model(_lowercase       ,     attention_mask=_lowercase  )[0]
									a_		:		Dict				    =				torch.Size((1, 11, 256)  )
									self.assertEqual(output.shape       ,     _lowercase  )
									a_		:		Tuple				    =				torch.tensor(
									    [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]  )

									self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,     _lowercase       ,     atol=1e-4  )  )





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"""):
	__snake_case							:     List[str] 						=	True
	from torch.cuda.amp import autocast


__snake_case							:     Dict 						=	logging.getLogger(__name__)



@dataclass
class    A__:
						"""simple docstring"""






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







def 			_UpperCAmelCase							(		a__       ,					a__):

			'''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_		:		Optional[int]				    =				logging.WARNING
			if model_args.verbose_logging:
						a_		:		Any				    =				logging.DEBUG
			elif trainer_utils.is_main_process(training_args.local_rank):
						a_		:		List[str]				    =				logging.INFO
			logger.setLevel(a__)



@dataclass
class    A__:
						"""simple docstring"""






						_A  :							str   			=	field(
						    default=a_,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
						_A  :							Optional[str]   			=	field(
						    default=a_,       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=a_,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''},       )
						_A  :							Optional[float]   			=	field(
						    default=a_,       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=a_,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}    )
						_A  :							Optional[int]   			=	field(
						    default=a_,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''},       )







@dataclass
class    A__:
						"""simple docstring"""






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





						@classmethod
						def 	UpperCamelCase__			(  cls       ,     _lowercase  )      ->				Optional[Any]:
									if name == "librispeech":
												return cls()
									if name == "timit":
												return cls(
												    do_lower_case=_lowercase       ,     translation_table=str.maketrans({"""-""": """ """}  )       ,     )
									if name == "buckwalter":
												a_		:		Tuple				    =				{
												    """-""": """ """,  # 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 	UpperCamelCase__			(  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:
												a_		:		Dict				    =				text.translate(self.translation_table  )
									if len(self.words_to_remove  ) == 0:
												a_		:		Any				    =				""" """.join(text.split()  )  # clean up whitespaces
									else:
												a_		:		Optional[int]				    =				""" """.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
									return text





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				WavaVecaProcessor:
									a_		:		List[str]				    =				WavaVecaFeatureExtractor.from_pretrained(
									    model_args.model_name_or_path       ,     cache_dir=model_args.cache_dir  )
									if self.vocab_file:
												a_		:		List[Any]				    =				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_		:		Dict				    =				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  :							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       ,     _lowercase  )      ->				Dict[str, torch.Tensor]:
									# split inputs and labels since they have to be of different lenghts and need
									# different padding methods
									a_		:		Dict				    =				[{"""input_values""": feature["""input_values"""]} for feature in features]
									a_		:		str				    =				[{"""input_ids""": feature["""labels"""]} for feature in features]

									a_		:		Optional[Any]				    =				self.processor.pad(
									    _lowercase       ,     padding=self.padding       ,     max_length=self.max_length       ,     pad_to_multiple_of=self.pad_to_multiple_of       ,     return_tensors="""pt"""       ,     )
									a_		:		Any				    =				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
									a_		:		Optional[int]				    =				labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1  )       ,     -100  )

									a_		:		Tuple				    =				labels

									return batch







class    A__(a_    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase  )      ->				torch.Tensor:
									model.train()
									a_		:		Union[str, Any]				    =				self._prepare_inputs(_lowercase  )

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

									if self.args.n_gpu > 1:
												if model.module.config.ctc_loss_reduction == "mean":
															a_		:		List[str]				    =				loss.mean()
												elif model.module.config.ctc_loss_reduction == "sum":
															a_		:		int				    =				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_		:		Any				    =				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 			_UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		str				    =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))

			a_    ,			a_    ,			a_		:		str				    =				parser.parse_args_into_dataclasses()
			configure_logger(a__       ,					a__)

			a_		:		List[str]				    =				Orthography.from_name(data_args.orthography.lower())
			a_		:		Optional[Any]				    =				orthography.create_processor(a__)
			a_		:		Any				    =				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_		:		str				    =				datasets.load_dataset(
			    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=data_args.train_split_name)
			a_		:		Tuple				    =				datasets.load_dataset(
			    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=data_args.validation_split_name)

			a_		:		str				    =				datasets.load_metric("""wer""")
			a_		:		Optional[Any]				    =				processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
			a_		:		Union[str, Any]				    =				"""""".join(t for t in processor.tokenizer.get_vocab().keys() if len(a__) == 1)
			a_		:		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       ,					)
			a_		:		Optional[Any]				    =				[]

			def prepare_example(a__):  # TODO(elgeish) make use of multiprocessing?
						a_    ,			a_		:		str				    =				librosa.load(example[data_args.speech_file_column]       ,					sr=a__)
						if data_args.max_duration_in_seconds is not None:
									a_		:		Optional[int]				    =				len(example["""speech"""]) / example["""sampling_rate"""]
						# Normalize and clean up text; order matters!
						a_		:		List[Any]				    =				orthography.preprocess_for_training(example[data_args.target_text_column])
						a_		:		Optional[Any]				    =				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))
									a_		:		Union[str, Any]				    =				updated_text
						return example

			a_		:		Optional[int]				    =				train_dataset.map(a__       ,					remove_columns=[data_args.speech_file_column])
			a_		:		Optional[int]				    =				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__):
									return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

						a_		:		int				    =				len(a__)
						a_		:		Optional[Any]				    =				len(a__)
						a_		:		Tuple				    =				train_dataset.filter(a__       ,					remove_columns=["""duration_in_seconds"""])
						a_		:		List[Any]				    =				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}"''')
			a_		:		List[str]				    =				None

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

			a_		:		Optional[Any]				    =				train_dataset.map(
			    a__       ,					batch_size=training_args.per_device_train_batch_size       ,					batched=a__       ,					num_proc=data_args.preprocessing_num_workers       ,					)
			a_		:		List[str]				    =				val_dataset.map(
			    a__       ,					batch_size=training_args.per_device_train_batch_size       ,					batched=a__       ,					num_proc=data_args.preprocessing_num_workers       ,					)

			a_		:		List[str]				    =				DataCollatorCTCWithPadding(processor=a__       ,					padding=a__)

			def compute_metrics(a__):
						a_		:		List[Any]				    =				pred.predictions
						a_		:		List[str]				    =				np.argmax(a__       ,					axis=-1)

						a_		:		Optional[Any]				    =				processor.tokenizer.pad_token_id

						a_		:		Optional[Any]				    =				processor.batch_decode(a__)
						# we do not want to group tokens when computing the metrics
						a_		:		Optional[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__)}"''')

						a_		:		Optional[int]				    =				wer_metric.compute(predictions=a__       ,					references=a__)

						return {"wer": wer}

			if model_args.freeze_feature_extractor:
						model.freeze_feature_extractor()

			a_		:		Dict				    =				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()


