
'''simple docstring'''







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

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

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


if is_apex_available():
							from apex import amp

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


_lowercase					:    Dict       				=			logging.getLogger(__name__)






@dataclass
class 					lowerCAmelCase__  :
					lowerCAmelCase_	  =	field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
					lowerCAmelCase_	  =	field(
					    default=lowerCamelCase_       ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,		)
					lowerCAmelCase_	  =	field(
					    default=lowerCamelCase_       ,		metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}	)
					lowerCAmelCase_	  =	field(
					    default=lowerCamelCase_       ,		metadata={'''help''': '''Whether to log verbose messages or not.'''}       ,		)







def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					ModelArguments , __SCREAMING_SNAKE_CASE   :					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				)] , )
						lowercase_      :	Tuple						=			logging.WARNING
						if model_args.verbose_logging:
												lowercase_      :	List[str]						=			logging.DEBUG
						elif trainer_utils.is_main_process(training_args.local_rank				):
												lowercase_      :	Dict						=			logging.INFO
						logger.setLevel(__SCREAMING_SNAKE_CASE				)






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







@dataclass
class 					lowerCAmelCase__  :
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	None
					lowerCAmelCase_	  =	"|"
					lowerCAmelCase_	  =	field(default_factory=lowerCamelCase_	)
					lowerCAmelCase_	  =	field(default_factory=lowerCamelCase_	)
					lowerCAmelCase_	  =	None






					@classmethod
					def       _snake_case       (  cls							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											if len(self.translation_table						) > 0:
																	lowercase_      :	Union[str, Any]						=			text.translate(self.translation_table						)
											if len(self.words_to_remove						) == 0:
																	lowercase_      :	Dict						=			''' '''.join(text.split()						)  # clean up whitespaces
											else:
																	lowercase_      :	Union[str, Any]						=			''' '''.join(w for w in text.split() if w not in self.words_to_remove						)  # and clean up whilespaces
											return text






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	str						=			WavaVecaFeatureExtractor.from_pretrained(
											    model_args.model_name_or_path							,				cache_dir=model_args.cache_dir						)
											if self.vocab_file:
																	lowercase_      :	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:
																	lowercase_      :	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(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)







@dataclass
class 					lowerCAmelCase__  :
					lowerCAmelCase_	  =	42
					lowerCAmelCase_	  =	True
					lowerCAmelCase_	  =	None
					lowerCAmelCase_	  =	None
					lowerCAmelCase_	  =	None
					lowerCAmelCase_	  =	None






					def __call__(  self							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			[{'''input_values''': feature['''input_values''']} for feature in features]
											lowercase_      :	Any						=			[{'''input_ids''': feature['''labels''']} for feature in features]

											lowercase_      :	Optional[Any]						=			self.processor.pad(
											    __SCREAMING_SNAKE_CASE							,				padding=self.padding							,				max_length=self.max_length							,				pad_to_multiple_of=self.pad_to_multiple_of							,				return_tensors='''pt'''							,				)
											lowercase_      :	int						=			self.processor.pad(
											    labels=__SCREAMING_SNAKE_CASE							,				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
											lowercase_      :	Any						=			labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1						)							,				-1_00						)

											lowercase_      :	Tuple						=			labels

											return batch







class 					lowerCAmelCase__  (    lowerCamelCase_	):






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											model.train()
											lowercase_      :	Optional[int]						=			self._prepare_inputs(__SCREAMING_SNAKE_CASE						)

											if self.use_amp:
																	with autocast():
																							lowercase_      :	int						=			self.compute_loss(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
											else:
																	lowercase_      :	Dict						=			self.compute_loss(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

											if self.args.n_gpu > 1:
																	if model.module.config.ctc_loss_reduction == "mean":
																							lowercase_      :	Union[str, Any]						=			loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																							lowercase_      :	Union[str, Any]						=			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:
																	lowercase_      :	Optional[Any]						=			loss / self.args.gradient_accumulation_steps

											if self.use_amp:
																	self.scaler.scale(__SCREAMING_SNAKE_CASE						).backward()
											elif self.use_apex:
																	with amp.scale_loss(__SCREAMING_SNAKE_CASE							,				self.optimizer						) as scaled_loss:
																							scaled_loss.backward()
											elif self.deepspeed:
																	self.deepspeed.backward(__SCREAMING_SNAKE_CASE						)
											else:
																	loss.backward()

											return loss.detach()







def    snake_case_						(							):



						"""simple docstring"""





						lowercase_      :	Tuple						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

						lowercase_		,					lowercase_		,					lowercase_      :	Tuple						=			parser.parse_args_into_dataclasses()
						configure_logger(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						lowercase_      :	List[Any]						=			Orthography.from_name(data_args.orthography.lower()				)
						lowercase_      :	Tuple						=			orthography.create_processor(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	List[str]						=			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				) , )

						lowercase_      :	Dict						=			datasets.load_dataset(
						    data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name				)
						lowercase_      :	Optional[Any]						=			datasets.load_dataset(
						    data_args.dataset_name , data_args.dataset_config_name , split=data_args.validation_split_name				)

						lowercase_      :	Optional[Any]						=			datasets.load_metric('''wer'''				)
						lowercase_      :	Optional[Any]						=			processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
						lowercase_      :	Union[str, Any]						=			''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(__SCREAMING_SNAKE_CASE				) == 1				)
						lowercase_      :	int						=			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 , )
						lowercase_      :	Optional[Any]						=			[]

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

						lowercase_      :	int						=			train_dataset.map(__SCREAMING_SNAKE_CASE , remove_columns=[data_args.speech_file_column]				)
						lowercase_      :	Dict						=			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   :					Tuple				):
																		return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

												lowercase_      :	List[str]						=			len(__SCREAMING_SNAKE_CASE				)
												lowercase_      :	Optional[Any]						=			len(__SCREAMING_SNAKE_CASE				)
												lowercase_      :	int						=			train_dataset.filter(__SCREAMING_SNAKE_CASE , remove_columns=['''duration_in_seconds''']				)
												lowercase_      :	int						=			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}"'''				)
						lowercase_      :	Union[str, Any]						=			None

						def prepare_dataset(__SCREAMING_SNAKE_CASE   :					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}.'''

												lowercase_      :	Any						=			processor(
												    audio=batch['''speech'''] , text=batch[data_args.target_text_column] , sampling_rate=batch['''sampling_rate'''][0]				)
												batch.update(__SCREAMING_SNAKE_CASE				)
												return batch

						lowercase_      :	Any						=			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 , )
						lowercase_      :	List[Any]						=			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 , )

						lowercase_      :	Optional[int]						=			DataCollatorCTCWithPadding(processor=__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE				)

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

												lowercase_      :	str						=			processor.tokenizer.pad_token_id

												lowercase_      :	List[Any]						=			processor.batch_decode(__SCREAMING_SNAKE_CASE				)
												# we do not want to group tokens when computing the metrics
												lowercase_      :	Any						=			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				)}"'''				)

												lowercase_      :	Tuple						=			wer_metric.compute(predictions=__SCREAMING_SNAKE_CASE , references=__SCREAMING_SNAKE_CASE				)

												return {"wer": wer}

						if model_args.freeze_feature_extractor:
												model.freeze_feature_extractor()

						lowercase_      :	List[str]						=			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()





'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowercase					:    List[Any]       				=			{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Any				):



						"""simple docstring"""





						for attribute in key.split('''.'''				):
												lowercase_      :	Optional[int]						=			getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						if weight_type is not None:
												lowercase_      :	Optional[int]						=			getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).shape
						else:
												lowercase_      :	Optional[Any]						=			hf_pointer.shape

						assert hf_shape == value.shape, (
						    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
						    F''' {value.shape} for {full_name}'''
						)

						if weight_type == "weight":
												lowercase_      :	int						=			value
						elif weight_type == "weight_g":
												lowercase_      :	Dict						=			value
						elif weight_type == "weight_v":
												lowercase_      :	Dict						=			value
						elif weight_type == "bias":
												lowercase_      :	Union[str, Any]						=			value
						else:
												lowercase_      :	Optional[Any]						=			value

						logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					Optional[Any]				):



						"""simple docstring"""





						lowercase_      :	Tuple						=			[]
						lowercase_      :	Union[str, Any]						=			fairseq_model.state_dict()

						lowercase_      :	Dict						=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase_      :	str						=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == '''group''' , )
																		lowercase_      :	int						=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase_      :	Union[str, Any]						=			'''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																								if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
																														lowercase_      :	Dict						=			True
																														if "*" in mapped_key:
																																				lowercase_      :	str						=			name.split(__SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				lowercase_      :	Tuple						=			mapped_key.replace('''*''' , __SCREAMING_SNAKE_CASE				)
																														if "weight_g" in name:
																																				lowercase_      :	Dict						=			'''weight_g'''
																														elif "weight_v" in name:
																																				lowercase_      :	Dict						=			'''weight_v'''
																														elif "weight" in name:
																																				lowercase_      :	int						=			'''weight'''
																														elif "bias" in name:
																																				lowercase_      :	List[Any]						=			'''bias'''
																														else:
																																				lowercase_      :	Optional[Any]						=			None
																														set_recursively(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)
																								continue
												if not is_used:
																		unused_weights.append(__SCREAMING_SNAKE_CASE				)

						logger.warning(F'''Unused weights: {unused_weights}'''				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Optional[int]				):



						"""simple docstring"""





						lowercase_      :	int						=			full_name.split('''conv_layers.'''				)[-1]
						lowercase_      :	int						=			name.split('''.'''				)
						lowercase_      :	int						=			int(items[0]				)
						lowercase_      :	List[Any]						=			int(items[1]				)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
																		)
																		lowercase_      :	int						=			value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
																		)
																		lowercase_      :	Any						=			value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
																		    " found."
																		)
																		lowercase_      :	int						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F'''{full_name} has size {value.shape}, but'''
																		    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
																		)
																		lowercase_      :	List[str]						=			value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						else:
												unused_weights.append(__SCREAMING_SNAKE_CASE				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					Tuple				):



						"""simple docstring"""





						lowercase_      :	str						=			SEWDConfig()
						if is_finetuned:
												lowercase_      :	Optional[int]						=			model.wav_encoder.wav_model.cfg
						else:
												lowercase_      :	Dict						=			model.cfg

						lowercase_      :	Tuple						=			fs_config.conv_bias
						lowercase_      :	Dict						=			eval(fs_config.conv_feature_layers				)
						lowercase_      :	Union[str, Any]						=			[x[0] for x in conv_layers]
						lowercase_      :	Dict						=			[x[1] for x in conv_layers]
						lowercase_      :	List[str]						=			[x[2] for x in conv_layers]
						lowercase_      :	Any						=			'''gelu'''
						lowercase_      :	Union[str, Any]						=			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						lowercase_      :	Union[str, Any]						=			0.0
						lowercase_      :	str						=			fs_config.activation_fn.name
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_embed_dim
						lowercase_      :	Any						=			0.02
						lowercase_      :	Any						=			fs_config.encoder_ffn_embed_dim
						lowercase_      :	List[str]						=			1E-5
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_layerdrop
						lowercase_      :	Union[str, Any]						=			fs_config.encoder_attention_heads
						lowercase_      :	Dict						=			fs_config.conv_pos_groups
						lowercase_      :	int						=			fs_config.conv_pos
						lowercase_      :	Union[str, Any]						=			len(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[Any]						=			fs_config.encoder_layers
						lowercase_      :	List[Any]						=			fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						lowercase_      :	Union[str, Any]						=			fs_config.max_position_embeddings
						lowercase_      :	Tuple						=			fs_config.position_buckets
						lowercase_      :	str						=			fs_config.share_att_key
						lowercase_      :	List[Any]						=			fs_config.relative_attention
						lowercase_      :	Any						=			fs_config.position_biased_input
						lowercase_      :	Union[str, Any]						=			tuple(fs_config.pos_att_type.split('''|'''				)				)
						lowercase_      :	List[str]						=			fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												lowercase_      :	List[Any]						=			model.cfg
												lowercase_      :	List[str]						=			fs_config.final_dropout
												lowercase_      :	Union[str, Any]						=			fs_config.layerdrop
						lowercase_      :	Optional[int]						=			fs_config.activation_dropout
						lowercase_      :	Dict						=			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						lowercase_      :	int						=			fs_config.attention_dropout
						lowercase_      :	Union[str, Any]						=			fs_config.dropout_input
						lowercase_      :	Any						=			fs_config.dropout
						lowercase_      :	List[str]						=			fs_config.mask_channel_length
						lowercase_      :	Union[str, Any]						=			fs_config.mask_channel_prob
						lowercase_      :	str						=			fs_config.mask_length
						lowercase_      :	List[Any]						=			fs_config.mask_prob

						lowercase_      :	Optional[int]						=			'''Wav2Vec2FeatureExtractor'''
						lowercase_      :	Dict						=			'''Wav2Vec2CTCTokenizer'''

						return config
@torch.no_grad()
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Any , __SCREAMING_SNAKE_CASE   :					int=None , __SCREAMING_SNAKE_CASE   :					List[Any]=None , __SCREAMING_SNAKE_CASE   :					Tuple=True				):



						"""simple docstring"""





						if is_finetuned:
												lowercase_		,					lowercase_		,					lowercase_      :	Optional[Any]						=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
						else:
												lowercase_		,					lowercase_		,					lowercase_      :	List[str]						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

						if config_path is not None:
												lowercase_      :	Union[str, Any]						=			SEWDConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	List[str]						=			convert_config(model[0] , __SCREAMING_SNAKE_CASE				)
						lowercase_      :	Tuple						=			model[0].eval()

						lowercase_      :	Any						=			True if config.feat_extract_norm == '''layer''' else False
						lowercase_      :	List[Any]						=			WavaVecaFeatureExtractor(
						    feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__SCREAMING_SNAKE_CASE , return_attention_mask=__SCREAMING_SNAKE_CASE , )

						if is_finetuned:
												if dict_path:
																		lowercase_      :	Optional[int]						=			Dictionary.load(__SCREAMING_SNAKE_CASE				)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowercase_      :	int						=			target_dict.pad_index
																		lowercase_      :	List[Any]						=			target_dict.bos_index
																		lowercase_      :	Dict						=			target_dict.pad_index
																		lowercase_      :	Dict						=			target_dict.bos_index
																		lowercase_      :	List[str]						=			target_dict.eos_index
																		lowercase_      :	Dict						=			len(target_dict.symbols				)
																		lowercase_      :	Optional[Any]						=			os.path.join(__SCREAMING_SNAKE_CASE , '''vocab.json'''				)
																		if not os.path.isdir(__SCREAMING_SNAKE_CASE				):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__SCREAMING_SNAKE_CASE				)				)
																								return
																		os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE				)
																		with open(__SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8'''				) as vocab_handle:
																								json.dump(target_dict.indices , __SCREAMING_SNAKE_CASE				)
																		lowercase_      :	List[str]						=			WavaVecaCTCTokenizer(
																		    __SCREAMING_SNAKE_CASE , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__SCREAMING_SNAKE_CASE , )
																		lowercase_      :	Optional[int]						=			WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE				)
																		processor.save_pretrained(__SCREAMING_SNAKE_CASE				)

												lowercase_      :	int						=			SEWDForCTC(__SCREAMING_SNAKE_CASE				)
						else:
												lowercase_      :	Union[str, Any]						=			SEWDModel(__SCREAMING_SNAKE_CASE				)
												feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE				)

						recursively_load_weights(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)

						hf_model.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
							_lowercase					:    List[str]       				=			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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument(
							    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
							)
							_lowercase					:    str       				=			parser.parse_args()
							convert_sew_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
							)



