import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


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:	List[Any]       			=		logging.getLogger(__name__)



@dataclass
class        lowerCamelCase__				:
			UpperCamelCase__    							=field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
			UpperCamelCase__    							=field(
			    default=UpperCAmelCase       ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,)
			UpperCamelCase__    							=field(
			    default=UpperCAmelCase       ,metadata={"help": "Whether to freeze the feature extractor layers of the model."}				)
			UpperCamelCase__    							=field(
			    default=UpperCAmelCase       ,metadata={"help": "Whether to log verbose messages or not."}       ,)
			UpperCamelCase__    							=field(
			    default=2.0       ,metadata={"help": "Maximum temperature for gumbel softmax."}				)
			UpperCamelCase__    							=field(
			    default=0.5       ,metadata={"help": "Minimum temperature for gumbel softmax."}				)
			UpperCamelCase__    							=field(
			    default=0.99_99_95       ,metadata={"help": "Decay of gumbel temperature during training."}				)







def _lowerCamelCase  (		snake_case     , snake_case  ):
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     , datefmt='%m/%d/%Y %H:%M:%S'     , handlers=[logging.StreamHandler(sys.stdout  )]     , )
							_lowerCAmelCase							=							logging.WARNING
							if model_args.verbose_logging:
														_lowerCAmelCase							=							logging.DEBUG
							elif trainer_utils.is_main_process(training_args.local_rank  ):
														_lowerCAmelCase							=							logging.INFO
							logger.setLevel(snake_case  )



@dataclass
class        lowerCamelCase__				:
			UpperCamelCase__    							=field(
			    default=UpperCAmelCase       ,metadata={"help": "The name of the dataset to use (via the datasets library)."}				)
			UpperCamelCase__    							=field(
			    default=UpperCAmelCase       ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
			UpperCamelCase__    							=field(
			    default="train"       ,metadata={
			        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
			    }       ,)
			UpperCamelCase__    							=field(
			    default="validation"       ,metadata={
			        "help": (
			            "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
			        )
			    }       ,)
			UpperCamelCase__    							=field(
			    default="file"       ,metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}       ,)
			UpperCamelCase__    							=field(
			    default=UpperCAmelCase       ,metadata={"help": "Overwrite the cached preprocessed datasets or not."}				)
			UpperCamelCase__    							=field(
			    default=1       ,metadata={
			        "help": "The percentage of the train set used as validation set in case there's no validation split"
			    }       ,)
			UpperCamelCase__    							=field(
			    default=UpperCAmelCase       ,metadata={"help": "The number of processes to use for the preprocessing."}       ,)
			UpperCamelCase__    							=field(
			    default=20.0       ,metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}				)







@dataclass
class        lowerCamelCase__				:
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42
			UpperCamelCase__    							="longest"
			UpperCamelCase__    							=None
			UpperCamelCase__    							=None





			def __call__(       self	:	Union[str, Any]  ,  lowercase__	:	List[Dict[str, Union[List[int], torch.Tensor]]]						):
										# reformat list to dict and set to pytorch format
										_lowerCAmelCase							=							self.feature_extractor.pad(
										    lowercase__  ,  max_length=self.max_length  ,  padding=self.padding  ,  pad_to_multiple_of=self.pad_to_multiple_of  ,  return_tensors='pt'  ,  )
										_lowerCAmelCase							=							self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]						)

										_lowerCAmelCase							=							batch['input_values'].shape[0]

										# make sure that no loss is computed on padded inputs
										if batch["attention_mask"] is not None:
																	# compute real output lengths according to convolution formula
																	_lowerCAmelCase							=							self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1						)						).to(
																	    torch.long						)

																	_lowerCAmelCase							=							torch.zeros(
																	    (batch_size, mask_indices_seq_length)  ,  dtype=torch.long  ,  device=batch['input_values'].device						)

																	# these two operations makes sure that all values
																	# before the output lengths indices are attended to
																	_lowerCAmelCase							=							1
																	_lowerCAmelCase							=							attention_mask.flip([-1]						).cumsum(-1						).flip([-1]						).bool()

										# sample randomly masked indices
										_lowerCAmelCase							=							_compute_mask_indices(
										    (batch_size, mask_indices_seq_length)  ,  self.model.config.mask_time_prob  ,  self.model.config.mask_time_length  ,  attention_mask=lowercase__  ,  min_masks=2  ,  )

										return batch







class        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	Optional[int]  ,  *lowercase__	:	Tuple  ,  lowercase__	:	List[Any]=1  ,  lowercase__	:	Tuple=0  ,  lowercase__	:	Any=1.0  ,  **lowercase__	:	Dict						):
										super().__init__(*lowercase__  ,  **lowercase__						)
										_lowerCAmelCase							=							0
										_lowerCAmelCase							=							max_gumbel_temp
										_lowerCAmelCase							=							min_gumbel_temp
										_lowerCAmelCase							=							gumbel_temp_decay





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	nn.Module  ,  lowercase__	:	Dict[str, Union[torch.Tensor, Any]]						):
										model.train()
										_lowerCAmelCase							=							self._prepare_inputs(lowercase__						)

										if self.use_amp:
																	with autocast():
																								_lowerCAmelCase							=							self.compute_loss(lowercase__  ,  lowercase__						)
										else:
																	_lowerCAmelCase							=							self.compute_loss(lowercase__  ,  lowercase__						)

										if self.args.n_gpu > 1 or self.deepspeed:
																	if model.module.config.ctc_loss_reduction == "mean":
																								_lowerCAmelCase							=							loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																								_lowerCAmelCase							=							loss.sum() / (inputs['mask_time_indices']).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:
																	_lowerCAmelCase							=							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()

										self.num_update_step += 1
										# make sure gumbel softmax temperature is decayed
										if self.args.n_gpu > 1 or self.deepspeed:
																	model.module.set_gumbel_temperature(
																	    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step  ,  self.min_gumbel_temp						)						)
										else:
																	model.set_gumbel_temperature(
																	    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step  ,  self.min_gumbel_temp						)						)

										return loss.detach()







def _lowerCamelCase  (		):
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

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

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

							# Downloading and loading a dataset from the hub.
							_lowerCAmelCase							=							load_dataset(data_args.dataset_name     , data_args.dataset_config_name     , cache_dir=model_args.cache_dir  )

							if "validation" not in datasets.keys():
														# make sure only "validation" and "train" keys remain"
														_lowerCAmelCase							=							DatasetDict()
														_lowerCAmelCase							=							load_dataset(
														    data_args.dataset_name     , data_args.dataset_config_name     , split=F'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'     , cache_dir=model_args.cache_dir     , )
														_lowerCAmelCase							=							load_dataset(
														    data_args.dataset_name     , data_args.dataset_config_name     , split=F'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'     , cache_dir=model_args.cache_dir     , )
							else:
														# make sure only "validation" and "train" keys remain"
														_lowerCAmelCase							=							DatasetDict()
														_lowerCAmelCase							=							load_dataset(
														    data_args.dataset_name     , data_args.dataset_config_name     , split='validation'     , cache_dir=model_args.cache_dir     , )
														_lowerCAmelCase							=							load_dataset(
														    data_args.dataset_name     , data_args.dataset_config_name     , split=F'{data_args.train_split_name}'     , cache_dir=model_args.cache_dir     , )

							# only normalized-inputs-training is supported
							_lowerCAmelCase							=							WavaVecaFeatureExtractor.from_pretrained(
							    model_args.model_name_or_path     , cache_dir=model_args.cache_dir     , do_normalize=snake_case  )

							def prepare_dataset(snake_case  ):
														# check that all files have the correct sampling rate
														_lowerCAmelCase			,		_lowerCAmelCase							=							librosa.load(batch[data_args.speech_file_column]     , sr=feature_extractor.sampling_rate  )
														return batch

							# load audio files into numpy arrays
							_lowerCAmelCase							=							datasets.map(
							    snake_case     , num_proc=data_args.preprocessing_num_workers     , remove_columns=datasets['train'].column_names  )

							# filter audio files that are too long
							_lowerCAmelCase							=							vectorized_datasets.filter(
							    lambda snake_case  : len(data['speech']  ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate  )  )

							def normalize(snake_case  ):
														return feature_extractor(batch['speech']     , sampling_rate=feature_extractor.sampling_rate  )

							# normalize and transform to `BatchFeatures`
							_lowerCAmelCase							=							vectorized_datasets.map(
							    snake_case     , batched=snake_case     , num_proc=data_args.preprocessing_num_workers     , load_from_cache_file=not data_args.overwrite_cache     , remove_columns=vectorized_datasets['train'].column_names     , )

							# pretraining is only supported for "newer" stable layer norm architecture
							# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
							_lowerCAmelCase							=							WavaVecaConfig.from_pretrained(
							    model_args.model_name_or_path     , cache_dir=model_args.cache_dir     , gradient_checkpointing=training_args.gradient_checkpointing     , )

							if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
														raise ValueError(
														    'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
														    ' ``config.feat_extract_norm=\'layer\''  )

							_lowerCAmelCase							=							WavaVecaForPreTraining(snake_case  )

							_lowerCAmelCase							=							DataCollatorForWavaVecaPretraining(model=snake_case     , feature_extractor=snake_case  )

							_lowerCAmelCase							=							WavaVecaPreTrainer(
							    model=snake_case     , data_collator=snake_case     , args=snake_case     , train_dataset=vectorized_datasets['train']     , eval_dataset=vectorized_datasets['validation']     , tokenizer=snake_case     , max_gumbel_temp=model_args.max_gumbel_temperature     , min_gumbel_temp=model_args.min_gumbel_temperature     , gumbel_temp_decay=model_args.gumbel_temperature_decay     , )
							trainer.train()


if __name__ == "__main__":
				main()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_lowercase:	Tuple       			=		{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="roberta-prelayernorm"





			def __init__(       self	:	int  ,  lowercase__	:	int=5_02_65  ,  lowercase__	:	str=7_68  ,  lowercase__	:	List[Any]=12  ,  lowercase__	:	Any=12  ,  lowercase__	:	Optional[Any]=30_72  ,  lowercase__	:	List[Any]="gelu"  ,  lowercase__	:	str=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Tuple=5_12  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Dict=0.0_2  ,  lowercase__	:	Tuple=1e-12  ,  lowercase__	:	Optional[int]=1  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Any="absolute"  ,  lowercase__	:	Any=True  ,  lowercase__	:	Any=None  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(pad_token_id=lowercase__  ,  bos_token_id=lowercase__  ,  eos_token_id=lowercase__  ,  **lowercase__						)

										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							position_embedding_type
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							classifier_dropout







class        lowerCamelCase__				(   UpperCAmelCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										if self.task == "multiple-choice":
																	_lowerCAmelCase							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	_lowerCAmelCase							=							{0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]						)






