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"""):
	_UpperCAmelCase            =		True
	from torch.cuda.amp import autocast


_UpperCAmelCase            =		logging.getLogger(__name__)





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
				lowerCamelCase_    = field(
				    default=__A ,	metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,	)
				lowerCamelCase_    = field(
				    default=__A ,	metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}  )
				lowerCamelCase_    = field(
				    default=__A ,	metadata={'''help''': '''Whether to log verbose messages or not.'''} ,	)
				lowerCamelCase_    = field(
				    default=2.0 ,	metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}  )
				lowerCamelCase_    = field(
				    default=0.5 ,	metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}  )
				lowerCamelCase_    = field(
				    default=0.999_995 ,	metadata={'''help''': '''Decay of gumbel temperature during training.'''}  )




def     UpperCamelCase						(	__lowercase						:			ModelArguments						,__lowercase						:			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     )]						,)
		A_		:    Union[str, Any]         =				logging.WARNING
		if model_args.verbose_logging:
				A_		:    int         =				logging.DEBUG
		elif trainer_utils.is_main_process(training_args.local_rank     ):
				A_		:    Tuple         =				logging.INFO
		logger.setLevel(__lowercase     )





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






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





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = "longest"
				lowerCamelCase_    = None
				lowerCamelCase_    = None
				def __call__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    str         =				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'		,     )
						A_		:    Union[str, Any]         =				self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1] )

						A_		:    int         =				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
								A_		:    Any         =				self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1 ) ).to(
								    torch.long )

								A_		:    Any         =				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
								A_		:    int         =				1
								A_		:    Optional[int]         =				attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()

						# sample randomly masked indices
						A_		:    Dict         =				_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 UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     *lowercase		,     lowercase=1		,     lowercase=0		,     lowercase=1.0		,     **lowercase ):



						"""simple docstring"""





						super().__init__(*lowercase		,     **lowercase )
						A_		:    List[str]         =				0
						A_		:    str         =				max_gumbel_temp
						A_		:    Optional[Any]         =				min_gumbel_temp
						A_		:    Any         =				gumbel_temp_decay







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						model.train()
						A_		:    str         =				self._prepare_inputs(lowercase )

						if self.use_amp:
								with autocast():
										A_		:    Optional[int]         =				self.compute_loss(lowercase		,     lowercase )
						else:
								A_		:    int         =				self.compute_loss(lowercase		,     lowercase )

						if self.args.n_gpu > 1 or self.deepspeed:
								if model.module.config.ctc_loss_reduction == "mean":
										A_		:    Optional[Any]         =				loss.mean()
								elif model.module.config.ctc_loss_reduction == "sum":
										A_		:    List[Any]         =				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:
								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()

						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     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Any         =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

		A_       ,      A_       ,      A_		:    List[str]         =				parser.parse_args_into_dataclasses()
		configure_logger(__lowercase						,__lowercase     )

		# Downloading and loading a dataset from the hub.
		A_		:    int         =				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"
				A_		:    Any         =				DatasetDict()
				A_		:    Dict         =				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						,)
				A_		:    Tuple         =				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"
				A_		:    Tuple         =				DatasetDict()
				A_		:    Dict         =				load_dataset(
				    data_args.dataset_name						,data_args.dataset_config_name						,split='validation'						,cache_dir=model_args.cache_dir						,)
				A_		:    Union[str, Any]         =				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
		A_		:    Dict         =				WavaVecaFeatureExtractor.from_pretrained(
		    model_args.model_name_or_path						,cache_dir=model_args.cache_dir						,do_normalize=__lowercase     )

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

		# load audio files into numpy arrays
		A_		:    List[Any]         =				datasets.map(
		    __lowercase						,num_proc=data_args.preprocessing_num_workers						,remove_columns=datasets['train'].column_names     )

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

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

		# normalize and transform to `BatchFeatures`
		A_		:    Optional[int]         =				vectorized_datasets.map(
		    __lowercase						,batched=__lowercase						,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
		A_		:    int         =				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\''     )

		A_		:    int         =				WavaVecaForPreTraining(__lowercase     )

		A_		:    Union[str, Any]         =				DataCollatorForWavaVecaPretraining(model=__lowercase						,feature_extractor=__lowercase     )

		A_		:    Union[str, Any]         =				WavaVecaPreTrainer(
		    model=__lowercase						,data_collator=__lowercase						,args=__lowercase						,train_dataset=vectorized_datasets['train']						,eval_dataset=vectorized_datasets['validation']						,tokenizer=__lowercase						,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()







import math




def     UpperCamelCase						(	__lowercase						:			int = 1_00     ):
		'''simple docstring'''




		A_		:    List[Any]         =				sum(i * i for i in range(1						,n + 1     )     )
		A_		:    int         =				int(math.pow(sum(range(1						,n + 1     )     )						,2     )     )
		return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")






