


'''simple docstring'''






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'):
							__A						:						str  =  True
							from torch.cuda.amp import autocast


__A						:						Optional[int]  =  logging.getLogger(__name__)
@dataclass
class     __UpperCamelCase						:
							lowercase      :		str 		=	field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
							lowercase      :		Optional[int] 		=	field(
							    default=a__    ,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,	)
							lowercase      :		int 		=	field(
							    default=a__    ,	metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}				)
							lowercase      :		Optional[int] 		=	field(
							    default=a__    ,	metadata={'help': 'Whether to log verbose messages or not.'}    ,	)
							lowercase      :		Optional[int] 		=	field(
							    default=2.0    ,	metadata={'help': 'Maximum temperature for gumbel softmax.'}				)
							lowercase      :		List[Any] 		=	field(
							    default=0.5    ,	metadata={'help': 'Minimum temperature for gumbel softmax.'}				)
							lowercase      :		List[str] 		=	field(
							    default=0.9_9_9_9_9_5    ,	metadata={'help': 'Decay of gumbel temperature during training.'}				)





def   UpperCAmelCase   (		lowerCamelCase_	:ModelArguments					,   lowerCamelCase_	: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			)]					,   )
		snake_case_		:				Union[str, Any]      							=    logging.WARNING
		if model_args.verbose_logging:
				snake_case_		:				Optional[int]      							=    logging.DEBUG
		elif trainer_utils.is_main_process(training_args.local_rank			):
				snake_case_		:				str      							=    logging.INFO
		logger.setLevel(__snake_case			)
@dataclass
class     __UpperCamelCase						:
							lowercase      :		int 		=	field(
							    default=a__    ,	metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
							lowercase      :		Tuple 		=	field(
							    default=a__    ,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
							lowercase      :		List[str] 		=	field(
							    default='train'    ,	metadata={
							        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
							    }    ,	)
							lowercase      :		Tuple 		=	field(
							    default='validation'    ,	metadata={
							        'help': (
							            'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
							        )
							    }    ,	)
							lowercase      :		str 		=	field(
							    default='file'    ,	metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}    ,	)
							lowercase      :		Dict 		=	field(
							    default=a__    ,	metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}				)
							lowercase      :		int 		=	field(
							    default=1    ,	metadata={
							        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
							    }    ,	)
							lowercase      :		Any 		=	field(
							    default=a__    ,	metadata={'help': 'The number of processes to use for the preprocessing.'}    ,	)
							lowercase      :		List[Any] 		=	field(
							    default=2_0.0    ,	metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'}				)





@dataclass
class     __UpperCamelCase						:
							lowercase      :		Optional[Any] 		=	4_2
							lowercase      :		List[str] 		=	4_2
							lowercase      :		Dict 		=	'longest'
							lowercase      :		List[str] 		=	None
							lowercase      :		Dict 		=	None





							def __call__(	self		:Tuple	,_UpperCamelCase		:int      ):
									snake_case_		:				Optional[int]      							=    self.feature_extractor.pad(
									    _UpperCAmelCase	,max_length=self.max_length	,padding=self.padding	,pad_to_multiple_of=self.pad_to_multiple_of	,return_tensors="""pt"""	,)
									snake_case_		:				Tuple      							=    self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]      )

									snake_case_		:				Any      							=    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
											snake_case_		:				Union[str, Any]      							=    self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1      )      ).to(
											    torch.long      )

											snake_case_		:				Dict      							=    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
											snake_case_		:				List[str]      							=    1
											snake_case_		:				Optional[Any]      							=    attention_mask.flip([-1]      ).cumsum(-1      ).flip([-1]      ).bool()

									# sample randomly masked indices
									snake_case_		:				Optional[Any]      							=    _compute_mask_indices(
									    (batch_size, mask_indices_seq_length)	,self.model.config.mask_time_prob	,self.model.config.mask_time_length	,attention_mask=_UpperCAmelCase	,min_masks=2	,)

									return batch





class     __UpperCamelCase						(    a__				):





							def __init__(	self		:List[str]	,*_UpperCamelCase		:Dict	,_UpperCamelCase		:Dict=1	,_UpperCamelCase		:str=0	,_UpperCamelCase		:Any=1.0	,**_UpperCamelCase		:Optional[int]      ):
									super().__init__(*_UpperCAmelCase	,**_UpperCAmelCase      )
									snake_case_		:				Tuple      							=    0
									snake_case_		:				List[Any]      							=    max_gumbel_temp
									snake_case_		:				int      							=    min_gumbel_temp
									snake_case_		:				Union[str, Any]      							=    gumbel_temp_decay





							def 		a__ (	self		:Dict	,_UpperCamelCase		:List[str]	,_UpperCamelCase		:Dict      ):
									model.train()
									snake_case_		:				Any      							=    self._prepare_inputs(_UpperCAmelCase      )

									if self.use_amp:
											with autocast():
													snake_case_		:				Any      							=    self.compute_loss(_UpperCAmelCase	,_UpperCAmelCase      )
									else:
											snake_case_		:				Tuple      							=    self.compute_loss(_UpperCAmelCase	,_UpperCAmelCase      )

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

									if self.use_amp:
											self.scaler.scale(_UpperCAmelCase      ).backward()
									elif self.use_apex:
											with amp.scale_loss(_UpperCAmelCase	,self.optimizer      ) as scaled_loss:
													scaled_loss.backward()
									elif self.deepspeed:
											self.deepspeed.backward(_UpperCAmelCase      )
									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'''
		# 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.

		snake_case_		:				Optional[int]      							=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)

		snake_case_		:				Any      							=    parser.parse_args_into_dataclasses()
		configure_logger(__snake_case					,   __snake_case			)

		# Downloading and loading a dataset from the hub.
		snake_case_		:				Optional[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"
				snake_case_		:				Optional[int]      							=    DatasetDict()
				snake_case_		:				Optional[int]      							=    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					,   )
				snake_case_		:				str      							=    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"
				snake_case_		:				Dict      							=    DatasetDict()
				snake_case_		:				List[Any]      							=    load_dataset(
				    data_args.dataset_name					,   data_args.dataset_config_name					,   split="""validation"""					,   cache_dir=model_args.cache_dir					,   )
				snake_case_		:				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
		snake_case_		:				List[Any]      							=    WavaVecaFeatureExtractor.from_pretrained(
		    model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   do_normalize=__snake_case			)

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

		# load audio files into numpy arrays
		snake_case_		:				List[str]      							=    datasets.map(
		    __snake_case					,   num_proc=data_args.preprocessing_num_workers					,   remove_columns=datasets["""train"""].column_names			)

		# filter audio files that are too long
		snake_case_		:				List[str]      							=    vectorized_datasets.filter(
		    lambda lowerCamelCase_			: len(data["""speech"""]			) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate			)			)

		def normalize(lowerCamelCase_	:int			):
				return feature_extractor(batch["""speech"""]					,   sampling_rate=feature_extractor.sampling_rate			)

		# normalize and transform to `BatchFeatures`
		snake_case_		:				str      							=    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
		snake_case_		:				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\'"""			)

		snake_case_		:				int      							=    WavaVecaForPreTraining(__snake_case			)

		snake_case_		:				Optional[int]      							=    DataCollatorForWavaVecaPretraining(model=__snake_case					,   feature_extractor=__snake_case			)

		snake_case_		:				Dict      							=    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()






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[Any]:  # noqa: E741
     __A		:					Tuple						=			len(__snake_case     )
     __A		:					Optional[int]						=			0
     __A		:					str						=			[0] * n
     __A		:					int						=			[False] * n
     __A		:					Tuple						=			[False] * n

     def dfs(__snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					int     ):
          if parent == root:
               out_edge_count += 1
          __A		:					str						=			True
          __A		:					Tuple						=			at

          for to in l[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    __A		:					Optional[int]						=			dfs(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
                    __A		:					int						=			min(low[at]					,				low[to]     )

                    # AP found via bridge
                    if at < low[to]:
                         __A		:					Tuple						=			True
                    # AP found via cycle
                    if at == low[to]:
                         __A		:					Optional[Any]						=			True
               else:
                    __A		:					Any						=			min(low[at]					,				__snake_case     )
          return out_edge_count

     for i in range(__snake_case     ):
          if not visited[i]:
               __A		:					Tuple						=			0
               __A		:					List[Any]						=			dfs(__snake_case					,				__snake_case					,				-1					,				__snake_case     )
               __A		:					Union[str, Any]						=			out_edge_count > 1

     for x in range(len(__snake_case     )     ):
          if is_art[x] is True:
               print(__snake_case     )


# Adjacency list of graph
lowercase__       : Tuple         =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)