
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						:      str									= logging.getLogger(__name__)




@dataclass
class 				lowerCamelCase__							:




	'''simple docstring'''




	lowerCAmelCase_			=  field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
	lowerCAmelCase_			=  field(
	    default=lowercase__  ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,		)
	lowerCAmelCase_			=  field(
	    default=lowercase__  ,		metadata={'help': 'Whether to freeze the feature extractor layers of the model.'})
	lowerCAmelCase_			=  field(
	    default=lowercase__  ,		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=0.999995  ,		metadata={'help': 'Decay of gumbel temperature during training.'})




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    ModelArguments		,						_lowerCamelCase				:    TrainingArguments)      ->					List[Any]:






							'''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)]		,						)
							__UpperCamelCase			:     str         =      logging.WARNING
							if model_args.verbose_logging:
														__UpperCamelCase			:     str         =      logging.DEBUG
							elif trainer_utils.is_main_process(training_args.local_rank):
														__UpperCamelCase			:     List[str]         =      logging.INFO
							logger.setLevel(_lowerCamelCase)




@dataclass
class 				lowerCamelCase__							:




	'''simple docstring'''




	lowerCAmelCase_			=  field(
	    default=lowercase__  ,		metadata={'help': 'The name of the dataset to use (via the datasets library).'})
	lowerCAmelCase_			=  field(
	    default=lowercase__  ,		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=lowercase__  ,		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=lowercase__  ,		metadata={'help': 'The number of processes to use for the preprocessing.'}  ,		)
	lowerCAmelCase_			=  field(
	    default=2_0.0  ,		metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'})




@dataclass
class 				lowerCamelCase__							:




	'''simple docstring'''




	lowerCAmelCase_			=  4_2
	lowerCAmelCase_			=  4_2
	lowerCAmelCase_			=  'longest'
	lowerCAmelCase_			=  None
	lowerCAmelCase_			=  None







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

								__UpperCamelCase			:     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
															__UpperCamelCase			:     List[Any]         =      self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1		)		).to(
															    torch.long		)

															__UpperCamelCase			:     int         =      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
															__UpperCamelCase			:     Union[str, Any]         =      1
															__UpperCamelCase			:     Optional[int]         =      attention_mask.flip([-1]		).cumsum(-1		).flip([-1]		).bool()

								# sample randomly masked indices
								__UpperCamelCase			:     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=a  ,     min_masks=2  ,     )

								return batch




class 				lowerCamelCase__							(       lowercase__):




	'''simple docstring'''




	def __init__(      self	:Union[str, Any]  ,     *a	:Dict  ,     a	:List[Any]=1  ,     a	:Optional[Any]=0  ,     a	:Tuple=1.0  ,     **a	:Tuple		)	->				Optional[int]:
								super().__init__(*a  ,     **a		)
								__UpperCamelCase			:     List[str]         =      0
								__UpperCamelCase			:     str         =      max_gumbel_temp
								__UpperCamelCase			:     str         =      min_gumbel_temp
								__UpperCamelCase			:     Union[str, Any]         =      gumbel_temp_decay







	def      _lowerCamelCase       (      self	:Any  ,     a	:nn.Module  ,     a	:Dict[str, Union[torch.Tensor, Any]]		)	->				torch.Tensor:
								model.train()
								__UpperCamelCase			:     int         =      self._prepare_inputs(a		)

								if self.use_amp:
															with autocast():
																						__UpperCamelCase			:     Optional[int]         =      self.compute_loss(a  ,     a		)
								else:
															__UpperCamelCase			:     List[str]         =      self.compute_loss(a  ,     a		)

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

								if self.use_amp:
															self.scaler.scale(a		).backward()
								elif self.use_apex:
															with amp.scale_loss(a  ,     self.optimizer		) as scaled_loss:
																						scaled_loss.backward()
								elif self.deepspeed:
															self.deepspeed.backward(a		)
								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 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))

							__UpperCamelCase			:     int         =      parser.parse_args_into_dataclasses()
							configure_logger(_lowerCamelCase		,						_lowerCamelCase)

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

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

							# load audio files into numpy arrays
							__UpperCamelCase			:     Optional[Any]         =      datasets.map(
							    _lowerCamelCase		,						num_proc=data_args.preprocessing_num_workers		,						remove_columns=datasets["train"].column_names)

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

							def normalize(_lowerCamelCase				:    Union[str, Any]):
														return feature_extractor(batch["speech"]		,						sampling_rate=feature_extractor.sampling_rate)

							# normalize and transform to `BatchFeatures`
							__UpperCamelCase			:     int         =      vectorized_datasets.map(
							    _lowerCamelCase		,						batched=_lowerCamelCase		,						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
							__UpperCamelCase			:     Any         =      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'")

							__UpperCamelCase			:     Dict         =      WavaVecaForPreTraining(_lowerCamelCase)

							__UpperCamelCase			:     Optional[int]         =      DataCollatorForWavaVecaPretraining(model=_lowerCamelCase		,						feature_extractor=_lowerCamelCase)

							__UpperCamelCase			:     Any         =      WavaVecaPreTrainer(
							    model=_lowerCamelCase		,						data_collator=_lowerCamelCase		,						args=_lowerCamelCase		,						train_dataset=vectorized_datasets["train"]		,						eval_dataset=vectorized_datasets["validation"]		,						tokenizer=_lowerCamelCase		,						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()

def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int = 200)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      [1, 2, 5, 10, 20, 50, 100, 200]
							__UpperCamelCase			:     Any         =      [0] * (pence + 1)
							__UpperCamelCase			:     Union[str, Any]         =      1  # base case: 1 way to make 0 pence

							for coin in coins:
														for i in range(_lowerCamelCase		,						pence + 1		,						1):
																					number_of_ways[i] += number_of_ways[i - coin]
							return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682