
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 :			Optional[Any]              =	True
					from torch.cuda.amp import autocast


_UpperCAmelCase :			Optional[Any]              =	logging.getLogger(__name__)


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






def 				A	( lowercase   ,	lowercase			)       ->  str:
		'''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          =					logging.WARNING
		if model_args.verbose_logging:
				UpperCamelCase          =					logging.DEBUG
		elif trainer_utils.is_main_process(training_args.local_rank			):
				UpperCamelCase          =					logging.INFO
		logger.setLevel(lowercase			)


@dataclass
class     lowercase :
		__lowercase	:	str     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "The name of the dataset to use (via the datasets library)."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
		__lowercase	:	Optional[str]     					=    field(
		    default="train"  ,       metadata={
		        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
		    }  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default="validation"  ,       metadata={
		        "help": (
		            "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
		        )
		    }  ,       )
		__lowercase	:	Optional[str]     					=    field(
		    default="file"  ,       metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}  ,       )
		__lowercase	:	bool     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "Overwrite the cached preprocessed datasets or not."}       )
		__lowercase	:	Optional[int]     					=    field(
		    default=1  ,       metadata={
		        "help": "The percentage of the train set used as validation set in case there's no validation split"
		    }  ,       )
		__lowercase	:	Optional[int]     					=    field(
		    default=_SCREAMING_SNAKE_CASE  ,       metadata={"help": "The number of processes to use for the preprocessing."}  ,       )
		__lowercase	:	Optional[float]     					=    field(
		    default=2_0.0  ,       metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}       )






@dataclass
class     lowercase :
		__lowercase	:	WavaVecaForPreTraining
		__lowercase	:	WavaVecaFeatureExtractor
		__lowercase	:	Union[bool, str]     					=    "longest"
		__lowercase	:	Optional[int]     					=    None
		__lowercase	:	Optional[int]     					=    None



		def __call__(			self    ,				A_	)  -> Dict[str, torch.Tensor]:
				"""simple docstring"""
				# reformat list to dict and set to pytorch format
				UpperCamelCase          =					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          =					self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]	)

				UpperCamelCase          =					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          =					self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1	)	).to(
						    torch.long	)

						UpperCamelCase          =					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          =					1
						UpperCamelCase          =					attention_mask.flip([-1]	).cumsum(-1	).flip([-1]	).bool()

				# sample randomly masked indices
				UpperCamelCase          =					_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     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				*A_    ,				A_=1    ,				A_=0    ,				A_=1.0    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				super().__init__(*A_    ,				**A_	)
				UpperCamelCase          =					0
				UpperCamelCase          =					max_gumbel_temp
				UpperCamelCase          =					min_gumbel_temp
				UpperCamelCase          =					gumbel_temp_decay



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> torch.Tensor:
				"""simple docstring"""
				model.train()
				UpperCamelCase          =					self._prepare_inputs(A_	)

				if self.use_amp:
						with autocast():
								UpperCamelCase          =					self.compute_loss(A_    ,				A_	)
				else:
						UpperCamelCase          =					self.compute_loss(A_    ,				A_	)

				if self.args.n_gpu > 1 or self.deepspeed:
						if model.module.config.ctc_loss_reduction == "mean":
								UpperCamelCase          =					loss.mean()
						elif model.module.config.ctc_loss_reduction == "sum":
								UpperCamelCase          =					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          =					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 				A	( )       ->  List[Any]:
		'''simple docstring'''

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

		UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					parser.parse_args_into_dataclasses()
		configure_logger(lowercase   ,	lowercase			)

		# Downloading and loading a dataset from the hub.
		UpperCamelCase          =					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          =					DatasetDict()
				UpperCamelCase          =					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          =					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          =					DatasetDict()
				UpperCamelCase          =					load_dataset(
				    data_args.dataset_name   ,	data_args.dataset_config_name   ,	split='validation'   ,	cache_dir=model_args.cache_dir   ,	)
				UpperCamelCase          =					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          =					WavaVecaFeatureExtractor.from_pretrained(
		    model_args.model_name_or_path   ,	cache_dir=model_args.cache_dir   ,	do_normalize=lowercase			)

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

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

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

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

		# normalize and transform to `BatchFeatures`
		UpperCamelCase          =					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
		UpperCamelCase          =					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          =					WavaVecaForPreTraining(lowercase			)

		UpperCamelCase          =					DataCollatorForWavaVecaPretraining(model=lowercase   ,	feature_extractor=lowercase			)

		UpperCamelCase          =					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()


from __future__ import annotations


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

		UpperCamelCase          =					[]
		UpperCamelCase     ,  UpperCamelCase          =					input_list[low:mid], input_list[mid : high + 1]
		while left and right:
				result.append((left if left[0] <= right[0] else right).pop(0			)			)
		UpperCamelCase          =					result + left + right
		return input_list


def 				A	( lowercase			)       ->  list:
		'''simple docstring'''

		if len(lowercase			) <= 1:
				return input_list
		UpperCamelCase          =					list(lowercase			)

		# iteration for two-way merging
		UpperCamelCase          =					2
		while p <= len(lowercase			):
				# getting low, high and middle value for merge-sort of single list
				for i in range(0   ,	len(lowercase			)   ,	lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					i + p - 1
						UpperCamelCase          =					(low + high + 1) // 2
						UpperCamelCase          =					merge(lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)
				# final merge of last two parts
				if p * 2 >= len(lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					merge(lowercase   ,	0   ,	lowercase   ,	len(lowercase			) - 1			)
						break
				p *= 2

		return input_list


if __name__ == "__main__":
					_UpperCAmelCase :			Any              =	input("Enter numbers separated by a comma:\n").strip()
					if user_input == "":
										_UpperCAmelCase :			Optional[Any]              =	[]
					else:
										_UpperCAmelCase :			Any              =	[int(item.strip()) for item in user_input.split(",")]
					print(iter_merge_sort(unsorted))
