





import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_a 						=      16
_a 						=      32





def lowerCAmelCase__(__snake_case       ,__snake_case = 16       ,__snake_case = "bert-base-cased"							)				->      Any:







	'''simple docstring'''







	lowerCamelCase__								=		AutoTokenizer.from_pretrained(_lowercase							)
	lowerCamelCase__								=		load_dataset('''glue'''       ,'''mrpc'''							)

	def tokenize_function(__snake_case							):
		# max_length=None => use the model max length (it's actually the default)
		lowerCamelCase__								=		tokenizer(examples['''sentence1''']       ,examples['''sentence2''']       ,truncation=_lowercase       ,max_length=_lowercase							)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	lowerCamelCase__								=		datasets.map(
	    _lowercase       ,batched=_lowercase       ,remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,load_from_cache_file=_lowercase							)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	lowerCamelCase__								=		tokenized_datasets.rename_column('''label'''       ,'''labels'''							)

	def collate_fn(__snake_case							):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_lowercase       ,padding='''max_length'''       ,max_length=128       ,return_tensors='''pt'''							)
		return tokenizer.pad(_lowercase       ,padding='''longest'''       ,return_tensors='''pt'''							)

	# Instantiate dataloaders.
	lowerCamelCase__								=		DataLoader(
	    tokenized_datasets['''train''']       ,shuffle=_lowercase       ,collate_fn=_lowercase       ,batch_size=_lowercase							)
	lowerCamelCase__								=		DataLoader(
	    tokenized_datasets['''validation''']       ,shuffle=_lowercase       ,collate_fn=_lowercase       ,batch_size=_lowercase							)

	return train_dataloader, eval_dataloader





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      str:







	'''simple docstring'''







	lowerCamelCase__								=		Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowerCamelCase__								=		config['''lr''']
	lowerCamelCase__								=		int(config['''num_epochs''']							)
	lowerCamelCase__								=		int(config['''seed''']							)
	lowerCamelCase__								=		int(config['''batch_size''']							)
	lowerCamelCase__								=		args.model_name_or_path

	set_seed(_lowercase							)
	lowerCamelCase__								=		get_dataloaders(_lowercase       ,_lowercase       ,_lowercase							)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowerCamelCase__								=		AutoModelForSequenceClassification.from_pretrained(_lowercase       ,return_dict=_lowercase							)

	# Instantiate optimizer
	lowerCamelCase__								=		(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	lowerCamelCase__								=		optimizer_cls(params=model.parameters()       ,lr=_lowercase							)

	if accelerator.state.deepspeed_plugin is not None:
		lowerCamelCase__								=		accelerator.state.deepspeed_plugin.deepspeed_config[
		    '''gradient_accumulation_steps'''
		]
	else:
		lowerCamelCase__								=		1
	lowerCamelCase__								=		(len(_lowercase							) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		lowerCamelCase__								=		get_linear_schedule_with_warmup(
		    optimizer=_lowercase       ,num_warmup_steps=0       ,num_training_steps=_lowercase       ,)
	else:
		lowerCamelCase__								=		DummyScheduler(_lowercase       ,total_num_steps=_lowercase       ,warmup_num_steps=0							)

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	lowerCamelCase__								=		accelerator.prepare(
	    _lowercase       ,_lowercase       ,_lowercase       ,_lowercase       ,_lowercase							)

	# We need to keep track of how many total steps we have iterated over
	lowerCamelCase__								=		0
	# We also need to keep track of the stating epoch so files are named properly
	lowerCamelCase__								=		0

	# Now we train the model
	lowerCamelCase__								=		evaluate.load('''glue'''       ,'''mrpc'''							)
	lowerCamelCase__								=		0
	lowerCamelCase__								=		{}
	for epoch in range(_lowercase       ,_lowercase							):
		model.train()
		for step, batch in enumerate(_lowercase							):
			lowerCamelCase__								=		model(**_lowercase							)
			lowerCamelCase__								=		outputs.loss
			lowerCamelCase__								=		loss / gradient_accumulation_steps
			accelerator.backward(_lowercase							)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		lowerCamelCase__								=		0
		for step, batch in enumerate(_lowercase							):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device							)
			with torch.no_grad():
				lowerCamelCase__								=		model(**_lowercase							)
			lowerCamelCase__								=		outputs.logits.argmax(dim=-1							)
			# It is slightly faster to call this once, than multiple times
			lowerCamelCase__								=		accelerator.gather(
			    (predictions, batch['''labels'''])							)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(_lowercase							) - 1:
					lowerCamelCase__								=		predictions[: len(eval_dataloader.dataset							) - samples_seen]
					lowerCamelCase__								=		references[: len(eval_dataloader.dataset							) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=_lowercase       ,references=_lowercase       ,)

		lowerCamelCase__								=		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'       ,_lowercase							)
		lowerCamelCase__								=		eval_metric['''accuracy''']

		if best_performance < eval_metric["accuracy"]:
			lowerCamelCase__								=		eval_metric['''accuracy''']

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir       ,'''all_results.json'''							)       ,'''w'''							) as f:
			json.dump(_lowercase       ,_lowercase							)





def lowerCAmelCase__()				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''							)
	parser.add_argument(
	    '''--model_name_or_path'''       ,type=_lowercase       ,default='''bert-base-cased'''       ,help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,required=_lowercase       ,)
	parser.add_argument(
	    '''--output_dir'''       ,type=_lowercase       ,default='''.'''       ,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,)
	parser.add_argument(
	    '''--performance_lower_bound'''       ,type=_lowercase       ,default=_lowercase       ,help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''       ,)
	parser.add_argument(
	    '''--num_epochs'''       ,type=_lowercase       ,default=3       ,help='''Number of train epochs.'''       ,)
	lowerCamelCase__								=		parser.parse_args()
	lowerCamelCase__								=		{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
	training_function(_lowercase       ,_lowercase							)


if __name__ == "__main__":
			main()




from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


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





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""input_features""", """attention_mask"""]


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				List[str]=80    ,		UpperCamelCase__	:				Tuple=1_6000    ,		UpperCamelCase__	:				Dict=0.0    ,		UpperCamelCase__	:				Tuple=10    ,		UpperCamelCase__	:				int=25    ,		UpperCamelCase__	:				Optional[Any]="hamming_window"    ,		UpperCamelCase__	:				Tuple=3_2768.0    ,		UpperCamelCase__	:				str=0.97    ,		UpperCamelCase__	:				List[str]=1.0    ,		UpperCamelCase__	:				Optional[int]=True    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				List[str]=False    ,		**UpperCamelCase__	:				List[str]    ,		):





												'''simple docstring'''





												super().__init__(feature_size=UpperCamelCase__    ,		sampling_rate=UpperCamelCase__    ,		padding_value=UpperCamelCase__    ,		**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Dict							=							feature_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							sampling_rate
												SCREAMING_SNAKE_CASE							:    int							=							padding_value
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							hop_length
												SCREAMING_SNAKE_CASE							:    Tuple							=							win_length
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							frame_signal_scale
												SCREAMING_SNAKE_CASE							:    int							=							preemphasis_coeff
												SCREAMING_SNAKE_CASE							:    List[Any]							=							mel_floor
												SCREAMING_SNAKE_CASE							:    int							=							normalize_means
												SCREAMING_SNAKE_CASE							:    List[str]							=							normalize_vars
												SCREAMING_SNAKE_CASE							:    Any							=							win_function
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							return_attention_mask

												SCREAMING_SNAKE_CASE							:    int							=							win_length * sampling_rate // 1000
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							hop_length * sampling_rate // 1000

												SCREAMING_SNAKE_CASE							:    int							=							optimal_fft_length(self.sample_size						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							(self.n_fft // 2) + 1


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				np.array						):





												'''simple docstring'''





												if self.win_function == "hamming_window":
																		SCREAMING_SNAKE_CASE							:    List[str]							=							window_function(window_length=self.sample_size    ,		name=self.win_function    ,		periodic=UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							window_function(window_length=self.sample_size    ,		name=self.win_function						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mel_filter_bank(
												    num_frequency_bins=self.n_freqs    ,		num_mel_filters=self.feature_size    ,		min_frequency=0.0    ,		max_frequency=self.sampling_rate / 2.0    ,		sampling_rate=self.sampling_rate    ,		)

												SCREAMING_SNAKE_CASE							:    Tuple							=							spectrogram(
												    one_waveform * self.frame_signal_scale    ,		window=UpperCamelCase__    ,		frame_length=self.sample_size    ,		hop_length=self.sample_stride    ,		fft_length=self.n_fft    ,		center=UpperCamelCase__    ,		preemphasis=self.preemphasis_coeff    ,		mel_filters=UpperCamelCase__    ,		mel_floor=self.mel_floor    ,		log_mel='''log'''    ,		)
												return msfc_features.T


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												if self.normalize_means:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].mean(axis=0						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							np.subtract(UpperCamelCase__    ,		UpperCamelCase__						)
												if self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].std(axis=0						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							np.divide(UpperCamelCase__    ,		UpperCamelCase__						)

												if input_length < x.shape[0]:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							padding_value

												# make sure array is in float32
												SCREAMING_SNAKE_CASE							:    str							=							x.astype(np.floataa						)

												return x


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[np.ndarray]    ,		UpperCamelCase__	:				Optional[np.ndarray] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [self._normalize_one(UpperCamelCase__    ,		UpperCamelCase__    ,		self.padding_value						) for x, n in zip(UpperCamelCase__    ,		UpperCamelCase__						)]



						def __call__(       self	:				Dict    ,		UpperCamelCase__	:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,		UpperCamelCase__	:				Union[bool, str, PaddingStrategy] = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		**UpperCamelCase__	:				Optional[int]    ,		):





												'''simple docstring'''





												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																								    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
																								    f""" {self.sampling_rate} and not {sampling_rate}."""						)
												else:
																		logger.warning(
																		    '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
																		    '''Failing to do so can result in silent errors that might be hard to debug.'''						)

												SCREAMING_SNAKE_CASE							:    Any							=							isinstance(UpperCamelCase__    ,		np.ndarray						) and len(raw_speech.shape						) > 1
												if is_batched_numpy and len(raw_speech.shape						) > 2:
																		raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""						)
												SCREAMING_SNAKE_CASE							:    str							=							is_batched_numpy or (
												    isinstance(UpperCamelCase__    ,		(list, tuple)						) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)						))
												)

												if is_batched:
																		SCREAMING_SNAKE_CASE							:    int							=							[np.asarray(UpperCamelCase__    ,		dtype=np.floataa						) for speech in raw_speech]
												elif not is_batched and not isinstance(UpperCamelCase__    ,		np.ndarray						):
																		SCREAMING_SNAKE_CASE							:    Dict							=							np.asarray(UpperCamelCase__    ,		dtype=np.floataa						)
												elif isinstance(UpperCamelCase__    ,		np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							raw_speech.astype(np.floataa						)

												# always return batch
												if not is_batched:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							[raw_speech]

												# extract fbank features
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[self._extract_mfsc_features(UpperCamelCase__						) for one_waveform in raw_speech]

												# convert into correct format for padding
												SCREAMING_SNAKE_CASE							:    Tuple							=							BatchFeature({'''input_features''': features}						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.pad(
												    UpperCamelCase__    ,		padding=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		pad_to_multiple_of=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												# make sure list is in array format
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''input_features'''						)
												if isinstance(input_features[0]    ,		UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    Any							=							[np.asarray(UpperCamelCase__    ,		dtype=np.floataa						) for feature in input_features]

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''attention_mask'''						)
												if attention_mask is not None:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							[np.asarray(UpperCamelCase__    ,		dtype=np.intaa						) for array in attention_mask]

												if self.normalize_means or self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
																		    np.array(UpperCamelCase__    ,		dtype=np.intaa						)
																		    if self._get_padding_strategies(UpperCamelCase__    ,		max_length=UpperCamelCase__						) is not PaddingStrategy.DO_NOT_PAD
																		    and padding
																		    else None
																		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							self.normalize(
																		    padded_inputs['''input_features''']    ,		attention_mask=UpperCamelCase__						)

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.convert_to_tensors(UpperCamelCase__						)

												return padded_inputs





