


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   UpperCamelCase__):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					["""input_features""", """is_longer"""]


							def __init__(							self  ,  a_=64  ,  a_=48_000  ,  a_=480  ,  a_=10  ,  a_=1_024  ,  a_=0.0  ,  a_=False  ,  a_ = 0  ,  a_ = 14_000  ,  a_ = None  ,  a_ = "fusion"  ,  a_ = "repeatpad"  ,  **a_  ,  ):
											super().__init__(
											    feature_size=__A  ,  sampling_rate=__A  ,  padding_value=__A  ,  return_attention_mask=__A  ,  **__A  ,  )
											a__				      =		top_db
											a__				      =		truncation
											a__				      =		padding
											a__				      =		fft_window_size
											a__				      =		(fft_window_size >> 1) + 1
											a__				      =		hop_length
											a__				      =		max_length_s
											a__				      =		max_length_s * sampling_rate
											a__				      =		sampling_rate
											a__				      =		frequency_min
											a__				      =		frequency_max
											a__				      =		mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins  ,  num_mel_filters=__A  ,  min_frequency=__A  ,  max_frequency=__A  ,  sampling_rate=__A  ,  norm=__A  ,  mel_scale="""htk"""  ,  )
											a__				      =		mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins  ,  num_mel_filters=__A  ,  min_frequency=__A  ,  max_frequency=__A  ,  sampling_rate=__A  ,  norm="""slaney"""  ,  mel_scale="""slaney"""  ,  )


							def _a      (							self		):
											a__				      =		copy.deepcopy(self.__dict__		)
											a__				      =		self.__class__.__name__
											if "mel_filters" in output:
															del output["mel_filters"]
											if "mel_filters_slaney" in output:
															del output["mel_filters_slaney"]
											return output


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		spectrogram(
											    __A  ,  window_function(self.fft_window_size  ,  """hann"""		)  ,  frame_length=self.fft_window_size  ,  hop_length=self.hop_length  ,  power=2.0  ,  mel_filters=__A  ,  log_mel="""dB"""  ,  )
											return log_mel_spectrogram.T


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		np.array_split(list(range(0  ,  total_frames - chunk_frames + 1		)		)  ,  3		)
											if len(ranges[1]		) == 0:
															# if the audio is too short, we just use the first chunk
															a__				      =		[0]
											if len(ranges[2]		) == 0:
															# if the audio is too short, we just use the first chunk
															a__				      =		[0]
											# randomly choose index for each part
											a__				      =		np.random.choice(ranges[0]		)
											a__				      =		np.random.choice(ranges[1]		)
											a__				      =		np.random.choice(ranges[2]		)

											a__				      =		mel[idx_front : idx_front + chunk_frames, :]
											a__				      =		mel[idx_middle : idx_middle + chunk_frames, :]
											a__				      =		mel[idx_back : idx_back + chunk_frames, :]

											a__				      =		torch.tensor(mel[None, None, :]		)
											a__				      =		torch.nn.functional.interpolate(
											    __A  ,  size=[chunk_frames, 64]  ,  mode="""bilinear"""  ,  align_corners=__A		)
											a__				      =		mel_shrink[0][0].numpy()
											a__				      =		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]  ,  axis=0		)
											return mel_fusion


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_		):



											if waveform.shape[0] > max_length:
															if truncation == "rand_trunc":
																			a__				      =		True
																			# random crop to max_length (for compatibility) -> this should be handled by self.pad
																			a__				      =		len(__A		) - max_length
																			a__				      =		np.random.randint(0  ,  overflow + 1		)
																			a__				      =		waveform[idx : idx + max_length]
																			a__				      =		self._np_extract_fbank_features(__A  ,  self.mel_filters_slaney		)[None, :]
															elif truncation == "fusion":
																			a__				      =		self._np_extract_fbank_features(__A  ,  self.mel_filters		)
																			a__				      =		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																			a__				      =		mel.shape[0]
																			if chunk_frames == total_frames:
																							# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																							# In this case, we just use the whole audio.
																							a__				      =		np.stack([mel, mel, mel, mel]  ,  axis=0		)
																							a__				      =		False
																			else:
																							a__				      =		self._random_mel_fusion(__A  ,  __A  ,  __A		)
																							a__				      =		True
															else:
																			raise NotImplementedError(F'''data_truncating {truncation} not implemented'''		)

											else:
															a__				      =		False
															# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
															if waveform.shape[0] < max_length:
																			if padding == "repeat":
																							a__				      =		int(max_length / len(__A		)		)
																							a__				      =		np.stack(np.tile(__A  ,  n_repeat + 1		)		)[:max_length]
																			if padding == "repeatpad":
																							a__				      =		int(max_length / len(__A		)		)
																							a__				      =		np.stack(np.tile(__A  ,  __A		)		)
																			a__				      =		np.pad(__A  ,  (0, max_length - waveform.shape[0])  ,  mode="""constant"""  ,  constant_values=0		)

															if truncation == "fusion":
																			a__				      =		self._np_extract_fbank_features(__A  ,  self.mel_filters		)
																			a__				      =		np.stack([input_mel, input_mel, input_mel, input_mel]  ,  axis=0		)
															else:
																			a__				      =		self._np_extract_fbank_features(__A  ,  self.mel_filters_slaney		)[None, :]

											return input_mel, longer







							def __call__(							self  ,  a_  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		truncation if truncation is not None else self.truncation
											a__				      =		padding if padding else self.padding

											if sampling_rate is not None:
															if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																			    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																			    F''' was sampled with {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."""		)

											a__				      =		isinstance(__A  ,  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}'''		)
											a__				      =		is_batched_numpy or (
											    isinstance(__A  ,  (list, tuple)		) and (isinstance(raw_speech[0]  ,  (np.ndarray, tuple, list)		))
											)

											if is_batched:
															a__				      =		[np.asarray(__A  ,  dtype=np.floataa		) for speech in raw_speech]
											elif not is_batched and not isinstance(__A  ,  np.ndarray		):
															a__				      =		np.asarray(__A  ,  dtype=np.floataa		)
											elif isinstance(__A  ,  np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
															a__				      =		raw_speech.astype(np.floataa		)

											# always return batch
											if not is_batched:
															a__				      =		[np.asarray(__A		)]

											# convert to mel spectrogram, truncate and pad if needed.
											a__				      =		[
											    self._get_input_mel(__A  ,  max_length if max_length else self.nb_max_samples  ,  __A  ,  __A		)
											    for waveform in raw_speech
											]

											a__				      =		[]
											a__				      =		[]
											for mel, longer in padded_inputs:
															input_mel.append(__A		)
															is_longer.append(__A		)

											if truncation == "fusion" and sum(__A		) == 0:
															# if no audio is longer than 10s, then randomly select one audio to be longer
															a__				      =		np.random.randint(0  ,  len(__A		)		)
															a__				      =		True

											if isinstance(input_mel[0]  ,  __A		):
															a__				      =		[np.asarray(__A  ,  dtype=np.floataa		) for feature in input_mel]

											# is_longer is a list of bool
											a__				      =		[[longer] for longer in is_longer]

											a__				      =		{"""input_features""": input_mel, """is_longer""": is_longer}
											a__				      =		BatchFeature(__A		)

											if return_tensors is not None:
															a__				      =		input_features.convert_to_tensors(__A		)

											return input_features






import argparse
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase							 =					16
UpperCAmelCase							 =					32

def 			A_      (	__a     :				Accelerator ,		__a     :				int = 16		):

				"""simple docstring"""
				a__				      =		AutoTokenizer.from_pretrained("""bert-base-cased"""		)
				a__				      =		load_dataset("""glue""" ,		"""mrpc"""		)

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								a__				      =		datasets.map(
								    __a ,		batched=__a ,		remove_columns=["""idx""", """sentence1""", """sentence2"""] ,		)

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

				def collate_fn(__a     :				Optional[int]		):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								a__				      =		128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												a__				      =		16
								elif accelerator.mixed_precision != "no":
												a__				      =		8
								else:
												a__				      =		None

								return tokenizer.pad(
								    __a ,		padding="""longest""" ,		max_length=__a ,		pad_to_multiple_of=__a ,		return_tensors="""pt""" ,		)

				# Instantiate dataloaders.
				a__				      =		DataLoader(
				    tokenized_datasets["""train"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)
				a__				      =		DataLoader(
				    tokenized_datasets["""validation"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	UpperCAmelCase							 =					mocked_dataloaders  # noqa: F811

def 			A_      (	__a     :				List[Any] ,		__a     :				Tuple		):

				"""simple docstring"""
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,		__a		) == "1":
								a__				      =		2
				# New Code #
				a__				      =		int(args.gradient_accumulation_steps		)
				a__				      =		int(args.local_sgd_steps		)
				# Initialize accelerator
				a__				      =		Accelerator(
				    cpu=args.cpu ,		mixed_precision=args.mixed_precision ,		gradient_accumulation_steps=__a		)
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""		)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				a__				      =		config["""lr"""]
				a__				      =		int(config["""num_epochs"""]		)
				a__				      =		int(config["""seed"""]		)
				a__				      =		int(config["""batch_size"""]		)

				a__				      =		evaluate.load("""glue""" ,		"""mrpc"""		)

				set_seed(__a		)
				a__      ,			a__				      =		get_dataloaders(__a ,		__a		)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				a__				      =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,		return_dict=__a		)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				a__				      =		model.to(accelerator.device		)

				# Instantiate optimizer
				a__				      =		AdamW(params=model.parameters() ,		lr=__a		)

				# Instantiate scheduler
				a__				      =		get_linear_schedule_with_warmup(
				    optimizer=__a ,		num_warmup_steps=100 ,		num_training_steps=(len(__a		) * num_epochs) ,		)

				# 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.
				a__      ,			a__      ,			a__      ,			a__      ,			a__				      =		accelerator.prepare(
				    __a ,		__a ,		__a ,		__a ,		__a		)

				# Now we train the model
				for epoch in range(__a		):
								model.train()
								with LocalSGD(
								    accelerator=__a ,		model=__a ,		local_sgd_steps=__a ,		enabled=local_sgd_steps is not None		) as local_sgd:
												for step, batch in enumerate(__a		):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device		)
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(__a		):
																				a__				      =		model(**__a		)
																				a__				      =		output.loss
																				accelerator.backward(__a		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								for step, batch in enumerate(__a		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												with torch.no_grad():
																a__				      =		model(**__a		)
												a__				      =		outputs.logits.argmax(dim=-1		)
												a__      ,			a__				      =		accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
												metric.add_batch(
												    predictions=__a ,		references=__a ,		)

								a__				      =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:''' ,		__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser(description="""Simple example of training script."""		)
				parser.add_argument(
				    """--mixed_precision""" ,		type=__a ,		default=__a ,		choices=["""no""", """fp16""", """bf16""", """fp8"""] ,		help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU.""" ,		)
				# New Code #
				parser.add_argument(
				    """--gradient_accumulation_steps""" ,		type=__a ,		default=1 ,		help="""The number of minibatches to be ran before gradients are accumulated.""" ,		)
				parser.add_argument(
				    """--local_sgd_steps""" ,		type=__a ,		default=8 ,		help="""Number of local SGD steps or None to disable local SGD"""		)
				parser.add_argument("""--cpu""" ,		action="""store_true""" ,		help="""If passed, will train on the CPU."""		)
				a__				      =		parser.parse_args()
				a__				      =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(__a ,		__a		)


if __name__ == "__main__":
	main()

