






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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


A__			:    int         =				16
A__			:    int         =				32






def 						a    (		lowerCamelCase_	,     lowerCamelCase_ = 16       ):






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

				def tokenize_function(lowerCamelCase_       ):
								# max_length=None => use the model max length (it's actually the default)
								lowercase__     				=    tokenizer(examples['''sentence1''']	,     examples['''sentence2''']	,     truncation=lowerCamelCase_	,     max_length=lowerCamelCase_       )
								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():
								lowercase__     				=    datasets.map(
								    lowerCamelCase_	,     batched=lowerCamelCase_	,     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
				lowercase__     				=    tokenized_datasets.rename_column('''label'''	,     '''labels'''       )

				def collate_fn(lowerCamelCase_       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase__     				=    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":
												lowercase__     				=    16
								elif accelerator.mixed_precision != "no":
												lowercase__     				=    8
								else:
												lowercase__     				=    None

								return tokenizer.pad(
								    lowerCamelCase_	,     padding='''longest'''	,     max_length=lowerCamelCase_	,     pad_to_multiple_of=lowerCamelCase_	,     return_tensors='''pt'''	,     )

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

				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

							A__			:    Dict         =				mocked_dataloaders  # noqa: F811






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,     lowerCamelCase_       ) == "1":
								lowercase__     				=    2
				# New Code #
				lowercase__     				=    int(args.gradient_accumulation_steps       )
				# Initialize accelerator
				lowercase__     				=    Accelerator(
				    cpu=args.cpu	,     mixed_precision=args.mixed_precision	,     gradient_accumulation_steps=lowerCamelCase_       )
				if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
								raise NotImplementedError(
								    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowercase__     				=    config['''lr''']
				lowercase__     				=    int(config['''num_epochs''']       )
				lowercase__     				=    int(config['''seed''']       )
				lowercase__     				=    int(config['''batch_size''']       )

				lowercase__     				=    evaluate.load('''glue'''	,     '''mrpc'''       )

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

				# 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).
				lowercase__     				=    model.to(accelerator.device       )

				# Instantiate optimizer
				lowercase__     				=    AdamW(params=model.parameters()	,     lr=lowerCamelCase_       )

				# Instantiate scheduler
				lowercase__     				=    get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase_	,     num_warmup_steps=100	,     num_training_steps=(len(lowerCamelCase_       ) * 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.
				lowercase__				,		lowercase__				,		lowercase__				,		lowercase__				,		lowercase__     				=    accelerator.prepare(
				    lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       )

				# Now we train the model
				for epoch in range(lowerCamelCase_       ):
								model.train()
								for step, batch in enumerate(lowerCamelCase_       ):
												# 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(lowerCamelCase_       ):
																lowercase__     				=    model(**lowerCamelCase_       )
																lowercase__     				=    output.loss
																accelerator.backward(lowerCamelCase_       )
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								lowercase__     				=    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""	,     lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    argparse.ArgumentParser(description='''Simple example of training script.'''       )
				parser.add_argument(
				    '''--mixed_precision'''	,     type=lowerCamelCase_	,     default=lowerCamelCase_	,     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=lowerCamelCase_	,     default=1	,     help='''The number of minibatches to be ran before gradients are accumulated.'''	,     )
				parser.add_argument('''--cpu'''	,     action='''store_true'''	,     help='''If passed, will train on the CPU.'''       )
				lowercase__     				=    parser.parse_args()
				lowercase__     				=    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCamelCase_	,     lowerCamelCase_       )


if __name__ == "__main__":
							main()












import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    args.log_outputs
				lowercase__     				=    '''_'''.join(args.dataset.split('''/'''       ) + [args.config, args.split]       )

				# load metric
				lowercase__     				=    load_metric('''wer'''       )
				lowercase__     				=    load_metric('''cer'''       )

				# compute metrics
				lowercase__     				=    wer.compute(references=result['''target''']	,     predictions=result['''prediction''']       )
				lowercase__     				=    cer.compute(references=result['''target''']	,     predictions=result['''prediction''']       )

				# print & log results
				lowercase__     				=    F"""WER: {wer_result}\nCER: {cer_result}"""
				print(lowerCamelCase_       )

				with open(F"""{dataset_id}_eval_results.txt"""	,     '''w'''       ) as f:
								f.write(lowerCamelCase_       )

				# log all results in text file. Possibly interesting for analysis
				if log_outputs is not None:
								lowercase__     				=    F"""log_{dataset_id}_predictions.txt"""
								lowercase__     				=    F"""log_{dataset_id}_targets.txt"""

								with open(lowerCamelCase_	,     '''w'''       ) as p, open(lowerCamelCase_	,     '''w'''       ) as t:
												# mapping function to write output
												def write_to_file(lowerCamelCase_	,     lowerCamelCase_       ):
																p.write(F"""{i}""" + '''\n'''       )
																p.write(batch['''prediction'''] + '''\n'''       )
																t.write(F"""{i}""" + '''\n'''       )
																t.write(batch['''target'''] + '''\n'''       )

												result.map(lowerCamelCase_	,     with_indices=lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

				lowercase__     				=    re.sub(lowerCamelCase_	,     ''''''	,     text.lower()       )

				# In addition, we can normalize the target text, e.g. removing new lines characters etc...
				# note that order is important here!
				lowercase__     				=    ['''\n\n''', '''\n''', '''   ''', '''  ''']

				for t in token_sequences_to_ignore:
								lowercase__     				=    ''' '''.join(text.split(lowerCamelCase_       )       )

				return text






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				# load dataset
				lowercase__     				=    load_dataset(args.dataset	,     args.config	,     split=args.split	,     use_auth_token=lowerCamelCase_       )

				# for testing: only process the first two examples as a test
				# dataset = dataset.select(range(10))

				# load processor
				lowercase__     				=    AutoFeatureExtractor.from_pretrained(args.model_id       )
				lowercase__     				=    feature_extractor.sampling_rate

				# resample audio
				lowercase__     				=    dataset.cast_column('''audio'''	,     Audio(sampling_rate=lowerCamelCase_       )       )

				# load eval pipeline
				if args.device is None:
								lowercase__     				=    0 if torch.cuda.is_available() else -1
				lowercase__     				=    pipeline('''automatic-speech-recognition'''	,     model=args.model_id	,     device=args.device       )

				# map function to decode audio
				def map_to_pred(lowerCamelCase_       ):
								lowercase__     				=    asr(
								    batch['''audio''']['''array''']	,     chunk_length_s=args.chunk_length_s	,     stride_length_s=args.stride_length_s       )

								lowercase__     				=    prediction['''text''']
								lowercase__     				=    normalize_text(batch['''sentence''']       )
								return batch

				# run inference on all examples
				lowercase__     				=    dataset.map(lowerCamelCase_	,     remove_columns=dataset.column_names       )

				# compute and log_results
				# do not change function below
				log_results(lowerCamelCase_	,     lowerCamelCase_       )


if __name__ == "__main__":
							A__			:    int         =				argparse.ArgumentParser()

							parser.add_argument(
							    '--model_id', type=str, required=True, help='Model identifier. Should be loadable with 🤗 Transformers'
							)
							parser.add_argument(
							    '--dataset',
							    type=str,
							    required=True,
							    help='Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets',
							)
							parser.add_argument(
							    '--config', type=str, required=True, help='Config of the dataset. *E.g.* `\'en\'`  for Common Voice'
							)
							parser.add_argument('--split', type=str, required=True, help='Split of the dataset. *E.g.* `\'test\'`')
							parser.add_argument(
							    '--chunk_length_s', type=float, default=None, help='Chunk length in seconds. Defaults to 5 seconds.'
							)
							parser.add_argument(
							    '--stride_length_s', type=float, default=None, help='Stride of the audio chunks. Defaults to 1 second.'
							)
							parser.add_argument(
							    '--log_outputs', action='store_true', help='If defined, write outputs to log file for analysis.'
							)
							parser.add_argument(
							    '--device',
							    type=int,
							    default=None,
							    help='The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.',
							)
							A__			:    Union[str, Any]         =				parser.parse_args()

							main(args)




