


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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_lowercase       :      Optional[Any]	=	16
_lowercase       :      List[Any]	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     int = 16						) ->   Tuple:





	"""simple docstring"""







	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				load_dataset("""glue"""							,					"""mrpc"""						)

	def tokenize_function(UpperCamelCase__:     List[Any]						):
		# max_length=None => use the model max length (it's actually the default)
		A								=				tokenizer(examples["""sentence1"""]							,					examples["""sentence2"""]							,					truncation=UpperCamelCase__							,					max_length=UpperCamelCase__						)
		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(
		    UpperCamelCase__							,					batched=UpperCamelCase__							,					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(UpperCamelCase__:     Union[str, Any]						):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A								=				1_28 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(
		    UpperCamelCase__							,					padding="""longest"""							,					max_length=UpperCamelCase__							,					pad_to_multiple_of=UpperCamelCase__							,					return_tensors="""pt"""							,					)

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

	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

				_lowercase       :      List[Any]	=	mocked_dataloaders  # noqa: F811





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,					UpperCamelCase__						) == "1":
		A								=				2
	# Initialize accelerator
	A								=				Accelerator(cpu=args.cpu							,					mixed_precision=args.mixed_precision						)
	# 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"""						)

	# If the batch size is too big we use gradient accumulation
	A								=				1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A								=				batch_size // MAX_GPU_BATCH_SIZE
		A								=				MAX_GPU_BATCH_SIZE

	set_seed(UpperCamelCase__						)
	A					,			A								=				get_dataloaders(UpperCamelCase__							,					UpperCamelCase__						)
	# 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=UpperCamelCase__						)

	# 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=UpperCamelCase__						)

	# Instantiate scheduler
	A								=				get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__							,					num_warmup_steps=1_00							,					num_training_steps=(len(UpperCamelCase__						) * num_epochs) // gradient_accumulation_steps							,					)

	# 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(
	    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

	# Now we train the model
	for epoch in range(UpperCamelCase__						):
		model.train()
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			A								=				model(**UpperCamelCase__						)
			A								=				outputs.loss
			A								=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase__						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		A								=				0
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				A								=				model(**UpperCamelCase__						)
			A								=				outputs.logits.argmax(dim=-1						)
			A					,			A								=				accelerator.gather((predictions, batch["""labels"""])						)
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(UpperCamelCase__						) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					A								=				predictions[: len(eval_dataloader.dataset						) - samples_seen]
					A								=				references[: len(eval_dataloader.dataset						) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=UpperCamelCase__							,					references=UpperCamelCase__							,					)

		A								=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   ) ->   Dict:





	"""simple docstring"""







	A								=				argparse.ArgumentParser(description="""Simple example of training script."""						)
	parser.add_argument(
	    """--mixed_precision"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					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."""							,					)
	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(UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				main()







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import SpeechaTextFeatureExtractor

_lowercase       :      Tuple	=	random.Random()





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]							,					UpperCamelCase__:     str=1.0							,					UpperCamelCase__:     int=None							,					UpperCamelCase__:     Optional[Any]=None						) ->   str:





	"""simple docstring"""







	if rng is None:
		A								=				global_rng

	A								=				[]
	for batch_idx in range(shape[0]						):
		values.append([]						)
		for _ in range(shape[1]						):
			values[-1].append(rng.random() * scale						)

	return values






@require_torch
@require_torchaudio
class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=7				,   a__=400				,   a__=2000				,   a__=24				,   a__=24				,   a__=0.0				,   a__=1_6000				,   a__=True				,   a__=True				,   )	->				Any:
					A								=				parent
					A								=				batch_size
					A								=				min_seq_length
					A								=				max_seq_length
					A								=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					A								=				feature_size
					A								=				num_mel_bins
					A								=				padding_value
					A								=				sampling_rate
					A								=				return_attention_mask
					A								=				do_normalize

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					return {
					    "feature_size": self.feature_size,
					    "num_mel_bins": self.num_mel_bins,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}

				def      _UpperCAmelCase					(						self				,   a__=False				,   a__=False   )	->				Tuple:
					def _flatten(a__   ):
						return list(itertools.chain(*a__   )   )

					if equal_length:
						A								=				[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
					else:
						# make sure that inputs increase in size
						A								=				[
						    floats_list((x, self.feature_size)   )
						    for x in range(self.min_seq_length				,   self.max_seq_length				,   self.seq_length_diff   )
						]
					if numpify:
						A								=				[np.asarray(a__   ) for x in speech_inputs]
					return speech_inputs






@require_torch
@require_torchaudio
class       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= SpeechaTextFeatureExtractor if is_speech_available() else None

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				SpeechaTextFeatureExtractionTester(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Any:
					self.assertTrue(np.all(np.mean(a__				,   axis=0   ) < 1e-3   )   )
					self.assertTrue(np.all(np.abs(np.var(a__				,   axis=0   ) - 1   ) < 1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					# create three inputs of length 800, 1000, and 1200
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				[np.asarray(a__   ) for speech_input in speech_inputs]

					# Test feature size
					A								=				feature_extractor(a__				,   padding=a__				,   return_tensors="""np"""   ).input_features
					self.assertTrue(input_features.ndim == 3   )
					self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

					# Test not batched input
					A								=				feature_extractor(speech_inputs[0]				,   return_tensors="""np"""   ).input_features
					A								=				feature_extractor(np_speech_inputs[0]				,   return_tensors="""np"""   ).input_features
					self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

					# Test batched
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					for enc_seq_a, enc_seq_a in zip(a__				,   a__   ):
						self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

					# Test 2-D numpy arrays are batched.
					A								=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
					A								=				np.asarray(a__   )
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					for enc_seq_a, enc_seq_a in zip(a__				,   a__   ):
						self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 16, None]
					for max_length, padding in zip(a__				,   a__   ):
						A								=				feature_extractor(
						    a__				,   padding=a__				,   max_length=a__				,   return_attention_mask=a__   )
						A								=				inputs.input_features
						A								=				inputs.attention_mask
						A								=				[np.sum(a__   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 16, None]
					for max_length, padding in zip(a__				,   a__   ):
						A								=				feature_extractor(
						    a__				,   max_length=a__				,   padding=a__				,   return_tensors="""np"""				,   return_attention_mask=a__   )
						A								=				inputs.input_features
						A								=				inputs.attention_mask
						A								=				[np.sum(a__   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""max_length"""				,   max_length=4				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""longest"""				,   max_length=4				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape				,   (3, 4, 24)   )

					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""longest"""				,   max_length=16				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape				,   (3, 6, 24)   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					import torch

					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				np.random.rand(100				,   32   ).astype(np.floataa   )
					A								=				np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
						A								=				feature_extractor.pad([{"""input_features""": inputs}]				,   return_tensors="""np"""   )
						self.assertTrue(np_processed.input_features.dtype == np.floataa   )
						A								=				feature_extractor.pad([{"""input_features""": inputs}]				,   return_tensors="""pt"""   )
						self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					from datasets import load_dataset

					A								=				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,   """clean"""				,   split="""validation"""   )
					# automatic decoding with librispeech
					A								=				ds.sort("""id"""   ).select(range(a__   )   )[:num_samples]["""audio"""]

					return [x["array"] for x in speech_samples]

				def      _UpperCAmelCase					(						self   )	->				Any:
					# fmt: off
					A								=				np.array([
					    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
					    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
					    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
					]   )
					# fmt: on

					A								=				self._load_datasamples(1   )
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				feature_extractor(a__				,   return_tensors="""pt"""   ).input_features
					self.assertEquals(input_features.shape				,   (1, 584, 24)   )
					self.assertTrue(np.allclose(input_features[0, 0, :30]				,   a__				,   atol=1e-4   )   )



