

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 the experiment tracking capability,
# 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
#
########################################################################

A_ :				Optional[Any]						 =    16
A_ :				int						 =    32

def 		UpperCamelCase      (lowercase_:				Accelerator					,   lowercase_:				int = 16      )  ->   Any:
	A__							:					str       =	AutoTokenizer.from_pretrained("""bert-base-cased"""      )
	A__							:					Dict       =	load_dataset("""glue"""					,   """mrpc"""      )

	def tokenize_function(lowercase_:				Union[str, Any]      ):
		# max_length=None => use the model max length (it's actually the default)
		A__							:					Dict       =	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
	# starting with the main process first:
	with accelerator.main_process_first():
		A__							:					Union[str, Any]       =	datasets.map(
		    lowercase_					,   batched=lowercase_					,   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__							:					Dict       =	tokenized_datasets.rename_column("""label"""					,   """labels"""      )

	def collate_fn(lowercase_:				Optional[int]      ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A__							:					List[Any]       =	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__							:					Optional[Any]       =	16
		elif accelerator.mixed_precision != "no":
			A__							:					str       =	8
		else:
			A__							:					Tuple       =	None

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

	# Instantiate dataloaders.
	A__							:					List[str]       =	DataLoader(
	    tokenized_datasets["""train"""]					,   shuffle=lowercase_					,   collate_fn=lowercase_					,   batch_size=lowercase_      )
	A__							:					Optional[int]       =	DataLoader(
	    tokenized_datasets["""validation"""]					,   shuffle=lowercase_					,   collate_fn=lowercase_					,   batch_size=lowercase_      )

	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_ :				Tuple						 =    mocked_dataloaders  # noqa: F811

def 		UpperCamelCase      (lowercase_:				Any					,   lowercase_:				Dict      )  ->   Union[str, Any]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,   lowercase_      ) == "1":
		A__							:					int       =	2
	# Initialize Accelerator

	# New Code #
	# We pass in "all" to `log_with` to grab all available trackers in the environment
	# Note: If using a custom `Tracker` class, should be passed in here such as:
	# >>> log_with = ["all", MyCustomTrackerClassInstance()]
	if args.with_tracking:
		A__							:					List[str]       =	Accelerator(
		    cpu=args.cpu					,   mixed_precision=args.mixed_precision					,   log_with="""all"""					,   project_dir=args.project_dir      )
	else:
		A__							:					Any       =	Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision      )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A__							:					Tuple       =	config["""lr"""]
	A__							:					List[str]       =	int(config["""num_epochs"""]      )
	A__							:					Dict       =	int(config["""seed"""]      )
	A__							:					Dict       =	int(config["""batch_size"""]      )
	set_seed(lowercase_      )

	A__     ,     A__							:					List[str]       =	get_dataloaders(lowercase_					,   lowercase_      )
	A__							:					Any       =	evaluate.load("""glue"""					,   """mrpc"""      )

	# If the batch size is too big we use gradient accumulation
	A__							:					Any       =	1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A__							:					Union[str, Any]       =	batch_size // MAX_GPU_BATCH_SIZE
		A__							:					Union[str, Any]       =	MAX_GPU_BATCH_SIZE

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A__							:					Optional[Any]       =	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""					,   return_dict=lowercase_      )

	# 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__							:					Tuple       =	model.to(accelerator.device      )

	# Instantiate optimizer
	A__							:					int       =	AdamW(params=model.parameters()					,   lr=lowercase_      )

	# Instantiate scheduler
	A__							:					Tuple       =	get_linear_schedule_with_warmup(
	    optimizer=lowercase_					,   num_warmup_steps=100					,   num_training_steps=(len(lowercase_      ) * 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__							:					Dict       =	accelerator.prepare(
	    lowercase_					,   lowercase_					,   lowercase_					,   lowercase_					,   lowercase_      )

	# New Code #
	# We need to initialize the trackers we use. Overall configurations can also be stored
	if args.with_tracking:
		A__							:					List[Any]       =	os.path.split(lowercase_      )[-1].split("""."""      )[0]
		accelerator.init_trackers(lowercase_					,   lowercase_      )

	# Now we train the model
	for epoch in range(lowercase_      ):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			A__							:					Any       =	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      )
			A__							:					str       =	model(**lowercase_      )
			A__							:					Optional[Any]       =	outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			A__							:					List[Any]       =	loss / gradient_accumulation_steps
			accelerator.backward(lowercase_      )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		A__							:					Union[str, Any]       =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""					,   lowercase_      )

		# New Code #
		# To actually log, we call `Accelerator.log`
		# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
		if args.with_tracking:
			accelerator.log(
			    {
			        """accuracy""": eval_metric["""accuracy"""],
			        """f1""": eval_metric["""f1"""],
			        """train_loss""": total_loss.item() / len(lowercase_      ),
			        """epoch""": epoch,
			    }					,   step=lowercase_					,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
	if args.with_tracking:
		accelerator.end_training()

def 		UpperCamelCase      ()  ->   str:
	A__							:					str       =	argparse.ArgumentParser(description="""Simple example of training script."""      )
	parser.add_argument(
	    """--mixed_precision"""					,   type=lowercase_					,   default=lowercase_					,   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."""      )
	parser.add_argument(
	    """--with_tracking"""					,   action="""store_true"""					,   help="""Whether to load in all available experiment trackers from the environment and use them for logging."""					,   )
	parser.add_argument(
	    """--project_dir"""					,   type=lowercase_					,   default="""logs"""					,   help="""Location on where to store experiment tracking logs` and relevent project information"""					,   )
	A__							:					Optional[int]       =	parser.parse_args()
	A__							:					Dict       =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowercase_					,   lowercase_      )


if __name__ == "__main__":
	main()






import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


A_ :				Dict						 =    random.Random()

if is_torch_available():
	import torch

def 		UpperCamelCase      (lowercase_:				Tuple					,   lowercase_:				Tuple=1.0					,   lowercase_:				Dict=None					,   lowercase_:				int=None      )  ->   str:
	if rng is None:
		A__							:					Optional[Any]       =	global_rng

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

	return values



class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__=7					, A__=400					, A__=2000					, A__=1					, A__=0.0					, A__=1_6000					, A__=True					, A__=True					, ):
						A__							:					Any       =	parent
						A__							:					Optional[int]       =	batch_size
						A__							:					Union[str, Any]       =	min_seq_length
						A__							:					Dict       =	max_seq_length
						A__							:					Tuple       =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A__							:					str       =	feature_size
						A__							:					Optional[int]       =	padding_value
						A__							:					List[str]       =	sampling_rate
						A__							:					List[str]       =	return_attention_mask
						A__							:					int       =	do_normalize






					def 						__A    (				self    ):
						return {
						    "feature_size": self.feature_size,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "return_attention_mask": self.return_attention_mask,
						    "do_normalize": self.do_normalize,
						}






					def 						__A    (				self					, A__=False					, A__=False    ):
						def _flatten(A__    ):
							return list(itertools.chain(*A__    )    )

						if equal_length:
							A__							:					Dict       =	floats_list((self.batch_size, self.max_seq_length)    )
						else:
							# make sure that inputs increase in size
							A__							:					Union[str, Any]       =	[
							    _flatten(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__							:					Optional[int]       =	[np.asarray(A__    ) for x in speech_inputs]

						return speech_inputs



@require_torch
@require_torchaudio
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     int													= ASTFeatureExtractor






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	ASTFeatureExtractionTester(self    )






					def 						__A    (				self    ):
						# Tests that all call wrap to encode_plus and batch_encode_plus
						A__							:					Optional[int]       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
						# create three inputs of length 800, 1000, and 1200
						A__							:					List[Any]       =	[floats_list((1, x)    )[0] for x in range(800					, 1400					, 200    )]
						A__							:					Optional[Any]       =	[np.asarray(A__    ) for speech_input in speech_inputs]

						# Test not batched input
						A__							:					Tuple       =	feat_extract(speech_inputs[0]					, return_tensors="""np"""    ).input_values
						A__							:					Tuple       =	feat_extract(np_speech_inputs[0]					, return_tensors="""np"""    ).input_values
						self.assertTrue(np.allclose(A__					, A__					, atol=1e-3    )    )

						# Test batched
						A__							:					Tuple       =	feat_extract(A__					, padding=A__					, return_tensors="""np"""    ).input_values
						A__							:					Tuple       =	feat_extract(A__					, padding=A__					, return_tensors="""np"""    ).input_values
						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__							:					int       =	[floats_list((1, x)    )[0] for x in (800, 800, 800)]
						A__							:					List[str]       =	np.asarray(A__    )
						A__							:					Union[str, Any]       =	feat_extract(A__					, return_tensors="""np"""    ).input_values
						A__							:					Optional[Any]       =	feat_extract(A__					, return_tensors="""np"""    ).input_values
						for enc_seq_a, enc_seq_a in zip(A__					, A__    ):
							self.assertTrue(np.allclose(A__					, A__					, atol=1e-3    )    )






					@require_torch
					def 						__A    (				self    ):
						import torch

						A__							:					Union[str, Any]       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
						A__							:					Tuple       =	np.random.rand(100    ).astype(np.floataa    )
						A__							:					Tuple       =	np_speech_inputs.tolist()

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






					def 						__A    (				self					, A__    ):
						from datasets import load_dataset

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

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






					@require_torch
					def 						__A    (				self    ):
						# fmt: off
						A__							:					Optional[Any]       =	torch.tensor(
						    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
						     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
						     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
						     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]    )
						# fmt: on

						A__							:					Any       =	self._load_datasamples(1    )
						A__							:					Tuple       =	ASTFeatureExtractor()
						A__							:					Dict       =	feature_extractor(A__					, return_tensors="""pt"""    ).input_values
						self.assertEquals(input_values.shape					, (1, 1024, 128)    )
						self.assertTrue(torch.allclose(input_values[0, 0, :30]					, A__					, atol=1e-4    )    )



