


"""simple docstring"""






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 checkpointing 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
#
########################################################################

lowercase  		=			16
lowercase  		=			32



def 			A__		(					_UpperCAmelCase    :      Accelerator				,    _UpperCAmelCase    :      int = 16	)							->	str:

	'''simple docstring'''


	snake_case__  :						Optional[int]		=      AutoTokenizer.from_pretrained("bert-base-cased"	)
	snake_case__  :						int		=      load_dataset("glue"				,    "mrpc"	)

	def tokenize_function(_UpperCAmelCase    :      Tuple	):
		# max_length=None => use the model max length (it's actually the default)
		snake_case__  :						str		=      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():
		snake_case__  :						List[Any]		=      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
	snake_case__  :						str		=      tokenized_datasets.rename_column("label"				,    "labels"	)

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

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

	# Instantiate dataloaders.
	snake_case__  :						Tuple		=      DataLoader(
	    tokenized_datasets["train"]				,    shuffle=_UpperCAmelCase				,    collate_fn=_UpperCAmelCase				,    batch_size=_UpperCAmelCase	)
	snake_case__  :						Optional[int]		=      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  		=			mocked_dataloaders  # noqa: F811



def 			A__		(					_UpperCAmelCase    :      Any				,    _UpperCAmelCase    :      Optional[Any]	)							->	List[str]:

	'''simple docstring'''


	if os.environ.get("TESTING_MOCKED_DATALOADERS"				,    _UpperCAmelCase	) == "1":
		snake_case__  :						int		=      2
	# Initialize accelerator
	snake_case__  :						List[Any]		=      Accelerator(cpu=args.cpu				,    mixed_precision=args.mixed_precision	)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	snake_case__  :						Optional[int]		=      config["lr"]
	snake_case__  :						Optional[int]		=      int(config["num_epochs"]	)
	snake_case__  :						List[str]		=      int(config["seed"]	)
	snake_case__  :						List[Any]		=      int(config["batch_size"]	)

	# New Code #
	# Parse out whether we are saving every epoch or after a certain number of batches
	if hasattr(args.checkpointing_steps				,    "isdigit"	):
		if args.checkpointing_steps == "epoch":
			snake_case__  :						Optional[Any]		=      args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			snake_case__  :						List[Any]		=      int(args.checkpointing_steps	)
		else:
			raise ValueError(
			    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
	else:
		snake_case__  :						Optional[Any]		=      None

	set_seed(_UpperCAmelCase	)

	snake_case__,					snake_case__  :						str		=      get_dataloaders(_UpperCAmelCase				,    _UpperCAmelCase	)
	snake_case__  :						Optional[int]		=      evaluate.load("glue"				,    "mrpc"	)

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

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case__  :						Any		=      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).
	snake_case__  :						Tuple		=      model.to(accelerator.device	)

	# Instantiate optimizer
	snake_case__  :						Tuple		=      AdamW(params=model.parameters()				,    lr=_UpperCAmelCase	)

	# Instantiate scheduler
	snake_case__  :						List[Any]		=      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.
	snake_case__,					snake_case__,					snake_case__,					snake_case__,					snake_case__  :						Optional[int]		=      accelerator.prepare(
	    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)

	# New Code #
	# We need to keep track of how many total steps we have iterated over
	snake_case__  :						List[Any]		=      0
	# We also need to keep track of the stating epoch so files are named properly
	snake_case__  :						int		=      0

	# We need to load the checkpoint back in before training here with `load_state`
	# The total number of epochs is adjusted based on where the state is being loaded from,
	# as we assume continuation of the same training script
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""	)
			accelerator.load_state(args.resume_from_checkpoint	)
			snake_case__  :						Any		=      os.path.basename(args.resume_from_checkpoint	)
		else:
			# Get the most recent checkpoint
			snake_case__  :						Optional[int]		=      [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
			dirs.sort(key=os.path.getctime	)
			snake_case__  :						Tuple		=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		snake_case__  :						Any		=      os.path.splitext(_UpperCAmelCase	)[0]

		if "epoch" in training_difference:
			snake_case__  :						Dict		=      int(training_difference.replace("epoch_"				,    ""	)	) + 1
			snake_case__  :						Union[str, Any]		=      None
		else:
			snake_case__  :						Union[str, Any]		=      int(training_difference.replace("step_"				,    ""	)	)
			snake_case__  :						List[str]		=      resume_step // len(_UpperCAmelCase	)
			resume_step -= starting_epoch * len(_UpperCAmelCase	)

    # Now we train the model
	for epoch in range(_UpperCAmelCase				,    _UpperCAmelCase	):
		model.train()
		# New Code #
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			snake_case__  :						Dict		=      accelerator.skip_first_batches(_UpperCAmelCase				,    _UpperCAmelCase	)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			snake_case__  :						List[str]		=      train_dataloader
		for step, batch in enumerate(_UpperCAmelCase	):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device	)
			snake_case__  :						Optional[Any]		=      model(**_UpperCAmelCase	)
			snake_case__  :						int		=      outputs.loss
			snake_case__  :						Any		=      loss / gradient_accumulation_steps
			accelerator.backward(_UpperCAmelCase	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
			# New Code #
			overall_step += 1

			# New Code #
			# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
			# These are saved to folders named `step_{overall_step}`
			# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
			# If mixed precision was used, will also save a "scalar.bin" file
			if isinstance(_UpperCAmelCase				,    _UpperCAmelCase	):
				snake_case__  :						List[Any]		=      F"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						snake_case__  :						int		=      os.path.join(args.output_dir				,    _UpperCAmelCase	)
					accelerator.save_state(_UpperCAmelCase	)

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

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

		# New Code #
		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
		# These are saved to folders named `epoch_{epoch}`
		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
		# If mixed precision was used, will also save a "scalar.bin" file
		if checkpointing_steps == "epoch":
			snake_case__  :						Dict		=      F"""epoch_{epoch}"""
			if args.output_dir is not None:
				snake_case__  :						Union[str, Any]		=      os.path.join(args.output_dir				,    _UpperCAmelCase	)
			accelerator.save_state(_UpperCAmelCase	)



def 			A__		(					)							->	Tuple:

	'''simple docstring'''


	snake_case__  :						str		=      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."	)
	parser.add_argument(
	    "--checkpointing_steps"				,    type=_UpperCAmelCase				,    default=_UpperCAmelCase				,    help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."				,    )
	parser.add_argument(
	    "--output_dir"				,    type=_UpperCAmelCase				,    default="."				,    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,    )
	parser.add_argument(
	    "--resume_from_checkpoint"				,    type=_UpperCAmelCase				,    default=_UpperCAmelCase				,    help="If the training should continue from a checkpoint folder."				,    )
	snake_case__  :						str		=      parser.parse_args()
	snake_case__  :						Optional[int]		=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(_UpperCAmelCase				,    _UpperCAmelCase	)


if __name__ == "__main__":
					main()






import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
      pass

if is_speech_available():
      from transformers import EncodecFeatureExtractor, MusicgenProcessor


UpperCamelCase    				=		random.Random()

def      lowercase_							(     _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			str=1.0						,      _lowerCamelCase				:			Optional[int]=None						,      _lowerCamelCase				:			int=None):
    if rng is None:
        lowercase__					:  Dict          =							global_rng

    lowercase__					:  Optional[Any]          =							[]
    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_sentencepiece
class     snake_case_			(						unittest.TestCase			):






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		Union[str, Any]:
       lowercase__					:  List[Any]          =							"facebook/musicgen-small"
       lowercase__					:  List[Any]          =							tempfile.mkdtemp()






   def  __UpperCamelCase	( self							:	Optional[Any]      ,		**lowercase_							:	Optional[Any]    )   ->		Optional[Any]:
       return TaTokenizer.from_pretrained(self.checkpoint      ,		**lowercase_    )






   def  __UpperCamelCase	( self							:	int      ,		**lowercase_							:	Any    )   ->		List[Any]:
       return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,		**lowercase_    )






   def  __UpperCamelCase	( self							:	str    )   ->		List[Any]:
       shutil.rmtree(self.tmpdirname    )






   def  __UpperCamelCase	( self							:	Dict    )   ->		Union[str, Any]:
       lowercase__					:  List[str]          =							self.get_tokenizer()
       lowercase__					:  Optional[Any]          =							self.get_feature_extractor()

       lowercase__					:  Any          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       processor.save_pretrained(self.tmpdirname    )
       lowercase__					:  Union[str, Any]          =							MusicgenProcessor.from_pretrained(self.tmpdirname    )

       self.assertEqual(processor.tokenizer.get_vocab()      ,		tokenizer.get_vocab()    )
       self.assertIsInstance(processor.tokenizer      ,		lowercase_    )

       self.assertEqual(processor.feature_extractor.to_json_string()      ,		feature_extractor.to_json_string()    )
       self.assertIsInstance(processor.feature_extractor      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	List[str]    )   ->		Union[str, Any]:
       lowercase__					:  Tuple          =							MusicgenProcessor(tokenizer=self.get_tokenizer()      ,		feature_extractor=self.get_feature_extractor()    )
       processor.save_pretrained(self.tmpdirname    )

       lowercase__					:  int          =							self.get_tokenizer(bos_token="(BOS)"      ,		eos_token="(EOS)"    )
       lowercase__					:  int          =							self.get_feature_extractor(do_normalize=lowercase_      ,		padding_value=1.0    )

       lowercase__					:  Optional[int]          =							MusicgenProcessor.from_pretrained(
           self.tmpdirname      ,		bos_token="(BOS)"      ,		eos_token="(EOS)"      ,		do_normalize=lowercase_      ,		padding_value=1.0    )

       self.assertEqual(processor.tokenizer.get_vocab()      ,		tokenizer_add_kwargs.get_vocab()    )
       self.assertIsInstance(processor.tokenizer      ,		lowercase_    )

       self.assertEqual(processor.feature_extractor.to_json_string()      ,		feature_extractor_add_kwargs.to_json_string()    )
       self.assertIsInstance(processor.feature_extractor      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Optional[Any]:
       lowercase__					:  str          =							self.get_feature_extractor()
       lowercase__					:  Union[str, Any]          =							self.get_tokenizer()

       lowercase__					:  str          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Tuple          =							floats_list((3, 10_00)    )

       lowercase__					:  int          =							feature_extractor(lowercase_      ,		return_tensors="np"    )
       lowercase__					:  int          =							processor(lowercase_      ,		return_tensors="np"    )

       for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()      ,		input_processor[key].sum()      ,		delta=1E-2    )






   def  __UpperCamelCase	( self							:	Any    )   ->		int:
       lowercase__					:  List[Any]          =							self.get_feature_extractor()
       lowercase__					:  int          =							self.get_tokenizer()

       lowercase__					:  int          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Tuple          =							"This is a test string"

       lowercase__					:  Any          =							processor(text=lowercase_    )

       lowercase__					:  str          =							tokenizer(lowercase_    )

       for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]      ,		encoded_processor[key]    )






   def  __UpperCamelCase	( self							:	Tuple    )   ->		List[str]:
       lowercase__					:  Dict          =							self.get_feature_extractor()
       lowercase__					:  Optional[Any]          =							self.get_tokenizer()

       lowercase__					:  str          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Any          =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowercase__					:  List[Any]          =							processor.batch_decode(sequences=lowercase_    )
       lowercase__					:  Optional[Any]          =							tokenizer.batch_decode(lowercase_    )

       self.assertListEqual(lowercase_      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	Dict    )   ->		List[str]:
       lowercase__					:  List[Any]          =							self.get_feature_extractor()
       lowercase__					:  Union[str, Any]          =							self.get_tokenizer()

       lowercase__					:  str          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       self.assertListEqual(
           processor.model_input_names      ,		feature_extractor.model_input_names      ,		msg="`processor` and `feature_extractor` model input names do not match"      ,		)







   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Optional[int]:
       lowercase__					:  List[str]          =							self.get_feature_extractor(padding_side="left"    )
       lowercase__					:  Tuple          =							self.get_tokenizer()

       lowercase__					:  Any          =							MusicgenProcessor(tokenizer=lowercase_      ,		feature_extractor=lowercase_    )

       lowercase__					:  Optional[Any]          =							[floats_list((1, x)    )[0] for x in range(5      ,		20      ,		5    )]
       lowercase__					:  Optional[int]          =							processor(lowercase_    ).padding_mask

       lowercase__					:  Union[str, Any]          =							np.asarray(floats_list((3, 20)    )    )[:, None, :]
       lowercase__					:  Dict          =							processor.batch_decode(lowercase_      ,		padding_mask=lowercase_    )

       self.assertIsInstance(lowercase_      ,		lowercase_    )

       for audio in decoded_audios:
           self.assertIsInstance(lowercase_      ,		np.ndarray    )

       self.assertTrue(decoded_audios[0].shape == (1, 10)    )
       self.assertTrue(decoded_audios[1].shape == (1, 15)    )
       self.assertTrue(decoded_audios[2].shape == (1, 20)    )



