





"""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 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
#
########################################################################

__SCREAMING_SNAKE_CASE							:    Any		=    16
__SCREAMING_SNAKE_CASE							:    List[str]		=    32







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 16				)    ->				Tuple:
							snake_case_           =		AutoTokenizer.from_pretrained("""bert-base-cased"""				)
							snake_case_           =		load_dataset("""glue"""				, """mrpc"""				)

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

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

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

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

							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

				__SCREAMING_SNAKE_CASE							:    Tuple		=    mocked_dataloaders  # noqa: F811







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				, _SCREAMING_SNAKE_CASE				) == "1":
														snake_case_           =		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:
														snake_case_           =		Accelerator(
														    cpu=args.cpu				, mixed_precision=args.mixed_precision				, log_with="""all"""				, project_dir=args.project_dir				)
							else:
														snake_case_           =		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_           =		config["""lr"""]
							snake_case_           =		int(config["""num_epochs"""]				)
							snake_case_           =		int(config["""seed"""]				)
							snake_case_           =		int(config["""batch_size"""]				)
							set_seed(_SCREAMING_SNAKE_CASE				)

							snake_case_       ,						snake_case_           =		get_dataloaders(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							snake_case_           =		evaluate.load("""glue"""				, """mrpc"""				)

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

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

							# 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_           =		model.to(accelerator.device				)

							# Instantiate optimizer
							snake_case_           =		AdamW(params=model.parameters()				, lr=_SCREAMING_SNAKE_CASE				)

							# Instantiate scheduler
							snake_case_           =		get_linear_schedule_with_warmup(
							    optimizer=_SCREAMING_SNAKE_CASE				, num_warmup_steps=100				, num_training_steps=(len(_SCREAMING_SNAKE_CASE				) * 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_           =		accelerator.prepare(
							    _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

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

							# Now we train the model
							for epoch in range(_SCREAMING_SNAKE_CASE				):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					snake_case_           =		0
														for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device				)
																					snake_case_           =		model(**_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					snake_case_           =		loss / gradient_accumulation_steps
																					accelerator.backward(_SCREAMING_SNAKE_CASE				)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
																					# 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_           =		model(**_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		outputs.logits.argmax(dim=-1				)
																					snake_case_       ,						snake_case_           =		accelerator.gather_for_metrics((predictions, batch["""labels"""])				)
																					metric.add_batch(
																					    predictions=_SCREAMING_SNAKE_CASE				, references=_SCREAMING_SNAKE_CASE				, )

														snake_case_           =		metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""				, _SCREAMING_SNAKE_CASE				)

														# 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(_SCREAMING_SNAKE_CASE				),
																					        """epoch""": epoch,
																					    }				, step=_SCREAMING_SNAKE_CASE				, )

    # 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 						_a				(      )    ->				Any:
							snake_case_           =		argparse.ArgumentParser(description="""Simple example of training script."""				)
							parser.add_argument(
							    """--mixed_precision"""				, type=_SCREAMING_SNAKE_CASE				, default=_SCREAMING_SNAKE_CASE				, 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=_SCREAMING_SNAKE_CASE				, default="""logs"""				, help="""Location on where to store experiment tracking logs` and relevent project information"""				, )
							snake_case_           =		parser.parse_args()
							snake_case_           =		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				main()







"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

__SCREAMING_SNAKE_CASE							:    Dict		=    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

__SCREAMING_SNAKE_CASE							:    Dict		=    'zero2'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    'zero3'
__SCREAMING_SNAKE_CASE							:    int		=    [ZEROa, ZEROa]







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							# customize the test name generator function as we want both params to appear in the sub-test
							# name, as by default it shows only the first param
							snake_case_           =		parameterized.to_safe_name("""_""".join(str(_SCREAMING_SNAKE_CASE				) for x in param.args				)				)
							return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__SCREAMING_SNAKE_CASE							:    Dict		=    list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class      __A     (snake_case__):





							'''simple docstring'''

							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Union[str, Any]       )					->Any:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		models[model]

														snake_case_           =		self.run_trainer(
														    stage=UpperCAmelCase_		,       model_name=UpperCAmelCase_		,       eval_steps=UpperCAmelCase_		,       num_train_epochs=1		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )

														self.do_checks(UpperCAmelCase_       )

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.get_auto_remove_tmp_dir("""./xxx"""		,       after=UpperCAmelCase_       )
														snake_case_           =		F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(UpperCAmelCase_       )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

														if fpaa:
																					args.extend(["""--fp16"""]       )

														# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
														# hence the separate config files
														snake_case_           =		F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
														snake_case_           =		[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
														snake_case_           =		self.get_launcher(UpperCAmelCase_       )

														snake_case_           =		launcher + script + args + ds_args
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
														execute_subprocess_async(UpperCAmelCase_		,       env=self.get_env()       )

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Any=False       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		min(2		,       get_gpu_count()       ) if distributed else 1
														return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()



