






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

UpperCAmelCase_   : Dict   			= 16
UpperCAmelCase_   : Optional[int]   			= 32



def 					_A			(__a    ,				__a = 16							) ->       Any:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    SCREAMING_SNAKE_CASE_      :  int    						=		load_dataset('''glue'''    ,				'''mrpc'''							)

    def tokenize_function(__a							):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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():
        SCREAMING_SNAKE_CASE_      :  Optional[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
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tokenized_datasets.rename_column('''label'''    ,				'''labels'''							)

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

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

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_      :  str    						=		DataLoader(
        tokenized_datasets['''train''']    ,				shuffle=UpperCamelCase__    ,				collate_fn=UpperCamelCase__    ,				batch_size=UpperCamelCase__							)
    SCREAMING_SNAKE_CASE_      :  str    						=		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

  UpperCAmelCase_   : int   			= mocked_dataloaders  # noqa: F811



def 					_A			(__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,				UpperCamelCase__							) == "1":
        SCREAMING_SNAKE_CASE_      :  Dict    						=		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:
        SCREAMING_SNAKE_CASE_      :  str    						=		Accelerator(
            cpu=args.cpu    ,				mixed_precision=args.mixed_precision    ,				log_with='''all'''    ,				project_dir=args.project_dir							)
    else:
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision							)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		config['''lr''']
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		int(config['''num_epochs''']							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		int(config['''seed''']							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		int(config['''batch_size''']							)
    set_seed(UpperCamelCase__							)

    SCREAMING_SNAKE_CASE_      :  int    						=		get_dataloaders(UpperCamelCase__    ,				UpperCamelCase__							)
    SCREAMING_SNAKE_CASE_      :  int    						=		evaluate.load('''glue'''    ,				'''mrpc'''							)

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

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		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).
    SCREAMING_SNAKE_CASE_      :  str    						=		model.to(accelerator.device							)

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		AdamW(params=model.parameters()    ,				lr=UpperCamelCase__							)

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_      :  Dict    						=		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.
    SCREAMING_SNAKE_CASE_      :  Dict    						=		accelerator.prepare(
        UpperCamelCase__    ,				UpperCamelCase__    ,				UpperCamelCase__    ,				UpperCamelCase__    ,				UpperCamelCase__							)

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

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

        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():
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(**UpperCamelCase__							)
            SCREAMING_SNAKE_CASE_      :  int    						=		outputs.logits.argmax(dim=-1							)
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
            metric.add_batch(
                predictions=UpperCamelCase__    ,				references=UpperCamelCase__    ,				)

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

        # 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(UpperCamelCase__							),
                    '''epoch''': epoch,
                }    ,				step=UpperCamelCase__    ,				)

    # 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			() ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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(
        '''--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=UpperCamelCase__    ,				default='''logs'''    ,				help='''Location on where to store experiment tracking logs` and relevent project information'''    ,				)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		parser.parse_args()
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCamelCase__    ,				UpperCamelCase__							)


if __name__ == "__main__":
  main()







"""simple docstring"""
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     @require_torch
     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  str											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''hf-internal-testing/tiny-clap-htsat-unfused'''		)
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  str											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}]       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         pass





     @slow
     @require_torch
     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Union[str, Any]											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''laion/clap-htsat-unfused'''       ,	)
         # This is an audio of a dog
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  Any											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)

         self.assertEqual(
             nested_simplify(A		)       ,	[
                 {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                 {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
             ]       ,	)

         _UpperCAmelCase							:  List[Any]											=    audio_classifier([audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)
         _UpperCAmelCase							:  Tuple											=    audio_classifier(
             [audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']       ,	batch_size=5		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					int:
         pass


