






"""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_   : Tuple   			= 16
UpperCAmelCase_   : List[str]   			= 32



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






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

    def tokenize_function(__a							):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_      :  Any    						=		tokenizer(examples['''sentence1''']    ,				examples['''sentence2''']    ,				truncation=__a    ,				max_length=__a							)
        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_      :  Union[str, Any]    						=		datasets.map(
            __a    ,				batched=__a    ,				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_      :  str    						=		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_      :  Optional[int]    						=		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_      :  Any    						=		16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_      :  int    						=		8
        else:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		None

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

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		DataLoader(
        tokenized_datasets['''train''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		DataLoader(
        tokenized_datasets['''validation''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)

    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							) ->       Optional[int]:
    """simple docstring"""






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,				__a							) == "1":
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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_      :  Optional[Any]    						=		Accelerator(
            cpu=args.cpu    ,				mixed_precision=args.mixed_precision    ,				log_with='''all'''    ,				project_dir=args.project_dir							)
    else:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		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_      :  Any    						=		config['''lr''']
    SCREAMING_SNAKE_CASE_      :  int    						=		int(config['''num_epochs''']							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		int(config['''seed''']							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		int(config['''batch_size''']							)
    set_seed(__a							)

    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		get_dataloaders(__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		evaluate.load('''glue'''    ,				'''mrpc'''							)

    # If the batch size is too big we use gradient accumulation
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		batch_size // MAX_GPU_BATCH_SIZE
        SCREAMING_SNAKE_CASE_      :  Optional[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_      :  Optional[Any]    						=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,				return_dict=__a							)

    # 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_      :  List[str]    						=		model.to(accelerator.device							)

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

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_      :  int    						=		get_linear_schedule_with_warmup(
        optimizer=__a    ,				num_warmup_steps=1_00    ,				num_training_steps=(len(__a							) * 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_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		accelerator.prepare(
        __a    ,				__a    ,				__a    ,				__a    ,				__a							)

    # 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(__a							)[-1].split('''.'''							)[0]
        accelerator.init_trackers(__a    ,				__a							)

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

        model.eval()
        for step, batch in enumerate(__a							):
            # 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_      :  str    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  str    						=		outputs.logits.argmax(dim=-1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
            metric.add_batch(
                predictions=__a    ,				references=__a    ,				)

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

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

    # 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			() ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		argparse.ArgumentParser(description='''Simple example of training script.'''							)
    parser.add_argument(
        '''--mixed_precision'''    ,				type=__a    ,				default=__a    ,				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=__a    ,				default='''logs'''    ,				help='''Location on where to store experiment tracking logs` and relevent project information'''    ,				)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		parser.parse_args()
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__a    ,				__a							)


if __name__ == "__main__":
  main()










"""simple docstring"""






from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)



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






    try:
        with open(__a    ,				'''rb'''							) as flax_state_f:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		from_bytes(__a    ,				flax_state_f.read()							)
    except UnpicklingError as e:
        try:
            with open(__a							) as f:
                if f.read().startswith('''version'''							):
                    raise OSError(
                        '''You seem to have cloned a repository without having git-lfs installed. Please'''
                        ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
                        ''' folder you cloned.'''							)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '							)

    return load_flax_weights_in_pytorch_model(__a    ,				__a							)





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






    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''							)
        raise

    # check if we have bf16 weights
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flatten_dict(jax.tree_util.tree_map(lambda __a							: x.dtype == jnp.bfloataa    ,				__a							)							).values()
    if any(__a							):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
            '''before loading those in PyTorch model.'''							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jax.tree_util.tree_map(
            lambda __a							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params    ,				__a							)

    SCREAMING_SNAKE_CASE_      :  int    						=		''''''

    SCREAMING_SNAKE_CASE_      :  str    						=		flatten_dict(__a    ,				sep='''.'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pt_model.state_dict()

    # keep track of unexpected & missing keys
    SCREAMING_SNAKE_CASE_      :  str    						=		[]
    SCREAMING_SNAKE_CASE_      :  Any    						=		set(pt_model_dict.keys()							)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        SCREAMING_SNAKE_CASE_      :  Any    						=		flax_key_tuple.split('''.'''							)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            SCREAMING_SNAKE_CASE_      :  Any    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.transpose(__a    ,				(3, 2, 0, 1)							)
        elif flax_key_tuple_array[-1] == "kernel":
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flax_key_tuple_array[:-1] + ['''weight''']

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(__a							):
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		(
                    flax_key_tuple_string.replace('''_0'''    ,				'''.0'''							)
                    .replace('''_1'''    ,				'''.1'''							)
                    .replace('''_2'''    ,				'''.2'''							)
                    .replace('''_3'''    ,				'''.3'''							)
                    .replace('''_4'''    ,				'''.4'''							)
                    .replace('''_5'''    ,				'''.5'''							)
                    .replace('''_6'''    ,				'''.6'''							)
                    .replace('''_7'''    ,				'''.7'''							)
                    .replace('''_8'''    ,				'''.8'''							)
                    .replace('''_9'''    ,				'''.9'''							)
                )

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''.'''.join(__a							)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)
            else:
                # add weight to pytorch dict
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.asarray(__a							) if not isinstance(__a    ,				np.ndarray							) else flax_tensor
                SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.from_numpy(__a							)
                # remove from missing keys
                missing_keys.remove(__a							)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(__a							)

    pt_model.load_state_dict(__a							)

    # re-transform missing_keys to list
    SCREAMING_SNAKE_CASE_      :  int    						=		list(__a							)

    if len(__a							) > 0:
        logger.warning(
            '''Some weights of the Flax model were not used when initializing the PyTorch model'''
            f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
            f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
            ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
            f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
            ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
            ''' FlaxBertForSequenceClassification model).'''							)
    if len(__a							) > 0:
        logger.warning(
            f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
            f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
            ''' use it for predictions and inference.'''							)

    return pt_model


