
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
  import PIL.Image

  from .features import FeatureType


a_											=						None
a_											=						"<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
a_											=						[
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]






@dataclass
class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						True
 UpperCAmelCase_		    =						None
 # Automatically constructed
 UpperCAmelCase_		    =						"PIL.Image.Image"
 UpperCAmelCase_		    =						pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}					)
 UpperCAmelCase_		    =						field(default="""Image"""    ,						init=snake_case__    ,						repr=snake_case__					)





 def __call__(    self):
     return self.pa_type





 def        snake_case__		(    self ,       lowercase_):
     if config.PIL_AVAILABLE:
         import PIL.Image
     else:
         raise ImportError("To support encoding images, please install 'Pillow'.")

     if isinstance(lowercase_ ,       lowercase_):
         snake_case_ :    str     			=						np.array(lowercase_)

     if isinstance(lowercase_ ,       lowercase_):
         return {"path": value, "bytes": None}
     elif isinstance(lowercase_ ,       lowercase_):
         return {"path": None, "bytes": value}
     elif isinstance(lowercase_ ,       np.ndarray):
         # convert the image array to PNG/TIFF bytes
         return encode_np_array(lowercase_)
     elif isinstance(lowercase_ ,       PIL.Image.Image):
         # convert the PIL image to bytes (default format is PNG/TIFF)
         return encode_pil_image(lowercase_)
     elif value.get("path") is not None and os.path.isfile(value["path"]):
         # we set "bytes": None to not duplicate the data if they're already available locally
         return {"bytes": None, "path": value.get("path")}
     elif value.get("bytes") is not None or value.get("path") is not None:
         # store the image bytes, and path is used to infer the image format using the file extension
         return {"bytes": value.get("bytes"), "path": value.get("path")}
     else:
         raise ValueError(
             F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.')





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     if not self.decode:
         raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead.")

     if config.PIL_AVAILABLE:
         import PIL.Image
     else:
         raise ImportError("To support decoding images, please install 'Pillow'.")

     if token_per_repo_id is None:
         snake_case_ :    Dict     			=						{}

     snake_case_			, snake_case_ :    Dict     			=						value["path"], value["bytes"]
     if bytes_ is None:
         if path is None:
             raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.')
         else:
             if is_local_path(lowercase_):
                 snake_case_ :    Optional[int]     			=						PIL.Image.open(lowercase_)
             else:
                 snake_case_ :    str     			=						path.split("::")[-1]
                 try:
                     snake_case_ :    int     			=						string_to_dict(lowercase_ ,       config.HUB_DATASETS_URL)["repo_id"]
                     snake_case_ :    Optional[Any]     			=						token_per_repo_id.get(lowercase_)
                 except ValueError:
                     snake_case_ :    int     			=						None
                 with xopen(lowercase_ ,       "rb" ,       use_auth_token=lowercase_) as f:
                     snake_case_ :    List[str]     			=						BytesIO(f.read())
                 snake_case_ :    Dict     			=						PIL.Image.open(bytes_)
     else:
         snake_case_ :    Union[str, Any]     			=						PIL.Image.open(BytesIO(bytes_))
     image.load()  # to avoid "Too many open files" errors
     return image





 def        snake_case__		(    self):
     from .features import Value

     return (
         self
         if self.decode
         else {
             "bytes": Value("binary"),
             "path": Value("string"),
         }
     )





 def        snake_case__		(    self ,       lowercase_):
     if pa.types.is_string(storage.type):
         snake_case_ :    int     			=						pa.array([None] * len(lowercase_) ,       type=pa.binary())
         snake_case_ :    int     			=						pa.StructArray.from_arrays([bytes_array, storage] ,       ["bytes", "path"] ,       mask=storage.is_null())
     elif pa.types.is_binary(storage.type):
         snake_case_ :    str     			=						pa.array([None] * len(lowercase_) ,       type=pa.string())
         snake_case_ :    Optional[int]     			=						pa.StructArray.from_arrays([storage, path_array] ,       ["bytes", "path"] ,       mask=storage.is_null())
     elif pa.types.is_struct(storage.type):
         if storage.type.get_field_index("bytes") >= 0:
             snake_case_ :    List[str]     			=						storage.field("bytes")
         else:
             snake_case_ :    int     			=						pa.array([None] * len(lowercase_) ,       type=pa.binary())
         if storage.type.get_field_index("path") >= 0:
             snake_case_ :    Optional[Any]     			=						storage.field("path")
         else:
             snake_case_ :    str     			=						pa.array([None] * len(lowercase_) ,       type=pa.string())
         snake_case_ :    Dict     			=						pa.StructArray.from_arrays([bytes_array, path_array] ,       ["bytes", "path"] ,       mask=storage.is_null())
     elif pa.types.is_list(storage.type):
         snake_case_ :    List[str]     			=						pa.array(
             [encode_np_array(np.array(lowercase_))["bytes"] if arr is not None else None for arr in storage.to_pylist()] ,       type=pa.binary() ,       )
         snake_case_ :    int     			=						pa.array([None] * len(lowercase_) ,       type=pa.string())
         snake_case_ :    Tuple     			=						pa.StructArray.from_arrays(
             [bytes_array, path_array] ,       ["bytes", "path"] ,       mask=bytes_array.is_null())
     return array_cast(lowercase_ ,       self.pa_type)





 def        snake_case__		(    self ,       lowercase_):

     @no_op_if_value_is_null
     def path_to_bytes(lowercase_):
         with xopen(lowercase_ ,       "rb") as f:
             snake_case_ :    List[Any]     			=						f.read()
         return bytes_

     snake_case_ :    Optional[Any]     			=						pa.array(
         [
             (path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
             for x in storage.to_pylist()
         ] ,       type=pa.binary() ,       )
     snake_case_ :    Dict     			=						pa.array(
         [os.path.basename(lowercase_) if path is not None else None for path in storage.field("path").to_pylist()] ,       type=pa.string() ,       )
     snake_case_ :    Tuple     			=						pa.StructArray.from_arrays([bytes_array, path_array] ,       ["bytes", "path"] ,       mask=bytes_array.is_null())
     return array_cast(lowercase_ ,       self.pa_type)


def 						UpperCamelCase_     (	):
    """simple docstring"""






    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("To support encoding images, please install 'Pillow'."       )

    global _IMAGE_COMPRESSION_FORMATS
    if _IMAGE_COMPRESSION_FORMATS is None:
        PIL.Image.init()
        snake_case_ :    Dict     			=						list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
    return _IMAGE_COMPRESSION_FORMATS

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    int     			=						BytesIO()
    if image.format in list_image_compression_formats():
        snake_case_ :    List[Any]     			=						image.format
    else:
        snake_case_ :    Tuple     			=						"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
    image.save(__SCREAMING_SNAKE_CASE,		format=__SCREAMING_SNAKE_CASE       )
    return buffer.getvalue()

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if hasattr(__SCREAMING_SNAKE_CASE,		"filename"       ) and image.filename != "":
        return {"path": image.filename, "bytes": None}
    else:
        return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE       )}

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("To support encoding images, please install 'Pillow'."       )

    snake_case_ :    str     			=						array.dtype
    snake_case_ :    int     			=						dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
    snake_case_ :    int     			=						dtype.kind
    snake_case_ :    Optional[Any]     			=						dtype.itemsize

    snake_case_ :    Optional[int]     			=						None

    # Multi-channel array case (only np.dtype("|u1") is allowed)
    if array.shape[2:]:
        snake_case_ :    List[Any]     			=						np.dtype("|u1"       )
        if dtype_kind not in ["u", "i"]:
            raise TypeError(
                f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'       )
        if dtype is not dest_dtype:
            warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''       )
    # Exact match
    elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
        snake_case_ :    List[str]     			=						dtype
    else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
        while dtype_itemsize >= 1:
            snake_case_ :    int     			=						dtype_byteorder + dtype_kind + str(__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Optional[int]     			=						np.dtype(__SCREAMING_SNAKE_CASE       )
            if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''       )
                break
            else:
                dtype_itemsize //= 2

    if dest_dtype is None:
        raise TypeError(
            f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'       )
    snake_case_ :    Dict     			=						PIL.Image.fromarray(array.astype(__SCREAMING_SNAKE_CASE       )       )
    return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE       )}


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("To support encoding images, please install 'Pillow'."       )

    if objs:
        snake_case_			, snake_case_ :    Union[str, Any]     			=						first_non_null_value(__SCREAMING_SNAKE_CASE       )
        if isinstance(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
            return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
        if isinstance(__SCREAMING_SNAKE_CASE,		np.ndarray       ):
            snake_case_ :    List[str]     			=						no_op_if_value_is_null(__SCREAMING_SNAKE_CASE       )
            return [obj_to_image_dict_func(__SCREAMING_SNAKE_CASE       ) for obj in objs]
        elif isinstance(__SCREAMING_SNAKE_CASE,		PIL.Image.Image       ):
            snake_case_ :    str     			=						no_op_if_value_is_null(__SCREAMING_SNAKE_CASE       )
            return [obj_to_image_dict_func(__SCREAMING_SNAKE_CASE       ) for obj in objs]
        else:
            return objs
    else:
        return objs






'''simple docstring'''
import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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_											=						16
a_											=						32

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = 1_6       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						AutoTokenizer.from_pretrained("bert-base-cased"       )
    snake_case_ :    int     			=						DatasetDict(
        {
            "train": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "validation": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "test": dataset["validation"],
        }       )

    def tokenize_function(__SCREAMING_SNAKE_CASE       ):
        # max_length=None => use the model max length (it's actually the default)
        snake_case_ :    str     			=						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_ :    List[Any]     			=						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_ :    Dict     			=						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_ :    Dict     			=						1_2_8 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_ :    Optional[int]     			=						1_6
        elif accelerator.mixed_precision != "no":
            snake_case_ :    Tuple     			=						8
        else:
            snake_case_ :    Union[str, Any]     			=						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_ :    Optional[int]     			=						DataLoader(
        tokenized_datasets["train"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						DataLoader(
        tokenized_datasets["validation"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Tuple     			=						DataLoader(
        tokenized_datasets["test"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    return train_dataloader, eval_dataloader, test_dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						[]
    # Download the dataset
    snake_case_ :    Tuple     			=						load_dataset("glue",		"mrpc"       )
    # Create our splits
    snake_case_ :    Union[str, Any]     			=						StratifiedKFold(n_splits=int(args.num_folds       )       )
    # Initialize accelerator
    snake_case_ :    Optional[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[Any]     			=						config["lr"]
    snake_case_ :    str     			=						int(config["num_epochs"]       )
    snake_case_ :    Tuple     			=						int(config["seed"]       )
    snake_case_ :    Optional[Any]     			=						int(config["batch_size"]       )

    snake_case_ :    List[Any]     			=						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_ :    Union[str, Any]     			=						batch_size // MAX_GPU_BATCH_SIZE
        snake_case_ :    Union[str, Any]     			=						MAX_GPU_BATCH_SIZE

    set_seed(__SCREAMING_SNAKE_CASE       )

    # New Code #
    # Create our folds:
    snake_case_ :    int     			=						kfold.split(np.zeros(datasets["train"].num_rows       ),		datasets["train"]["label"]       )
    snake_case_ :    Optional[int]     			=						[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE       ):
        snake_case_			, snake_case_			, snake_case_ :    Tuple     			=						get_fold_dataloaders(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		)
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        snake_case_ :    Optional[int]     			=						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_ :    Union[str, Any]     			=						model.to(accelerator.device       )

        # Instantiate optimizer
        snake_case_ :    Any     			=						AdamW(params=model.parameters(),		lr=__SCREAMING_SNAKE_CASE       )

        # Instantiate scheduler
        snake_case_ :    List[str]     			=						get_linear_schedule_with_warmup(
            optimizer=__SCREAMING_SNAKE_CASE,		num_warmup_steps=1_0_0,		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_ :    str     			=						accelerator.prepare(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

        # Now we train the model
        for epoch in range(__SCREAMING_SNAKE_CASE       ):
            model.train()
            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_ :    List[Any]     			=						model(**__SCREAMING_SNAKE_CASE       )
                snake_case_ :    Union[str, Any]     			=						outputs.loss
                snake_case_ :    Union[str, Any]     			=						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`.
                batch.to(accelerator.device       )
                with torch.no_grad():
                    snake_case_ :    Dict     			=						model(**__SCREAMING_SNAKE_CASE       )
                snake_case_ :    str     			=						outputs.logits.argmax(dim=-1       )
                snake_case_			, snake_case_ :    Dict     			=						accelerator.gather_for_metrics((predictions, batch["labels"])       )
                metric.add_batch(
                    predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE,		)

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

        # New Code #
        # We also run predictions on the test set at the very end
        snake_case_ :    Any     			=						[]
        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       )
            with torch.no_grad():
                snake_case_ :    int     			=						model(**__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Tuple     			=						outputs.logits
            snake_case_			, snake_case_ :    List[Any]     			=						accelerator.gather_for_metrics((predictions, batch["labels"])       )
            fold_predictions.append(predictions.cpu()       )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )       )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    snake_case_ :    List[Any]     			=						torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )
    snake_case_ :    Any     			=						torch.stack(__SCREAMING_SNAKE_CASE,		dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    snake_case_ :    Tuple     			=						metric.compute(predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE       )
    accelerator.print("Average test metrics from all folds:",		__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Tuple     			=						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."       )
    # New Code #
    parser.add_argument("--num_folds",		type=__SCREAMING_SNAKE_CASE,		default=3,		help="The number of splits to perform across the dataset"       )
    snake_case_ :    List[Any]     			=						parser.parse_args()
    snake_case_ :    Optional[Any]     			=						{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  main()




