





'''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
# and perform gradient accumulation
#
# 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 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 lowerCamelCase						(     __lowerCamelCase :    Accelerator					,       __lowerCamelCase :    int = 16    )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			AutoTokenizer.from_pretrained("""bert-base-cased"""    )
    _SCREAMING_SNAKE_CASE									=			load_dataset("""glue"""					,       """mrpc"""    )

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

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

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

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

    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 lowerCamelCase						(     __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Optional[int]    )						->str:
    # For testing only
    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,       __lowerCamelCase    ) == "1":
        _SCREAMING_SNAKE_CASE									=			2
    # New Code #
    _SCREAMING_SNAKE_CASE									=			int(args.gradient_accumulation_steps    )
    # Initialize accelerator
    _SCREAMING_SNAKE_CASE									=			Accelerator(
        cpu=args.cpu					,       mixed_precision=args.mixed_precision					,       gradient_accumulation_steps=__lowerCamelCase    )
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""    )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _SCREAMING_SNAKE_CASE									=			config["""lr"""]
    _SCREAMING_SNAKE_CASE									=			int(config["""num_epochs"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""seed"""]    )
    _SCREAMING_SNAKE_CASE									=			int(config["""batch_size"""]    )

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

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

    # 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									=			model.to(accelerator.device    )

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

    # Instantiate scheduler
    _SCREAMING_SNAKE_CASE									=			get_linear_schedule_with_warmup(
        optimizer=__lowerCamelCase					,       num_warmup_steps=100					,       num_training_steps=(len(__lowerCamelCase    ) * num_epochs)					,       )

    # 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									=			accelerator.prepare(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

    # Now we train the model
    for epoch in range(__lowerCamelCase    ):
        model.train()
        for step, batch in enumerate(__lowerCamelCase    ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device    )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(__lowerCamelCase    ):
                _SCREAMING_SNAKE_CASE									=			model(**__lowerCamelCase    )
                _SCREAMING_SNAKE_CASE									=			output.loss
                accelerator.backward(__lowerCamelCase    )
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

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

        _SCREAMING_SNAKE_CASE									=			metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'					,       __lowerCamelCase    )





def lowerCamelCase						(     )						->Any:
    _SCREAMING_SNAKE_CASE									=			argparse.ArgumentParser(description="""Simple example of training script."""    )
    parser.add_argument(
        """--mixed_precision"""					,       type=__lowerCamelCase					,       default=__lowerCamelCase					,       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."""					,       )
    # New Code #
    parser.add_argument(
        """--gradient_accumulation_steps"""					,       type=__lowerCamelCase					,       default=1					,       help="""The number of minibatches to be ran before gradients are accumulated."""					,       )
    parser.add_argument("""--cpu"""					,       action="""store_true"""					,       help="""If passed, will train on the CPU."""    )
    _SCREAMING_SNAKE_CASE									=			parser.parse_args()
    _SCREAMING_SNAKE_CASE									=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(__lowerCamelCase					,       __lowerCamelCase    )


if __name__ == "__main__":
      main()










'''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


lowercase_					= None
lowercase_					= """<""" 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
lowercase_					= [
    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 					a_     :

    '''simple docstring'''
    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       )		->   Tuple:
        return self.pa_type


    def 			snake_case_(		self ,		A       )		->   dict:
        if config.PIL_AVAILABLE:
            import PIL.Image
        else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""       )

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			np.array(A       )

        if isinstance(A ,		A       ):
            return {"path": value, "bytes": None}
        elif isinstance(A ,		A       ):
            return {"path": None, "bytes": value}
        elif isinstance(A ,		np.ndarray       ):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(A       )
        elif isinstance(A ,		PIL.Image.Image       ):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(A       )
        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 ,		A ,		A=None       )		->   "PIL.Image.Image":
        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:
            _SCREAMING_SNAKE_CASE									=			{}

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			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(A       ):
                    _SCREAMING_SNAKE_CASE									=			PIL.Image.open(A       )
                else:
                    _SCREAMING_SNAKE_CASE									=			path.split("""::"""       )[-1]
                    try:
                        _SCREAMING_SNAKE_CASE									=			string_to_dict(A ,		config.HUB_DATASETS_URL       )["""repo_id"""]
                        _SCREAMING_SNAKE_CASE									=			token_per_repo_id.get(A       )
                    except ValueError:
                        _SCREAMING_SNAKE_CASE									=			None
                    with xopen(A ,		"""rb""" ,		use_auth_token=A       ) as f:
                        _SCREAMING_SNAKE_CASE									=			BytesIO(f.read()       )
                    _SCREAMING_SNAKE_CASE									=			PIL.Image.open(bytes_       )
        else:
            _SCREAMING_SNAKE_CASE									=			PIL.Image.open(BytesIO(bytes_       )       )
        image.load()  # to avoid "Too many open files" errors
        return image


    def 			snake_case_(		self       )		->   Union["FeatureType", Dict[str, "FeatureType"]]:
        from .features import Value

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


    def 			snake_case_(		self ,		A       )		->   pa.StructArray:
        if pa.types.is_string(storage.type       ):
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.binary()       )
            _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays([bytes_array, storage] ,		["""bytes""", """path"""] ,		mask=storage.is_null()       )
        elif pa.types.is_binary(storage.type       ):
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.string()       )
            _SCREAMING_SNAKE_CASE									=			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:
                _SCREAMING_SNAKE_CASE									=			storage.field("""bytes"""       )
            else:
                _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.binary()       )
            if storage.type.get_field_index("""path"""       ) >= 0:
                _SCREAMING_SNAKE_CASE									=			storage.field("""path"""       )
            else:
                _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.string()       )
            _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays([bytes_array, path_array] ,		["""bytes""", """path"""] ,		mask=storage.is_null()       )
        elif pa.types.is_list(storage.type       ):
            _SCREAMING_SNAKE_CASE									=			pa.array(
                [encode_np_array(np.array(A       )       )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] ,		type=pa.binary() ,		)
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.string()       )
            _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays(
                [bytes_array, path_array] ,		["""bytes""", """path"""] ,		mask=bytes_array.is_null()       )
        return array_cast(A ,		self.pa_type       )





    def 			snake_case_(		self ,		A       )		->   pa.StructArray:

        @no_op_if_value_is_null
        def path_to_bytes(A       ):
            with xopen(A ,		"""rb"""       ) as f:
                _SCREAMING_SNAKE_CASE									=			f.read()
            return bytes_

        _SCREAMING_SNAKE_CASE									=			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() ,		)
        _SCREAMING_SNAKE_CASE									=			pa.array(
            [os.path.basename(A       ) if path is not None else None for path in storage.field("""path"""       ).to_pylist()] ,		type=pa.string() ,		)
        _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays([bytes_array, path_array] ,		["""bytes""", """path"""] ,		mask=bytes_array.is_null()       )
        return array_cast(A ,		self.pa_type       )





def lowerCamelCase						(     )						->List[str]:
    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()
        _SCREAMING_SNAKE_CASE									=			list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
    return _IMAGE_COMPRESSION_FORMATS





def lowerCamelCase						(     __lowerCamelCase :    "PIL.Image.Image"    )						->bytes:
    _SCREAMING_SNAKE_CASE									=			BytesIO()
    if image.format in list_image_compression_formats():
        _SCREAMING_SNAKE_CASE									=			image.format
    else:
        _SCREAMING_SNAKE_CASE									=			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
    image.save(__lowerCamelCase					,       format=__lowerCamelCase    )
    return buffer.getvalue()





def lowerCamelCase						(     __lowerCamelCase :    "PIL.Image.Image"    )						->dict:
    if hasattr(__lowerCamelCase					,       """filename"""    ) and image.filename != "":
        return {"path": image.filename, "bytes": None}
    else:
        return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}





def lowerCamelCase						(     __lowerCamelCase :    np.ndarray    )						->dict:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("""To support encoding images, please install 'Pillow'."""    )

    _SCREAMING_SNAKE_CASE									=			array.dtype
    _SCREAMING_SNAKE_CASE									=			dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
    _SCREAMING_SNAKE_CASE									=			dtype.kind
    _SCREAMING_SNAKE_CASE									=			dtype.itemsize

    _SCREAMING_SNAKE_CASE									=			None

    # Multi-channel array case (only np.dtype("|u1") is allowed)
    if array.shape[2:]:
        _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			dtype_byteorder + dtype_kind + str(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			np.dtype(__lowerCamelCase    )
            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}'    )
    _SCREAMING_SNAKE_CASE									=			PIL.Image.fromarray(array.astype(__lowerCamelCase    )    )
    return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}






def lowerCamelCase						(     __lowerCamelCase :    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    )						->List[dict]:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("""To support encoding images, please install 'Pillow'."""    )

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



