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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


_lowerCamelCase   :				Dict     			=  16
_lowerCamelCase   :				List[str]     			=  32





def 		__lowerCamelCase							(UpperCAmelCase__       :							Accelerator ,	UpperCAmelCase__       :							int = 1_6					):
					SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("bert-base-cased"					)
					SCREAMING_SNAKE_CASE           =    load_dataset("glue" ,	"mrpc"					)

					def tokenize_function(UpperCAmelCase__       :							int					):
										# max_length=None => use the model max length (it's actually the default)
										SCREAMING_SNAKE_CASE           =    tokenizer(examples["sentence1"] ,	examples["sentence2"] ,	truncation=lowercase__ ,	max_length=lowercase__					)
										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(
										    lowercase__ ,	batched=lowercase__ ,	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(UpperCAmelCase__       :							Dict					):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										SCREAMING_SNAKE_CASE           =    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":
															SCREAMING_SNAKE_CASE           =    1_6
										elif accelerator.mixed_precision != "no":
															SCREAMING_SNAKE_CASE           =    8
										else:
															SCREAMING_SNAKE_CASE           =    None

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

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

					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

							_lowerCamelCase   :				List[Any]     			=  mocked_dataloaders  # noqa: F811





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Optional[int]					):





					if os.environ.get("TESTING_MOCKED_DATALOADERS" ,	lowercase__					) == "1":
										SCREAMING_SNAKE_CASE           =    2
					# New Code #
					SCREAMING_SNAKE_CASE           =    int(args.gradient_accumulation_steps					)
					SCREAMING_SNAKE_CASE           =    int(args.local_sgd_steps					)
					# Initialize accelerator
					SCREAMING_SNAKE_CASE           =    Accelerator(
					    cpu=args.cpu ,	mixed_precision=args.mixed_precision ,	gradient_accumulation_steps=lowercase__					)
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"					)
					# 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(lowercase__					)
					SCREAMING_SNAKE_CASE           =    get_dataloaders(lowercase__ ,	lowercase__					)
					# 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=lowercase__					)

					# 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=lowercase__					)

					# Instantiate scheduler
					SCREAMING_SNAKE_CASE           =    get_linear_schedule_with_warmup(
					    optimizer=lowercase__ ,	num_warmup_steps=1_0_0 ,	num_training_steps=(len(lowercase__					) * 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           =    accelerator.prepare(
					    lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__					)

					# Now we train the model
					for epoch in range(lowercase__					):
										model.train()
										with LocalSGD(
										    accelerator=lowercase__ ,	model=lowercase__ ,	local_sgd_steps=lowercase__ ,	enabled=local_sgd_steps is not None					) as local_sgd:
															for step, batch in enumerate(lowercase__					):
																				# 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(lowercase__					):
																									SCREAMING_SNAKE_CASE           =    model(**lowercase__					)
																									SCREAMING_SNAKE_CASE           =    output.loss
																									accelerator.backward(lowercase__					)
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

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

										SCREAMING_SNAKE_CASE           =    metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:" ,	lowercase__					)





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    argparse.ArgumentParser(description="Simple example of training script."					)
					parser.add_argument(
					    "--mixed_precision" ,	type=lowercase__ ,	default=lowercase__ ,	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=lowercase__ ,	default=1 ,	help="The number of minibatches to be ran before gradients are accumulated." ,	)
					parser.add_argument(
					    "--local_sgd_steps" ,	type=lowercase__ ,	default=8 ,	help="Number of local SGD steps or None to disable local SGD"					)
					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""": 4_2, """batch_size""": 1_6}
					training_function(lowercase__ ,	lowercase__					)


if __name__ == "__main__":
							main()





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_lowerCamelCase   :				List[Any]     			=						logging.get_logger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							["""pixel_values"""]




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[int, float] = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							**_UpperCamelCase				:   Dict		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"shortest_edge": 384}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												# Default value set here for backwards compatibility where the value in config is None
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else 224 / 256
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Dict[str, int]		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BICUBIC		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )
												if "shortest_edge" not in size:
																	raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
												SCREAMING_SNAKE_CASE           =    size["shortest_edge"]

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	SCREAMING_SNAKE_CASE           =    int(shortest_edge / crop_pct )
																	SCREAMING_SNAKE_CASE           =    get_resize_output_image_size(_UpperCamelCase		,							size=_UpperCamelCase		,							default_to_square=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[int, float]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			List[Any]:



												'''simple docstring'''

												return rescale(_UpperCamelCase		,							scale=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[float, List[float]]		,							_UpperCamelCase				:   Union[float, List[float]]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   List[Any]		,							)   ->			np.ndarray:



												'''simple docstring'''

												return normalize(_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )





							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[str, TensorType]] = None		,							_UpperCamelCase				:   ChannelDimension = ChannelDimension.FIRST		,							**_UpperCamelCase				:   Any		,							)   ->			PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else self.crop_pct
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    make_list_of_images(_UpperCamelCase )

												if not valid_images(_UpperCamelCase ):
																	raise ValueError(
																	    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																	    "torch.Tensor, tf.Tensor or jax.ndarray." )

												if do_resize and size is None or resample is None:
																	raise ValueError("Size and resample must be specified if do_resize is True." )

												if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("crop_pct must be specified if size < 384." )

												if do_rescale and rescale_factor is None:
																	raise ValueError("Rescale factor must be specified if do_rescale is True." )

												if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError("Image mean and std must be specified if do_normalize is True." )

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												if do_resize:
																	SCREAMING_SNAKE_CASE           =    [self.resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							crop_pct=_UpperCamelCase		,							resample=_UpperCamelCase ) for image in images]

												if do_rescale:
																	SCREAMING_SNAKE_CASE           =    [self.rescale(image=_UpperCamelCase		,							scale=_UpperCamelCase ) for image in images]

												if do_normalize:
																	SCREAMING_SNAKE_CASE           =    [self.normalize(image=_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase		,							tensor_type=_UpperCamelCase )




