

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
#
########################################################################

a		=16
a		=32






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 1_6      )  ->					int:
	__lowerCamelCase    :    Any 	=			AutoTokenizer.from_pretrained('bert-base-cased'      )
	__lowerCamelCase    :    Dict 	=			load_dataset('glue'     ,			'mrpc'      )

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

	def collate_fn(lowerCamelCase__      ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__lowerCamelCase    :    Any 	=			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":
			__lowerCamelCase    :    Tuple 	=			1_6
		elif accelerator.mixed_precision != "no":
			__lowerCamelCase    :    Union[str, Any] 	=			8
		else:
			__lowerCamelCase    :    Optional[int] 	=			None

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

	# Instantiate dataloaders.
	__lowerCamelCase    :    Optional[int] 	=			DataLoader(
	    tokenized_datasets['train']     ,			shuffle=lowerCamelCase__     ,			collate_fn=lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			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

					a		=mocked_dataloaders  # noqa: F811






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					str:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,			lowerCamelCase__      ) == "1":
		__lowerCamelCase    :    Tuple 	=			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:
		__lowerCamelCase    :    Dict 	=			Accelerator(
		    cpu=args.cpu     ,			mixed_precision=args.mixed_precision     ,			log_with='all'     ,			project_dir=args.project_dir      )
	else:
		__lowerCamelCase    :    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
	__lowerCamelCase    :    Tuple 	=			config['lr']
	__lowerCamelCase    :    str 	=			int(config['num_epochs']      )
	__lowerCamelCase    :    Tuple 	=			int(config['seed']      )
	__lowerCamelCase    :    Union[str, Any] 	=			int(config['batch_size']      )
	set_seed(lowerCamelCase__      )

	__lowerCamelCase	,			__lowerCamelCase    :    int 	=			get_dataloaders(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			evaluate.load('glue'     ,			'mrpc'      )

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

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowerCamelCase    :    str 	=			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).
	__lowerCamelCase    :    List[Any] 	=			model.to(accelerator.device      )

	# Instantiate optimizer
	__lowerCamelCase    :    Optional[int] 	=			AdamW(params=model.parameters()     ,			lr=lowerCamelCase__      )

	# Instantiate scheduler
	__lowerCamelCase    :    List[str] 	=			get_linear_schedule_with_warmup(
	    optimizer=lowerCamelCase__     ,			num_warmup_steps=1_0_0     ,			num_training_steps=(len(lowerCamelCase__      ) * 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.
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    str 	=			accelerator.prepare(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )

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

	# Now we train the model
	for epoch in range(lowerCamelCase__      ):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			__lowerCamelCase    :    Optional[Any] 	=			0
		for step, batch in enumerate(lowerCamelCase__      ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device      )
			__lowerCamelCase    :    Tuple 	=			model(**lowerCamelCase__      )
			__lowerCamelCase    :    Dict 	=			outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			__lowerCamelCase    :    str 	=			loss / gradient_accumulation_steps
			accelerator.backward(lowerCamelCase__      )
			if step % gradient_accumulation_steps == 0:
				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` (the default).
			batch.to(accelerator.device      )
			with torch.no_grad():
				__lowerCamelCase    :    Any 	=			model(**lowerCamelCase__      )
			__lowerCamelCase    :    Union[str, Any] 	=			outputs.logits.argmax(dim=-1      )
			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			accelerator.gather_for_metrics((predictions, batch['labels'])      )
			metric.add_batch(
			    predictions=lowerCamelCase__     ,			references=lowerCamelCase__     ,			)

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

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

    # 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        SCREAMING_SNAKE_CASE__	(  )  ->					Optional[int]:
	__lowerCamelCase    :    Any 	=			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.'     ,			)
	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=lowerCamelCase__     ,			default='logs'     ,			help='Location on where to store experiment tracking logs` and relevent project information'     ,			)
	__lowerCamelCase    :    Optional[Any] 	=			parser.parse_args()
	__lowerCamelCase    :    Dict 	=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(lowerCamelCase__     ,			lowerCamelCase__      )


if __name__ == "__main__":
					main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Pix2StructImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Pix2StructPreTrainedModel""",
					    """Pix2StructForConditionalGeneration""",
					    """Pix2StructVisionModel""",
					    """Pix2StructTextModel""",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




