





'''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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


_SCREAMING_SNAKE_CASE					=    1_6
_SCREAMING_SNAKE_CASE					=    3_2




def    _lowerCAmelCase						(				lowerCamelCase_			:  Accelerator		,       lowerCamelCase_			:  int = 1_6				):
			__lowercase										=							AutoTokenizer.from_pretrained('''bert-base-cased'''				)
			__lowercase										=							load_dataset('''glue'''		,       '''mrpc'''				)

			def tokenize_function(lowerCamelCase_			:  Any				):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase										=							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():
						__lowercase										=							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
			__lowercase										=							tokenized_datasets.rename_column('''label'''		,       '''labels'''				)

			def collate_fn(lowerCamelCase_			:  Dict				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__lowercase										=							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":
									__lowercase										=							1_6
						elif accelerator.mixed_precision != "no":
									__lowercase										=							8
						else:
									__lowercase										=							None

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

			# Instantiate dataloaders.
			__lowercase										=							DataLoader(
			    tokenized_datasets['''train''']		,       shuffle=lowerCamelCase_		,       collate_fn=lowerCamelCase_		,       batch_size=lowerCamelCase_				)
			__lowercase										=							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

	_SCREAMING_SNAKE_CASE					=    mocked_dataloaders  # noqa: F811




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Union[str, Any]				):
			# For testing only
			if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,       lowerCamelCase_				) == "1":
						__lowercase										=							2
			# Initialize accelerator
			__lowercase										=							Accelerator(cpu=args.cpu		,       mixed_precision=args.mixed_precision				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__lowercase										=							config['''lr''']
			__lowercase										=							int(config['''num_epochs''']				)
			__lowercase										=							int(config['''seed''']				)
			__lowercase										=							int(config['''batch_size''']				)

			__lowercase										=							evaluate.load('''glue'''		,       '''mrpc'''				)

			# If the batch size is too big we use gradient accumulation
			__lowercase										=							1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__lowercase										=							batch_size // MAX_GPU_BATCH_SIZE
						__lowercase										=							MAX_GPU_BATCH_SIZE

			set_seed(lowerCamelCase_				)
			__lowercase						,		__lowercase										=							get_dataloaders(lowerCamelCase_		,       lowerCamelCase_				)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__lowercase										=							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).
			__lowercase										=							model.to(accelerator.device				)

			# Instantiate optimizer
			__lowercase										=							AdamW(params=model.parameters()		,       lr=lowerCamelCase_				)

			# Instantiate scheduler
			__lowercase										=							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.
			__lowercase						,		__lowercase						,		__lowercase						,		__lowercase						,		__lowercase										=							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				)
									__lowercase										=							model(**lowerCamelCase_				)
									__lowercase										=							outputs.loss
									__lowercase										=							loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase_				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						__lowercase										=							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				)
									with torch.no_grad():
												__lowercase										=							model(**lowerCamelCase_				)
									__lowercase										=							outputs.logits.argmax(dim=-1				)
									__lowercase						,		__lowercase										=							accelerator.gather((predictions, batch['''labels'''])				)
									# New Code #
									# First we check if it's a distributed system
									if accelerator.use_distributed:
												# Then see if we're on the last batch of our eval dataloader
												if step == len(lowerCamelCase_				) - 1:
															# Last batch needs to be truncated on distributed systems as it contains additional samples
															__lowercase										=							predictions[: len(eval_dataloader.dataset				) - samples_seen]
															__lowercase										=							references[: len(eval_dataloader.dataset				) - samples_seen]
												else:
															# Otherwise we add the number of samples seen
															samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
									metric.add_batch(
									    predictions=lowerCamelCase_		,       references=lowerCamelCase_		,       )

						__lowercase										=							metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"epoch {epoch}:"		,       lowerCamelCase_				)




def    _lowerCAmelCase						(				):
			__lowercase										=							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.'''				)
			__lowercase										=							parser.parse_args()
			__lowercase										=							{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(lowerCamelCase_		,       lowerCamelCase_				)


if __name__ == "__main__":
	main()












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE					=    {
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''CLIPSegModel''',
	    '''CLIPSegPreTrainedModel''',
	    '''CLIPSegTextModel''',
	    '''CLIPSegVisionModel''',
	    '''CLIPSegForImageSegmentation''',
	]

if TYPE_CHECKING:
	from .configuration_clipseg import (
	    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    CLIPSegConfig,
	    CLIPSegTextConfig,
	    CLIPSegVisionConfig,
	)
	from .processing_clipseg import CLIPSegProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_clipseg import (
		    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CLIPSegForImageSegmentation,
		    CLIPSegModel,
		    CLIPSegPreTrainedModel,
		    CLIPSegTextModel,
		    CLIPSegVisionModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





