
"""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 the checkpointing 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
#
########################################################################

snake_case    =		1_6
snake_case    =		3_2

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_ = 1_6			):
					SCREAMING_SNAKE_CASE				=	AutoTokenizer.from_pretrained('bert-base-cased'			)
					SCREAMING_SNAKE_CASE				=	load_dataset('glue',     'mrpc'			)

					def tokenize_function(SCREAMING_SNAKE_CASE_			):
										# max_length=None => use the model max length (it's actually the default)
										SCREAMING_SNAKE_CASE				=	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():
										SCREAMING_SNAKE_CASE				=	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
					SCREAMING_SNAKE_CASE				=	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.
										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(
										    SCREAMING_SNAKE_CASE_,     padding='longest',     max_length=SCREAMING_SNAKE_CASE_,     pad_to_multiple_of=SCREAMING_SNAKE_CASE_,     return_tensors='pt',     )

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

					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

	snake_case    =		mocked_dataloaders  # noqa: F811

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS',     SCREAMING_SNAKE_CASE_			) == "1":
										SCREAMING_SNAKE_CASE				=	2
					# Initialize accelerator
					SCREAMING_SNAKE_CASE				=	Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision			)
					# 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']			)

					# New Code #
					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps,     'isdigit'			):
										if args.checkpointing_steps == "epoch":
															SCREAMING_SNAKE_CASE				=	args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															SCREAMING_SNAKE_CASE				=	int(args.checkpointing_steps			)
										else:
															raise ValueError(
															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
					else:
										SCREAMING_SNAKE_CASE				=	None

					set_seed(SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	get_dataloaders(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	evaluate.load('glue',     'mrpc'			)

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

					# 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=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).
					SCREAMING_SNAKE_CASE				=	model.to(accelerator.device			)

					# Instantiate optimizer
					SCREAMING_SNAKE_CASE				=	AdamW(params=model.parameters(),     lr=SCREAMING_SNAKE_CASE_			)

					# Instantiate scheduler
					SCREAMING_SNAKE_CASE				=	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.
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	accelerator.prepare(
					    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					SCREAMING_SNAKE_CASE				=	0
					# We also need to keep track of the stating epoch so files are named properly
					SCREAMING_SNAKE_CASE				=	0

					# We need to load the checkpoint back in before training here with `load_state`
					# The total number of epochs is adjusted based on where the state is being loaded from,
					# as we assume continuation of the same training script
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
															accelerator.load_state(args.resume_from_checkpoint			)
															SCREAMING_SNAKE_CASE				=	os.path.basename(args.resume_from_checkpoint			)
										else:
															# Get the most recent checkpoint
															SCREAMING_SNAKE_CASE				=	[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
															dirs.sort(key=os.path.getctime			)
															SCREAMING_SNAKE_CASE				=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										SCREAMING_SNAKE_CASE				=	os.path.splitext(SCREAMING_SNAKE_CASE_			)[0]

										if "epoch" in training_difference:
															SCREAMING_SNAKE_CASE				=	int(training_difference.replace('epoch_',     ''			)			) + 1
															SCREAMING_SNAKE_CASE				=	None
										else:
															SCREAMING_SNAKE_CASE				=	int(training_difference.replace('step_',     ''			)			)
															SCREAMING_SNAKE_CASE				=	resume_step // len(SCREAMING_SNAKE_CASE_			)
															resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_			)

    # Now we train the model
					for epoch in range(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										model.train()
										# New Code #
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															SCREAMING_SNAKE_CASE				=	accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															SCREAMING_SNAKE_CASE				=	train_dataloader
										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			)
															SCREAMING_SNAKE_CASE				=	model(**SCREAMING_SNAKE_CASE_			)
															SCREAMING_SNAKE_CASE				=	outputs.loss
															SCREAMING_SNAKE_CASE				=	loss / gradient_accumulation_steps
															accelerator.backward(SCREAMING_SNAKE_CASE_			)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
															# New Code #
															overall_step += 1

															# New Code #
															# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
															# These are saved to folders named `step_{overall_step}`
															# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
															# If mixed precision was used, will also save a "scalar.bin" file
															if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
																				SCREAMING_SNAKE_CASE				=	f'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														SCREAMING_SNAKE_CASE				=	os.path.join(args.output_dir,     SCREAMING_SNAKE_CASE_			)
																									accelerator.save_state(SCREAMING_SNAKE_CASE_			)

										model.eval()
										for step, batch in enumerate(SCREAMING_SNAKE_CASE_			):
															# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
															batch.to(accelerator.device			)
															with torch.no_grad():
																				SCREAMING_SNAKE_CASE				=	model(**SCREAMING_SNAKE_CASE_			)
															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=SCREAMING_SNAKE_CASE_,     references=SCREAMING_SNAKE_CASE_,     )

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

										# New Code #
										# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
										# These are saved to folders named `epoch_{epoch}`
										# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
										# If mixed precision was used, will also save a "scalar.bin" file
										if checkpointing_steps == "epoch":
															SCREAMING_SNAKE_CASE				=	f'''epoch_{epoch}'''
															if args.output_dir is not None:
																				SCREAMING_SNAKE_CASE				=	os.path.join(args.output_dir,     SCREAMING_SNAKE_CASE_			)
															accelerator.save_state(SCREAMING_SNAKE_CASE_			)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	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.'			)
					parser.add_argument(
					    '--checkpointing_steps',     type=SCREAMING_SNAKE_CASE_,     default=SCREAMING_SNAKE_CASE_,     help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',     )
					parser.add_argument(
					    '--output_dir',     type=SCREAMING_SNAKE_CASE_,     default='.',     help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',     )
					parser.add_argument(
					    '--resume_from_checkpoint',     type=SCREAMING_SNAKE_CASE_,     default=SCREAMING_SNAKE_CASE_,     help='If the training should continue from a checkpoint folder.',     )
					SCREAMING_SNAKE_CASE				=	parser.parse_args()
					SCREAMING_SNAKE_CASE				=	{'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()


"""simple docstring"""




from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import (
	    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
	    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
	    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
	    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
	)


snake_case    =		logging.get_logger(__name__)


snake_case    =		Dict[str, Any]
snake_case    =		List[Prediction]




@add_end_docstrings(__magic_name__      )
class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""simple docstring"""







									super().__init__(*lowercase__		,	**lowercase__			)

									if self.framework == "tf":
														raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''			)

									requires_backends(self		,	'vision'			)
									self.check_model_type(
									    dict(
									        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)


				def 							A					(				self		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	{}
									SCREAMING_SNAKE_CASE				=	{}
									if "subtask" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
									if "threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['threshold']
									if "mask_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['mask_threshold']
									if "overlap_mask_area_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['overlap_mask_area_threshold']

									return preprocess_kwargs, {}, postprocess_kwargs


				def __call__(				self		,	lowercase__		,	**lowercase__			)    ->    Union[Predictions, List[Prediction]]:
									"""simple docstring"""







									return super().__call__(lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	load_image(lowercase__			)
									SCREAMING_SNAKE_CASE				=	[(image.height, image.width)]
									if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																			SCREAMING_SNAKE_CASE				=	{}
														else:
																			SCREAMING_SNAKE_CASE				=	{'task_inputs': [subtask]}
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'		,	**lowercase__			)
														SCREAMING_SNAKE_CASE				=	self.tokenizer(
														    inputs['task_inputs']		,	padding='max_length'		,	max_length=self.model.config.task_seq_len		,	return_tensors=self.framework		,	)['input_ids']
									else:
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'			)
									SCREAMING_SNAKE_CASE				=	target_size
									return inputs


				def 							A					(				self		,	lowercase__			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	model_inputs.pop('target_size'			)
									SCREAMING_SNAKE_CASE				=	self.model(**lowercase__			)
									SCREAMING_SNAKE_CASE				=	target_size
									return model_outputs




				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=0.9		,	lowercase__=0.5		,	lowercase__=0.5			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	None
									if subtask in {"panoptic", None} and hasattr(self.image_processor		,	'post_process_panoptic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_panoptic_segmentation
									elif subtask in {"instance", None} and hasattr(self.image_processor		,	'post_process_instance_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_instance_segmentation

									if fn is not None:
														SCREAMING_SNAKE_CASE				=	fn(
														    lowercase__		,	threshold=lowercase__		,	mask_threshold=lowercase__		,	overlap_mask_area_threshold=lowercase__		,	target_sizes=model_outputs['target_size']		,	)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs['segmentation']

														for segment in outputs["segments_info"]:
																			SCREAMING_SNAKE_CASE				=	(segmentation == segment['id']) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.numpy().astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[segment['label_id']]
																			SCREAMING_SNAKE_CASE				=	segment['score']
																			annotation.append({'score': score, 'label': label, 'mask': mask}			)

									elif subtask in {"semantic", None} and hasattr(self.image_processor		,	'post_process_semantic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_semantic_segmentation(
														    lowercase__		,	target_sizes=model_outputs['target_size']			)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs.numpy()
														SCREAMING_SNAKE_CASE				=	np.unique(lowercase__			)

														for label in labels:
																			SCREAMING_SNAKE_CASE				=	(segmentation == label) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[label]
																			annotation.append({'score': None, 'label': label, 'mask': mask}			)
									else:
														raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model			)}'''			)
									return annotation
