



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

__magic_name__       =     16
__magic_name__       =     32





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = 16				):
					"""simple docstring"""






					lowercase						   =       AutoTokenizer.from_pretrained('bert-base-cased'				)
					lowercase						   =       load_dataset('glue'       ,			'mrpc'				)

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

					def collate_fn(_UpperCAmelCase				):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										lowercase						   =       1_28 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						   =       16
										elif accelerator.mixed_precision != "no":
															lowercase						   =       8
										else:
															lowercase						   =       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.
					lowercase						   =       DataLoader(
					    tokenized_datasets['train']       ,			shuffle=__SCREAMING_SNAKE_CASE       ,			collate_fn=__SCREAMING_SNAKE_CASE       ,			batch_size=__SCREAMING_SNAKE_CASE				)
					lowercase						   =       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

		__magic_name__       =     mocked_dataloaders  # noqa: F811





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,			__SCREAMING_SNAKE_CASE				) == "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']				)

					# 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":
															lowercase						   =       args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															lowercase						   =       int(args.checkpointing_steps				)
										else:
															raise ValueError(
															    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""				)
					else:
										lowercase						   =       None

					set_seed(__SCREAMING_SNAKE_CASE				)

					lowercase   ,      lowercase						   =       get_dataloaders(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE				)
					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

					# 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=__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).
					lowercase						   =       model.to(accelerator.device				)

					# Instantiate optimizer
					lowercase						   =       AdamW(params=model.parameters()       ,			lr=__SCREAMING_SNAKE_CASE				)

					# Instantiate scheduler
					lowercase						   =       get_linear_schedule_with_warmup(
					    optimizer=__SCREAMING_SNAKE_CASE       ,			num_warmup_steps=1_00       ,			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.
					lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase						   =       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
					lowercase						   =       0
					# We also need to keep track of the stating epoch so files are named properly
					lowercase						   =       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				)
															lowercase						   =       os.path.basename(args.resume_from_checkpoint				)
										else:
															# Get the most recent checkpoint
															lowercase						   =       [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
															dirs.sort(key=os.path.getctime				)
															lowercase						   =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										lowercase						   =       os.path.splitext(__SCREAMING_SNAKE_CASE				)[0]

										if "epoch" in training_difference:
															lowercase						   =       int(training_difference.replace('epoch_'       ,			''				)				) + 1
															lowercase						   =       None
										else:
															lowercase						   =       int(training_difference.replace('step_'       ,			''				)				)
															lowercase						   =       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
															lowercase						   =       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
															lowercase						   =       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				)
															lowercase						   =       model(**__SCREAMING_SNAKE_CASE				)
															lowercase						   =       outputs.loss
															lowercase						   =       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				):
																				lowercase						   =       f"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														lowercase						   =       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():
																				lowercase						   =       model(**__SCREAMING_SNAKE_CASE				)
															lowercase						   =       outputs.logits.argmax(dim=-1				)
															lowercase   ,      lowercase						   =       accelerator.gather_for_metrics((predictions, batch['labels'])				)
															metric.add_batch(
															    predictions=__SCREAMING_SNAKE_CASE       ,			references=__SCREAMING_SNAKE_CASE       ,			)

										lowercase						   =       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":
															lowercase						   =       f"""epoch_{epoch}"""
															if args.output_dir is not None:
																				lowercase						   =       os.path.join(args.output_dir       ,			__SCREAMING_SNAKE_CASE				)
															accelerator.save_state(__SCREAMING_SNAKE_CASE				)





def 		__snake_case						(    ):
					"""simple docstring"""






					lowercase						   =       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.'       ,			)
					lowercase						   =       parser.parse_args()
					lowercase						   =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(__SCREAMING_SNAKE_CASE       ,			__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
		main()








import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       UpperCamelCase_				 =							'''oneformer'''
       UpperCamelCase_				 =							{'''hidden_size''': '''hidden_dim'''}



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 2]  ,      num_heads=[3, 6, 12, 24]  ,      window_size=7  ,      drop_path_rate=0.3  ,      use_absolute_embeddings=_lowerCAmelCase  ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
