
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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        = 3_2




def     a						(							snake_case__: Accelerator						,       snake_case__: int = 16 ):


				'''simple docstring'''
				lowercase_					  = AutoTokenizer.from_pretrained('''bert-base-cased''' )
				lowercase_					  = load_dataset('''glue'''						,       '''mrpc''' )

				def tokenize_function(snake_case__: Optional[Any] ):
								# max_length=None => use the model max length (it's actually the default)
								lowercase_					  = tokenizer(examples['''sentence1''']						,       examples['''sentence2''']						,       truncation=snake_case__						,       max_length=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(
								    snake_case__						,       batched=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(snake_case__: Optional[int] ):
								# 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(
								    snake_case__						,       padding='''longest'''						,       pad_to_multiple_of=snake_case__						,       return_tensors='''pt'''						,       )

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

						__a        = mocked_dataloaders  # noqa: F811




def     a						(							snake_case__: Tuple						,       snake_case__: Any ):


				'''simple docstring'''
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,       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'''] )

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

				# New Code #
				# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
				# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
				# half each time. From this, we can calculate the number of gradient accumulation steps needed
				# and modify the Accelerator object as a result
				@find_executable_batch_size(starting_batch_size=int(snake_case__ ) )
				def inner_training_loop(snake_case__: Optional[int] ):
								# Since we need to modify the outside accelerator object, we need to bring it
								# to the local scope
								nonlocal accelerator

								# We can calculate the number of gradient accumulation steps based on the current
								# batch size vs the starting batch size
								lowercase_					  = observed_batch_size // batch_size

								# And then set it in the Accelerator directly:
								lowercase_					  = num_gradient_accumulation_steps

								# Next we need to free all of the stored model references in the Accelerator each time
								accelerator.free_memory()

								# And set the seed so our results are reproducable each reset
								set_seed(snake_case__ )

								# 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=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=snake_case__ )
								lowercase_  ,     lowercase_					  = get_dataloaders(snake_case__						,       snake_case__ )

								# Instantiate scheduler
								lowercase_					  = get_linear_schedule_with_warmup(
								    optimizer=snake_case__						,       num_warmup_steps=100						,       num_training_steps=(len(snake_case__ ) * 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.
								lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = accelerator.prepare(
								    snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						,       snake_case__ )

								# Now we train the model
								for epoch in range(snake_case__ ):
												model.train()
												for step, batch in enumerate(snake_case__ ):
																# And perform gradient accumulation
																with accelerator.accumulate(snake_case__ ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device )
																				lowercase_					  = model(**snake_case__ )
																				lowercase_					  = outputs.loss
																				accelerator.backward(snake_case__ )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(snake_case__ ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device )
																with torch.no_grad():
																				lowercase_					  = model(**snake_case__ )
																lowercase_					  = outputs.logits.argmax(dim=-1 )
																lowercase_  ,     lowercase_					  = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
																metric.add_batch(
																    predictions=snake_case__						,       references=snake_case__						,       )

												lowercase_					  = metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''						,       snake_case__ )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser(description='''Simple example of training script.''' )
				parser.add_argument(
				    '''--mixed_precision'''						,       type=snake_case__						,       default=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.''' )
				lowercase_					  = parser.parse_args()
				# New Code #
				# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
				lowercase_					  = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
				training_function(snake_case__						,       snake_case__ )


if __name__ == "__main__":
						main()






import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__a        = False
__a        = Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
__a        = datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=sys.argv[1:]	)	->					int:
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = None
					lowercase_					  = 1
					lowercase_  ,     lowercase_					  = getopt.getopt(SCREAMING_SNAKE_CASE_	,							'''i:o:b:s'''	,							['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
					for opt, arg in opts:
									if opt in ("-i", "--inputdir"):
													lowercase_					  = arg
									elif opt in ("-o", "--outfile"):
													lowercase_					  = arg
									elif opt in ("-b", "--batch_size"):
													lowercase_					  = int(SCREAMING_SNAKE_CASE_	)
									elif opt in ("-s", "--subset_list"):
													lowercase_					  = arg

					assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
					assert outputfile is not None and not os.path.isfile(SCREAMING_SNAKE_CASE_	), f'''{outputfile}'''
					if subset_list is not None:
									with open(os.path.realpath(SCREAMING_SNAKE_CASE_	)	) as f:
													lowercase_					  = {self._vqa_file_split()[0] for x in tryload(SCREAMING_SNAKE_CASE_	)}
					else:
									lowercase_					  = None

					lowercase_					  = CONFIG
					if torch.cuda.is_available():
									lowercase_					  = '''cuda'''
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = os.path.realpath(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = Preprocess(self.config	)
					lowercase_					  = GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''	,							config=self.config	)
					lowercase_					  = batch_size if batch_size != 0 else 1
					lowercase_					  = DEFAULT_SCHEMA
	def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	)	->					Any:
					lowercase_					  = int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
					lowercase_					  = os.path.join(self.inputdir	,							SCREAMING_SNAKE_CASE_	)
					return (img_id, filepath)
	@property
	def 						_lowercase  (	self  :      List[str]	)	->					List[Any]:
					lowercase_					  = []
					for i, file in enumerate(os.listdir(self.inputdir	)	):
									if self.subset_list is not None and i not in self.subset_list:
													continue
									batch.append(self._vqa_file_split(SCREAMING_SNAKE_CASE_	)	)
									if len(SCREAMING_SNAKE_CASE_	) == self.batch:
													lowercase_					  = batch
													lowercase_					  = []
													yield list(map(SCREAMING_SNAKE_CASE_	,							zip(*SCREAMING_SNAKE_CASE_	)	)	)

					for i in range(1	):
									yield list(map(SCREAMING_SNAKE_CASE_	,							zip(*SCREAMING_SNAKE_CASE_	)	)	)




	def __call__(	self  :      Any	)	->					List[str]:
					# make writer
					if not TEST:
									lowercase_					  = datasets.ArrowWriter(features=self.schema	,							path=self.outputfile	)
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator	):
									lowercase_  ,     lowercase_  ,     lowercase_					  = self.preprocess(SCREAMING_SNAKE_CASE_	)
									lowercase_					  = self.model(
									    SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							scales_yx=SCREAMING_SNAKE_CASE_	,							padding='''max_detections'''	,							max_detections=self.config.MAX_DETECTIONS	,							pad_value=0	,							return_tensors='''np'''	,							location='''cpu'''	,							)
									lowercase_					  = output_dict.pop('''normalized_boxes'''	)
									if not TEST:
													lowercase_					  = np.array(SCREAMING_SNAKE_CASE_	)
													lowercase_					  = self.schema.encode_batch(SCREAMING_SNAKE_CASE_	)
													writer.write_batch(SCREAMING_SNAKE_CASE_	)
									if TEST:
													break
									# finalizer the writer
					if not TEST:
									lowercase_  ,     lowercase_					  = writer.finalize()
									print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''	)




def     a						(							snake_case__: Optional[Any] ):


				'''simple docstring'''
				try:
								lowercase_					  = json.load(snake_case__ )
								try:
												lowercase_					  = list(data.keys() )
								except Exception:
												lowercase_					  = [d['''img_id'''] for d in data]
				except Exception:
								try:
												lowercase_					  = eval(stream.read() )
								except Exception:
												lowercase_					  = stream.read().split('''\n''' )
				return data


if __name__ == "__main__":
						__a        = Extract(sys.argv[1:])
						extract()
						if not TEST:
												__a        = datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)




