




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase__              =						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase__              =						{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase__              =						{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase__              =						sorted(arg_to_scheduler.keys())
UpperCamelCase__              =						'{' + ', '.join(arg_to_scheduler_choices) + '}'


class 				__SCREAMING_SNAKE_CASE							(		pl.LightningModule  ):

					def __init__(				self ,       __lowercase ,       __lowercase=None ,       __lowercase="base" ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       **__lowercase ,       ):







						'''simple docstring'''




						super().__init__()
						# TODO: move to self.save_hyperparameters()
						# self.save_hyperparameters()
						# can also expand arguments into trainer signature for easier reading

						self.save_hyperparameters(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							0
						lowerCamelCase_							:		Optional[Any]							   =							Path(self.hparams.output_dir					)
						lowerCamelCase_							:		Dict							   =							self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
							lowerCamelCase_							:		Tuple							   =							AutoConfig.from_pretrained(
							    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path ,       **({'num_labels': num_labels} if num_labels is not None else {}) ,       cache_dir=__lowercase ,       **__lowercase ,       )
						else:
							lowerCamelCase_							:		PretrainedConfig							   =							config

						lowerCamelCase_							:		Dict							   =							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
							if getattr(self.hparams ,       __lowercase ,       __lowercase					):
								assert hasattr(self.config ,       __lowercase					), f"model config doesn't have a `{p}` attribute"
								setattr(self.config ,       __lowercase ,       getattr(self.hparams ,       __lowercase					)					)

						if tokenizer is None:
							lowerCamelCase_							:		Union[str, Any]							   =							AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,       cache_dir=__lowercase ,       )
						else:
							lowerCamelCase_							:		PreTrainedTokenizer							   =							tokenizer
						lowerCamelCase_							:		str							   =							MODEL_MODES[mode]
						if model is None:
							lowerCamelCase_							:		List[Any]							   =							self.model_type.from_pretrained(
							    self.hparams.model_name_or_path ,       from_tf=bool('.ckpt' in self.hparams.model_name_or_path					) ,       config=self.config ,       cache_dir=__lowercase ,       )
						else:
							lowerCamelCase_							:		List[str]							   =							model

					def 	lowercase_							(				self ,       *__lowercase ,       **__lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							self.model_type.from_pretrained(*__lowercase ,       **__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							arg_to_scheduler[self.hparams.lr_scheduler]
						lowerCamelCase_							:		List[Any]							   =							get_schedule_func(
						    self.opt ,       num_warmup_steps=self.hparams.warmup_steps ,       num_training_steps=self.total_steps()					)
						lowerCamelCase_							:		int							   =							{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
						return scheduler

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.model
						lowerCamelCase_							:		int							   =							['bias', 'LayerNorm.weight']
						lowerCamelCase_							:		int							   =							[
						    {
						        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
						        'weight_decay': self.hparams.weight_decay,
						    },
						    {
						        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
						        'weight_decay': 0.0,
						    },
						]
						if self.hparams.adafactor:
							lowerCamelCase_							:		int							   =							Adafactor(
							    __lowercase ,       lr=self.hparams.learning_rate ,       scale_parameter=__lowercase ,       relative_step=__lowercase					)

						else:
							lowerCamelCase_							:		int							   =							AdamW(
							    __lowercase ,       lr=self.hparams.learning_rate ,       eps=self.hparams.adam_epsilon					)
						lowerCamelCase_							:		str							   =							optimizer

						lowerCamelCase_							:		int							   =							self.get_lr_scheduler()

						return [optimizer], [scheduler]

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						return self.validation_step(__lowercase ,       __lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return self.validation_end(__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							max(1 ,       self.hparams.gpus					)  # TODO: consider num_tpu_cores
						lowerCamelCase_							:		str							   =							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
						return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						if stage == "test":
							lowerCamelCase_							:		Dict							   =							len(self.test_dataloader().dataset					)
						else:
							lowerCamelCase_							:		List[str]							   =							self.get_dataloader('train' ,       self.hparams.train_batch_size ,       shuffle=__lowercase					)
							lowerCamelCase_							:		int							   =							len(self.train_dataloader().dataset					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase = False					):







						'''simple docstring'''




						raise NotImplementedError('You must implement this for your task'					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.train_loader

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.get_dataloader('dev' ,       self.hparams.eval_batch_size ,       shuffle=__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.get_dataloader('test' ,       self.hparams.eval_batch_size ,       shuffle=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return os.path.join(
						    self.hparams.data_dir ,       'cached_{}_{}_{}'.format(
						        __lowercase ,       list(filter(__lowercase ,       self.hparams.model_name_or_path.split('/'					)					)					).pop() ,       str(self.hparams.max_seq_length					) ,       ) ,       )

					@pl.utilities.rank_zero_only
					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							self.output_dir.joinpath('best_tfmr'					)
						lowerCamelCase_							:		Tuple							   =							self.step_count
						self.model.save_pretrained(__lowercase					)
						self.tokenizer.save_pretrained(__lowercase					)





					@staticmethod
					def 	lowercase_							(				__lowercase ,       __lowercase					):







						'''simple docstring'''




						parser.add_argument(
						    '--model_name_or_path' ,       default=__lowercase ,       type=__lowercase ,       required=__lowercase ,       help='Path to pretrained model or model identifier from huggingface.co/models' ,       )
						parser.add_argument(
						    '--config_name' ,       default='' ,       type=__lowercase ,       help='Pretrained config name or path if not the same as model_name'					)
						parser.add_argument(
						    '--tokenizer_name' ,       default=__lowercase ,       type=__lowercase ,       help='Pretrained tokenizer name or path if not the same as model_name' ,       )
						parser.add_argument(
						    '--cache_dir' ,       default='' ,       type=__lowercase ,       help='Where do you want to store the pre-trained models downloaded from huggingface.co' ,       )
						parser.add_argument(
						    '--encoder_layerdrop' ,       type=__lowercase ,       help='Encoder layer dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--decoder_layerdrop' ,       type=__lowercase ,       help='Decoder layer dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--dropout' ,       type=__lowercase ,       help='Dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--attention_dropout' ,       type=__lowercase ,       help='Attention dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument('--learning_rate' ,       default=5E-5 ,       type=__lowercase ,       help='The initial learning rate for Adam.'					)
						parser.add_argument(
						    '--lr_scheduler' ,       default='linear' ,       choices=__lowercase ,       metavar=__lowercase ,       type=__lowercase ,       help='Learning rate scheduler' ,       )
						parser.add_argument('--weight_decay' ,       default=0.0 ,       type=__lowercase ,       help='Weight decay if we apply some.'					)
						parser.add_argument('--adam_epsilon' ,       default=1E-8 ,       type=__lowercase ,       help='Epsilon for Adam optimizer.'					)
						parser.add_argument('--warmup_steps' ,       default=0 ,       type=__lowercase ,       help='Linear warmup over warmup_steps.'					)
						parser.add_argument('--num_workers' ,       default=4 ,       type=__lowercase ,       help='kwarg passed to DataLoader'					)
						parser.add_argument('--num_train_epochs' ,       dest='max_epochs' ,       default=3 ,       type=__lowercase					)
						parser.add_argument('--train_batch_size' ,       default=32 ,       type=__lowercase					)
						parser.add_argument('--eval_batch_size' ,       default=32 ,       type=__lowercase					)
						parser.add_argument('--adafactor' ,       action='store_true'					)


class 				__SCREAMING_SNAKE_CASE							(		pl.Callback  ):

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						if (
						    trainer.is_global_zero and trainer.global_rank == 0
						):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class 				__SCREAMING_SNAKE_CASE							(		pl.Callback  ):

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							trainer.lr_schedulers[0]['scheduler']
						lowerCamelCase_							:		Optional[Any]							   =							{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
						pl_module.logger.log_metrics(__lowercase					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						rank_zero_info('***** Validation results *****'					)
						lowerCamelCase_							:		Optional[Any]							   =							trainer.callback_metrics
						# Log results
						for key in sorted(__lowercase					):
							if key not in ["log", "progress_bar"]:
								rank_zero_info('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						rank_zero_info('***** Test results *****'					)
						lowerCamelCase_							:		Union[str, Any]							   =							trainer.callback_metrics
						# Log and save results to file
						lowerCamelCase_							:		Tuple							   =							os.path.join(pl_module.hparams.output_dir ,       'test_results.txt'					)
						with open(__lowercase ,       'w'					) as writer:
							for key in sorted(__lowercase					):
								if key not in ["log", "progress_bar"]:
									rank_zero_info('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)
									writer.write('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)






def 					snake_case					(							A						,   A			)							->							None:




	'''simple docstring'''


	parser.add_argument(
	    '--output_dir'						,   default=A						,   type=A						,   required=A						,   help='The output directory where the model predictions and checkpoints will be written.'						,   )
	parser.add_argument(
	    '--fp16'						,   action='store_true'						,   help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'						,   )

	parser.add_argument(
	    '--fp16_opt_level'						,   type=A						,   default='O2'						,   help=(
	        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
	        'See details at https://nvidia.github.io/apex/amp.html'
	    )						,   )
	parser.add_argument('--n_tpu_cores'						,   dest='tpu_cores'						,   type=A			)
	parser.add_argument('--max_grad_norm'						,   dest='gradient_clip_val'						,   default=1.0						,   type=A						,   help='Max gradient norm'			)
	parser.add_argument('--do_train'						,   action='store_true'						,   help='Whether to run training.'			)
	parser.add_argument('--do_predict'						,   action='store_true'						,   help='Whether to run predictions on the test set.'			)
	parser.add_argument(
	    '--gradient_accumulation_steps'						,   dest='accumulate_grad_batches'						,   type=A						,   default=1						,   help='Number of updates steps to accumulate before performing a backward/update pass.'						,   )
	parser.add_argument('--seed'						,   type=A						,   default=42						,   help='random seed for initialization'			)
	parser.add_argument(
	    '--data_dir'						,   default=A						,   type=A						,   required=A						,   help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,   )






def 					snake_case					(							A						,   A						,   A=None						,   A=True						,   A=None						,   A=[]						,   A=None						,   A=None						,   **A						,   )							->							Union[str, Any]:




	'''simple docstring'''


	pl.seed_everything(args.seed			)

	# init model
	lowerCamelCase_							:		Tuple							   =							Path(model.hparams.output_dir			)
	odir.mkdir(exist_ok=A			)

	# add custom checkpoints
	if checkpoint_callback is None:
		lowerCamelCase_							:		Dict							   =							pl.callbacks.ModelCheckpoint(
		    filepath=args.output_dir						,   prefix='checkpoint'						,   monitor='val_loss'						,   mode='min'						,   save_top_k=1			)
	if early_stopping_callback:
		extra_callbacks.append(A			)
	if logging_callback is None:
		lowerCamelCase_							:		Optional[Any]							   =							LoggingCallback()

	lowerCamelCase_							:		Tuple							   =							{}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		lowerCamelCase_							:		str							   =							16
		# train_params["amp_level"] = args.fp16_opt_level

	if args.gpus > 1:
		lowerCamelCase_							:		Optional[int]							   =							'auto'  # "ddp"
		lowerCamelCase_							:		Any							   =							'ddp'

	lowerCamelCase_							:		Optional[int]							   =							args.accumulate_grad_batches
	lowerCamelCase_							:		str							   =							None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
	lowerCamelCase_							:		Union[str, Any]							   =							'auto'

	lowerCamelCase_							:		Optional[Any]							   =							pl.Trainer.from_argparse_args(
	    A						,   weights_summary=A						,   callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,   logger=A						,   **A						,   )

	if args.do_train:
		trainer.fit(A			)

	return trainer


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
lowerCamelCase_  :       Tuple					      =     False
lowerCamelCase_  :       int					      =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowerCamelCase_  :       Any					      =     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, 20_48), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

class 				_UpperCamelCase :






			'''simple docstring'''

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=sys.argv[1:]					):
							UpperCamelCase_:							str		=  None
							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Tuple		=  None
							UpperCamelCase_:							Tuple		=  1
							UpperCamelCase_,      UpperCamelCase_:							Optional[Any]		=  getopt.getopt(snake_case_  ,		"""i:o:b:s"""  ,		["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]					)
							for opt, arg in opts:
											if opt in ("-i", "--inputdir"):
															UpperCamelCase_:							Dict		=  arg
											elif opt in ("-o", "--outfile"):
															UpperCamelCase_:							Optional[Any]		=  arg
											elif opt in ("-b", "--batch_size"):
															UpperCamelCase_:							List[str]		=  int(snake_case_					)
											elif opt in ("-s", "--subset_list"):
															UpperCamelCase_:							Dict		=  arg

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

							UpperCamelCase_:							Tuple		=  CONFIG
							if torch.cuda.is_available():
											UpperCamelCase_:							Dict		=  """cuda"""
							UpperCamelCase_:							int		=  os.path.realpath(snake_case_					)
							UpperCamelCase_:							List[str]		=  os.path.realpath(snake_case_					)
							UpperCamelCase_:							str		=  Preprocess(self.config					)
							UpperCamelCase_:							int		=  GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""  ,		config=self.config					)
							UpperCamelCase_:							Union[str, Any]		=  batch_size if batch_size != 0 else 1
							UpperCamelCase_:							Dict		=  DEFAULT_SCHEMA

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				int					):
							UpperCamelCase_:							int		=  int(file.split("""."""					)[0].split("""_"""					)[-1]					)
							UpperCamelCase_:							int		=  os.path.join(self.inputdir  ,		snake_case_					)
							return (img_id, filepath)

			@property
			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Dict		=  []
							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(snake_case_					)					)
											if len(snake_case_					) == self.batch:
															UpperCamelCase_:							Dict		=  batch
															UpperCamelCase_:							List[Any]		=  []
															yield list(map(snake_case_  ,		zip(*snake_case_					)					)					)

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




			def __call__(				self       :				Optional[int]					):
							# make writer
							if not TEST:
											UpperCamelCase_:							Optional[Any]		=  datasets.ArrowWriter(features=self.schema  ,		path=self.outputfile					)
							# do file generator
							for i, (img_ids, filepaths) in enumerate(self.file_generator					):
											UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Dict		=  self.preprocess(snake_case_					)
											UpperCamelCase_:							Optional[Any]		=  self.model(
											    snake_case_  ,		snake_case_  ,		scales_yx=snake_case_  ,		padding="""max_detections"""  ,		max_detections=self.config.MAX_DETECTIONS  ,		pad_value=0  ,		return_tensors="""np"""  ,		location="""cpu"""  ,		)
											UpperCamelCase_:							Tuple		=  output_dict.pop("""normalized_boxes"""					)
											if not TEST:
															UpperCamelCase_:							Union[str, Any]		=  np.array(snake_case_					)
															UpperCamelCase_:							int		=  self.schema.encode_batch(snake_case_					)
															writer.write_batch(snake_case_					)
											if TEST:
															break
											# finalizer the writer
							if not TEST:
											UpperCamelCase_,      UpperCamelCase_:							int		=  writer.finalize()
											print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''					)







def 				A__    (  lowerCamelCase  )     ->		Tuple:
				try:
								UpperCamelCase_:							str		=  json.load(lowerCamelCase  )
								try:
												UpperCamelCase_:							List[Any]		=  list(data.keys()  )
								except Exception:
												UpperCamelCase_:							Tuple		=  [d["""img_id"""] for d in data]
				except Exception:
								try:
												UpperCamelCase_:							int		=  eval(stream.read()  )
								except Exception:
												UpperCamelCase_:							Union[str, Any]		=  stream.read().split("""\n"""  )
				return data


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






