


"""simple docstring"""






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  =	False
_lowerCamelCase  =	Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_lowerCamelCase  =	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, 2048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__=sys.argv[1:]  ):
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					1
								SCREAMING_SNAKE_CASE__  				=					getopt.getopt(_lowercase							,		"i:o:b:s"							,		["inputdir=", "outfile=", "batch_size=", "subset_list="]  )
								for opt, arg in opts:
												if opt in ("-i", "--inputdir"):
																SCREAMING_SNAKE_CASE__  				=					arg
												elif opt in ("-o", "--outfile"):
																SCREAMING_SNAKE_CASE__  				=					arg
												elif opt in ("-b", "--batch_size"):
																SCREAMING_SNAKE_CASE__  				=					int(_lowercase  )
												elif opt in ("-s", "--subset_list"):
																SCREAMING_SNAKE_CASE__  				=					arg

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

								SCREAMING_SNAKE_CASE__  				=					CONFIG
								if torch.cuda.is_available():
												SCREAMING_SNAKE_CASE__  				=					'cuda'
								SCREAMING_SNAKE_CASE__  				=					os.path.realpath(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					os.path.realpath(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					Preprocess(self.config  )
								SCREAMING_SNAKE_CASE__  				=					GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"							,		config=self.config  )
								SCREAMING_SNAKE_CASE__  				=					batch_size if batch_size != 0 else 1
								SCREAMING_SNAKE_CASE__  				=					DEFAULT_SCHEMA



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					int(file.split("."  )[0].split("_"  )[-1]  )
								SCREAMING_SNAKE_CASE__  				=					os.path.join(self.inputdir							,		_lowercase  )
								return (img_id, filepath)



				@property
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								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(_lowercase  )  )
												if len(_lowercase  ) == self.batch:
																SCREAMING_SNAKE_CASE__  				=					batch
																SCREAMING_SNAKE_CASE__  				=					[]
																yield list(map(_lowercase							,		zip(*_lowercase  )  )  )

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






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


def __lowercase  (   lowerCamelCase_	:   Dict	):
				try:
								SCREAMING_SNAKE_CASE__  				=					json.load(lowerCamelCase_	)
								try:
												SCREAMING_SNAKE_CASE__  				=					list(data.keys()	)
								except Exception:
												SCREAMING_SNAKE_CASE__  				=					[d['img_id'] for d in data]
				except Exception:
								try:
												SCREAMING_SNAKE_CASE__  				=					eval(stream.read()	)
								except Exception:
												SCREAMING_SNAKE_CASE__  				=					stream.read().split("\n"	)
				return data


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








'''simple docstring'''

import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


A:   List[str]						=					"1.5.0"

A:   Any						=					["vision-encoder-decoder"]






def 						_UpperCAmelCase     (	a		:						int				)      ->      List[str]:





						"""simple docstring"""
						if is_optimum_available():
												from optimum.version import __version__ as optimum_version

												lowercase_   : List[Any]   	=      version.parse(a				)
												if parsed_optimum_version < version.parse(a				):
																		raise RuntimeError(
																		    f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
																		    'can upgrade optimum by running: pip install -U optimum[exporters]'				)
						else:
												raise RuntimeError(
												    'transformers.onnx requires optimum to run, you can install the library by running: pip install '
												    'optimum[exporters]'				)
						lowercase_   : List[Any]   	=      [
						    sys.executable,
						    '-m',
						    'optimum.exporters.onnx',
						    f"--model {args.model}",
						    f"--task {args.feature}",
						    f"--framework {args.framework}" if args.framework is not None else '',
						    f"{args.output}",
						]
						lowercase_   : Tuple   	=      subprocess.Popen(' '.join(a				)    ,      stdout=subprocess.PIPE    ,      shell=a				)
						proc.wait()

						logger.info(
						    'The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as '
						    'transformers.onnx is deprecated, and will be removed in v5. You can find more information here: '
						    'https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'				)






def 						_UpperCAmelCase     (	a		:						Dict				)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      args.output if args.output.is_file() else args.output.joinpath('model.onnx'				)
						if not args.output.parent.exists():
												args.output.parent.mkdir(parents=a				)

						# Allocate the model
						lowercase_   : int   	=      FeaturesManager.get_model_from_feature(
						    args.feature    ,      args.model    ,      framework=args.framework    ,      cache_dir=args.cache_dir				)

						lowercase_	,   lowercase_   : List[Any]   	=      FeaturesManager.check_supported_model_or_raise(a    ,      feature=args.feature				)
						lowercase_   : List[str]   	=      model_onnx_config(model.config				)

						if model_kind in ENCODER_DECODER_MODELS:
												lowercase_   : List[Any]   	=      model.get_encoder()
												lowercase_   : Any   	=      model.get_decoder()

												lowercase_   : List[str]   	=      onnx_config.get_encoder_config(encoder_model.config				)
												lowercase_   : int   	=      onnx_config.get_decoder_config(
												    encoder_model.config    ,      decoder_model.config    ,      feature=args.feature				)

												if args.opset is None:
																		lowercase_   : int   	=      max(encoder_onnx_config.default_onnx_opset    ,      decoder_onnx_config.default_onnx_opset				)

												if args.opset < min(encoder_onnx_config.default_onnx_opset    ,      decoder_onnx_config.default_onnx_opset				):
																		raise ValueError(
																		    f"Opset {args.opset} is not sufficient to export {model_kind}. At least "
																		    f" {min(encoder_onnx_config.default_onnx_opset    ,      decoder_onnx_config.default_onnx_opset				)} is required."				)

												lowercase_   : str   	=      AutoFeatureExtractor.from_pretrained(args.model				)

												lowercase_	,   lowercase_   : str   	=      export(
												    a    ,      a    ,      a    ,      args.opset    ,      args.output.parent.joinpath('encoder_model.onnx'				)    ,      )

												validate_model_outputs(
												    a    ,      a    ,      a    ,      args.output.parent.joinpath('encoder_model.onnx'				)    ,      a    ,      args.atol if args.atol else encoder_onnx_config.atol_for_validation    ,      )

												lowercase_   : Optional[Any]   	=      AutoTokenizer.from_pretrained(args.model				)

												lowercase_	,   lowercase_   : Union[str, Any]   	=      export(
												    a    ,      a    ,      a    ,      args.opset    ,      args.output.parent.joinpath('decoder_model.onnx'				)    ,      )

												validate_model_outputs(
												    a    ,      a    ,      a    ,      args.output.parent.joinpath('decoder_model.onnx'				)    ,      a    ,      args.atol if args.atol else decoder_onnx_config.atol_for_validation    ,      )
												logger.info(
												    f"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'				).as_posix()},"
												    f" {args.output.parent.joinpath('decoder_model.onnx'				).as_posix()}"				)

						else:
												# Instantiate the appropriate preprocessor
												if args.preprocessor == "auto":
																		lowercase_   : List[Any]   	=      get_preprocessor(args.model				)
												elif args.preprocessor == "tokenizer":
																		lowercase_   : List[str]   	=      AutoTokenizer.from_pretrained(args.model				)
												elif args.preprocessor == "image_processor":
																		lowercase_   : Optional[Any]   	=      AutoImageProcessor.from_pretrained(args.model				)
												elif args.preprocessor == "feature_extractor":
																		lowercase_   : Tuple   	=      AutoFeatureExtractor.from_pretrained(args.model				)
												elif args.preprocessor == "processor":
																		lowercase_   : Optional[int]   	=      AutoProcessor.from_pretrained(args.model				)
												else:
																		raise ValueError(f"Unknown preprocessor type '{args.preprocessor}'"				)

												# Ensure the requested opset is sufficient
												if args.opset is None:
																		lowercase_   : List[str]   	=      onnx_config.default_onnx_opset

												if args.opset < onnx_config.default_onnx_opset:
																		raise ValueError(
																		    f"Opset {args.opset} is not sufficient to export {model_kind}. "
																		    f"At least  {onnx_config.default_onnx_opset} is required."				)

												lowercase_	,   lowercase_   : str   	=      export(
												    a    ,      a    ,      a    ,      args.opset    ,      args.output    ,      )

												if args.atol is None:
																		lowercase_   : Dict   	=      onnx_config.atol_for_validation

												validate_model_outputs(a    ,      a    ,      a    ,      args.output    ,      a    ,      args.atol				)
												logger.info(f"All good, model saved at: {args.output.as_posix()}"				)
												warnings.warn(
												    'The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend'
												    ' using optimum.exporters.onnx in future. You can find more information here:'
												    ' https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'    ,      a    ,      )






def 						_UpperCAmelCase     (	)      ->      List[Any]:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      ArgumentParser('Hugging Face Transformers ONNX exporter'				)
						parser.add_argument(
						    '-m'    ,      '--model'    ,      type=a    ,      required=a    ,      help='Model ID on huggingface.co or path on disk to load model from.'				)
						parser.add_argument(
						    '--feature'    ,      default='default'    ,      help='The type of features to export the model with.'    ,      )
						parser.add_argument('--opset'    ,      type=a    ,      default=a    ,      help='ONNX opset version to export the model with.'				)
						parser.add_argument(
						    '--atol'    ,      type=a    ,      default=a    ,      help='Absolute difference tolerance when validating the model.'				)
						parser.add_argument(
						    '--framework'    ,      type=a    ,      choices=['pt', 'tf']    ,      default=a    ,      help=(
						        'The framework to use for the ONNX export.'
						        ' If not provided, will attempt to use the local checkpoint\'s original framework'
						        ' or what is available in the environment.'
						    )    ,      )
						parser.add_argument('output'    ,      type=a    ,      help='Path indicating where to store generated ONNX model.'				)
						parser.add_argument('--cache_dir'    ,      type=a    ,      default=a    ,      help='Path indicating where to store cache.'				)
						parser.add_argument(
						    '--preprocessor'    ,      type=a    ,      choices=['auto', 'tokenizer', 'feature_extractor', 'image_processor', 'processor']    ,      default='auto'    ,      help='Which type of preprocessor to use. \'auto\' tries to automatically detect it.'    ,      )
						parser.add_argument(
						    '--export_with_transformers'    ,      action='store_true'    ,      help=(
						        'Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be '
						        'useful when exporting a model supported in transformers but not in optimum, otherwise it is not '
						        'recommended.'
						    )    ,      )

						lowercase_   : int   	=      parser.parse_args()
						if args.export_with_transformers or not is_optimum_available():
												export_with_transformers(a				)
						else:
												export_with_optimum(a				)


if __name__ == "__main__":
						A:   Union[str, Any]						=					logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
						logger.setLevel(logging.INFO)
						main()
