






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
lowercase__      :    List[str]          =     False
lowercase__      :    Tuple          =     Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
lowercase__      :    str          =     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		,      __SCREAMING_SNAKE_CASE=sys.argv[1:]    )						->Dict:
								lowerCAmelCase     		=		None
								lowerCAmelCase     		=		None
								lowerCAmelCase     		=		None
								lowerCAmelCase     		=		1
								lowerCAmelCase		,				lowerCAmelCase     		=		getopt.getopt(__lowerCAmelCase		,      '''i:o:b:s'''		,      ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']    )
								for opt, arg in opts:
															if opt in ("-i", "--inputdir"):
																						lowerCAmelCase     		=		arg
															elif opt in ("-o", "--outfile"):
																						lowerCAmelCase     		=		arg
															elif opt in ("-b", "--batch_size"):
																						lowerCAmelCase     		=		int(__lowerCAmelCase    )
															elif opt in ("-s", "--subset_list"):
																						lowerCAmelCase     		=		arg

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

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


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[Any]:
								lowerCAmelCase     		=		int(file.split('''.'''    )[0].split('''_'''    )[-1]    )
								lowerCAmelCase     		=		os.path.join(self.inputdir		,      __lowerCAmelCase    )
								return (img_id, filepath)


	@property
	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[Any]:
								lowerCAmelCase     		=		[]
								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(__lowerCAmelCase    )    )
															if len(__lowerCAmelCase    ) == self.batch:
																						lowerCAmelCase     		=		batch
																						lowerCAmelCase     		=		[]
																						yield list(map(__lowerCAmelCase		,      zip(*__lowerCAmelCase    )    )    )

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




	def __call__(							self    )						->List[Any]:


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

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     List[str]:


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


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




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Union[str, Any]:
								return cls




	@slow
	@require_torch_gpu
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.get_auto_remove_tmp_dir()
								lowerCAmelCase     		=		F"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								lowerCAmelCase     		=		get_gpu_count()
								lowerCAmelCase     		=		F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
								lowerCAmelCase     		=		[sys.executable] + distributed_args + args
								execute_subprocess_async(__SCREAMING_SNAKE_CASE		,      env=self.get_env()    )

								lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      '''test_bleu.json'''    )
								lowerCAmelCase     		=		load_json(__SCREAMING_SNAKE_CASE    )
								# print(metrics)
								self.assertGreaterEqual(metrics['''bleu''']		,      25    )



