



"""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
snake_case_								=			False
snake_case_								=			Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
snake_case_								=			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  A_     :



						"""simple docstring"""






						def __init__(   self					:Tuple  ,    lowercase_					:Optional[int]=sys.argv[1:]   )							->     List[Any]:
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					1
												UpperCAmelCase ,       UpperCAmelCase			 =					getopt.getopt(lowercase_  ,    'i:o:b:s'  ,    ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']   )
												for opt, arg in opts:
																		if opt in ("-i", "--inputdir"):
																								UpperCAmelCase			 =					arg
																		elif opt in ("-o", "--outfile"):
																								UpperCAmelCase			 =					arg
																		elif opt in ("-b", "--batch_size"):
																								UpperCAmelCase			 =					int(lowercase_   )
																		elif opt in ("-s", "--subset_list"):
																								UpperCAmelCase			 =					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:
																								UpperCAmelCase			 =					{self._vqa_file_split()[0] for x in tryload(lowercase_   )}
												else:
																		UpperCAmelCase			 =					None

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






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[Any]   )							->     int:
												UpperCAmelCase			 =					int(file.split('.'   )[0].split('_'   )[-1]   )
												UpperCAmelCase			 =					os.path.join(self.inputdir  ,    lowercase_   )
												return (img_id, filepath)






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[str]:
												UpperCAmelCase			 =					[]
												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:
																								UpperCAmelCase			 =					batch
																								UpperCAmelCase			 =					[]
																								yield list(map(lowercase_  ,    zip(*lowercase_   )   )   )

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






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


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


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









import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers.activations_tf import get_tf_activation





@require_tf
class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]						)
       UpperCAmelCase_				:   List[Any]							= get_tf_activation('''gelu'''						)
       UpperCAmelCase_				:   Any							= get_tf_activation('''gelu_10'''						)

       UpperCAmelCase_				:   int							= gelu(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= geluaa(__snake_case						)

       UpperCAmelCase_				:   Union[str, Any]							= tf.where(y_gelu_aa < 10.0							, 1.0							, 0.0						)

       self.assertEqual(tf.math.reduce_max(__snake_case						).numpy().item()							, 10.0						)
       self.assertTrue(np.allclose(y_gelu * clipped_mask							, y_gelu_aa * clipped_mask						)						)





    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       get_tf_activation('''gelu'''						)
       get_tf_activation('''gelu_10'''						)
       get_tf_activation('''gelu_fast'''						)
       get_tf_activation('''gelu_new'''						)
       get_tf_activation('''glu'''						)
       get_tf_activation('''mish'''						)
       get_tf_activation('''quick_gelu'''						)
       get_tf_activation('''relu'''						)
       get_tf_activation('''sigmoid'''						)
       get_tf_activation('''silu'''						)
       get_tf_activation('''swish'''						)
       get_tf_activation('''tanh'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation('''bogus'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation(__snake_case						)