
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser




def     _a						(							snake_case__: Any=None						,       snake_case__: Tuple=None ):


				'''simple docstring'''
				return field(default_factory=lambda: default						,       metadata=__lowercase )
@dataclass
class 			lowercase__:



	"""simple docstring"""




	a      :List[Any]   					=     field(
	    metadata={'help': 'The csv file to plot.'}			,						)
	a      :List[Any]   					=     field(
	    default=lowercase_			,						metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}			,						)
	a      :Optional[int]   					=     field(
	    default=lowercase_			,						metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}			,						)
	a      :Union[str, Any]   					=     field(
	    default=lowercase_			,						metadata={'help': 'Disable logarithmic scale when plotting'}			,						)
	a      :Optional[int]   					=     field(
	    default=lowercase_			,						metadata={
	        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
	    }			,						)
	a      :Union[str, Any]   					=     field(
	    default=lowercase_			,						metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}			,						)
	a      :Any   					=     list_field(
	    default=lowercase_			,						metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}							)




def     _a						(							snake_case__: Union[str, Any] ):


				'''simple docstring'''

				try:
								int(__lowercase )
								return True
				except ValueError:
								return False




def     _a						(							snake_case__: Tuple ):


				'''simple docstring'''

				try:
								float(__lowercase )
								return True
				except ValueError:
								return False
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]	)	->					Tuple:
					lowercase_					  = args
					lowercase_					  = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

					with open(self.args.csv_file	,							newline=''''''	) as csv_file:
									lowercase_					  = csv.DictReader(UpperCamelCase_	)
									for row in reader:
													lowercase_					  = row['''model''']
													self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']	)	)
													self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']	)	)
													if can_convert_to_int(row['''result''']	):
																	# value is not None
																	lowercase_					  = int(row['''result''']	)
													elif can_convert_to_float(row['''result''']	):
																	# value is not None
																	lowercase_					  = float(row['''result''']	)




	def 						_lowercase  (	self  :      Optional[int]	)	->					Dict:
					lowercase_  ,     lowercase_					  = plt.subplots()
					lowercase_					  = '''Time usage''' if self.args.is_time else '''Memory usage'''
					lowercase_					  = title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

					if not self.args.no_log_scale:
									# set logarithm scales
									ax.set_xscale('''log'''	)
									ax.set_yscale('''log'''	)

					for axis in [ax.xaxis, ax.yaxis]:
									axis.set_major_formatter(ScalarFormatter()	)

					for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
									lowercase_					  = sorted(set(self.result_dict[model_name]['''bsz''']	)	)
									lowercase_					  = sorted(set(self.result_dict[model_name]['''seq_len''']	)	)
									lowercase_					  = self.result_dict[model_name]['''result''']

									((lowercase_)  ,     (lowercase_))					  = (
									    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
									)

									lowercase_					  = (
									    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
									)

									for inner_loop_value in inner_loop_array:
													if self.args.plot_along_batch:
																	lowercase_					  = np.asarray(
																	    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,							dtype=UpperCamelCase_	,							)
													else:
																	lowercase_					  = np.asarray(
																	    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,							dtype=np.floataa	,							)

													((lowercase_)  ,     (lowercase_))					  = (
													    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
													)

													lowercase_					  = np.asarray(UpperCamelCase_	,							UpperCamelCase_	)[: len(UpperCamelCase_	)]
													plt.scatter(
													    UpperCamelCase_	,							UpperCamelCase_	,							label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
													plt.plot(UpperCamelCase_	,							UpperCamelCase_	,							'''--'''	)

									title_str += f''' {label_model_name} vs.'''

					lowercase_					  = title_str[:-4]
					lowercase_					  = '''Time in s''' if self.args.is_time else '''Memory in MB'''

					# plot
					plt.title(UpperCamelCase_	)
					plt.xlabel(UpperCamelCase_	)
					plt.ylabel(UpperCamelCase_	)
					plt.legend()

					if self.args.figure_png_file is not None:
									plt.savefig(self.args.figure_png_file	)
					else:
									plt.show()




def     _a						(							):


				'''simple docstring'''
				lowercase_					  = HfArgumentParser(__lowercase )
				lowercase_					  = parser.parse_args_into_dataclasses()[0]
				lowercase_					  = Plot(args=__lowercase )
				plot.plot()


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)




