



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 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Tuple ,			__lowercase						:						Union[str, Any]=sys.argv[1:]		):

											'''simple docstring'''
											__a					       =						None
											__a					       =						None
											__a					       =						None
											__a					       =						1
											__a       ,   __a					       =						getopt.getopt(__lowercase ,			"""i:o:b:s""" ,			["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]		)
											for opt, arg in opts:
															if opt in ("-i", "--inputdir"):
																			__a					       =						arg
															elif opt in ("-o", "--outfile"):
																			__a					       =						arg
															elif opt in ("-b", "--batch_size"):
																			__a					       =						int(__lowercase		)
															elif opt in ("-s", "--subset_list"):
																			__a					       =						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:
																			__a					       =						{self._vqa_file_split()[0] for x in tryload(__lowercase		)}
											else:
															__a					       =						None

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





							def   UpperCamelCase_   (				self						:						List[Any] ,			__lowercase						:						Union[str, Any]		):

											'''simple docstring'''
											__a					       =						int(file.split("""."""		)[0].split("""_"""		)[-1]		)
											__a					       =						os.path.join(self.inputdir ,			__lowercase		)
											return (img_id, filepath)





							@property
							def   UpperCamelCase_   (				self						:						str		):

											'''simple docstring'''
											__a					       =						[]
											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:
																			__a					       =						batch
																			__a					       =						[]
																			yield list(map(__lowercase ,			zip(*__lowercase		)		)		)

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







							def __call__(				self						:						List[Any]		):

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



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[Any]      ):
				"""simple docstring"""







				try:
								__a					       =						json.load(_SCREAMING_SNAKE_CASE      )
								try:
												__a					       =						list(data.keys()      )
								except Exception:
												__a					       =						[d["""img_id"""] for d in data]
				except Exception:
								try:
												__a					       =						eval(stream.read()      )
								except Exception:
												__a					       =						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)





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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict=None			,       _SCREAMING_SNAKE_CASE    :     List[str]=None      ):
				"""simple docstring"""







				return field(default_factory=lambda: default			,       metadata=_SCREAMING_SNAKE_CASE      )






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



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				try:
								float(_SCREAMING_SNAKE_CASE      )
								return True
				except ValueError:
								return False






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						args
											__a					       =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

											with open(self.args.csv_file ,			newline=""""""		) as csv_file:
															__a					       =						csv.DictReader(__lowercase		)
															for row in reader:
																			__a					       =						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
																							__a					       =						int(row["""result"""]		)
																			elif can_convert_to_float(row["""result"""]		):
																							# value is not None
																							__a					       =						float(row["""result"""]		)







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a       ,   __a					       =						plt.subplots()
											__a					       =						"""Time usage""" if self.args.is_time else """Memory usage"""
											__a					       =						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()		):
															__a					       =						sorted(set(self.result_dict[model_name]["""bsz"""]		)		)
															__a					       =						sorted(set(self.result_dict[model_name]["""seq_len"""]		)		)
															__a					       =						self.result_dict[model_name]["""result"""]

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

															__a					       =						(
															    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:
																							__a					       =						np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,			dtype=__lowercase ,			)
																			else:
																							__a					       =						np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,			dtype=np.floataa ,			)

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

																			__a					       =						np.asarray(__lowercase ,			__lowercase		)[: len(__lowercase		)]
																			plt.scatter(
																			    __lowercase ,			__lowercase ,			label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
																			plt.plot(__lowercase ,			__lowercase ,			"""--"""		)

															title_str += F" {label_model_name} vs."

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

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

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



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser(_SCREAMING_SNAKE_CASE      )
				__a					       =						parser.parse_args_into_dataclasses()[0]
				__a					       =						Plot(args=_SCREAMING_SNAKE_CASE      )
				plot.plot()


if __name__ == "__main__":
						main()
