




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_			(  lowerCamelCase:       Any=None     ,				lowerCamelCase:       str=None						)					->			List[Any]:


      '''simple docstring'''




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






@dataclass
class 			A			:
     """simple docstring"""

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

def 			lowerCAmelCase_			(  lowerCamelCase:       Union[str, Any]						)					->			int:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Dict						)					->			List[Any]:


      '''simple docstring'''




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






class 			A			:
     """simple docstring"""

     def __init__(    self  :   Optional[int]	,  A__  :   str			)				->       Optional[int]:
           lowerCamelCase__        =				args
           lowerCamelCase__        =				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

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




     def  __snake_case   (    self  :   Optional[int]			)				->       Optional[Any]:
           lowerCamelCase__        =				plt.subplots()
           lowerCamelCase__        =				'Time usage' if self.args.is_time else 'Memory usage'
           lowerCamelCase__        =				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()			):
                 lowerCamelCase__        =				sorted(set(self.result_dict[model_name]['bsz']			)			)
                 lowerCamelCase__        =				sorted(set(self.result_dict[model_name]['seq_len']			)			)
                 lowerCamelCase__        =				self.result_dict[model_name]['result']

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

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

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

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

                 title_str += F""" {label_model_name} vs."""

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

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

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

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				HfArgumentParser(lowerCAmelCase__						)
      lowerCamelCase__        =				parser.parse_args_into_dataclasses()[0]
      lowerCamelCase__        =				Plot(args=lowerCAmelCase__						)
      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
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =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_048), 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  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  '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(A__			)
                 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(A__			), F"""{outputfile}"""
           if subset_list is not None:
                 with open(os.path.realpath(A__			)			) as f:
                       lowerCamelCase__        =				{self._vqa_file_split()[0] for x in tryload(A__			)}
           else:
                 lowerCamelCase__        =				None

           lowerCamelCase__        =				CONFIG
           if torch.cuda.is_available():
                 lowerCamelCase__        =				'cuda'
           lowerCamelCase__        =				os.path.realpath(A__			)
           lowerCamelCase__        =				os.path.realpath(A__			)
           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  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[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(A__			)			)
                 if len(A__			) == self.batch:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

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




     def __call__(    self  :   List[Any]			)				->       Optional[Any]:
           # make writer
           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(A__			)
                 lowerCamelCase__        =				self.model(
                     A__	,  A__	,  scales_yx=A__	,  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(A__			)
                       lowerCamelCase__        =				self.schema.encode_batch(A__			)
                       writer.write_batch(A__			)
                 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 >> 2_0} mb"""			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__        =				json.load(lowerCamelCase						)
            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__":
      __SCREAMING_SNAKE_CASE    :   Tuple       =Extract(sys.argv[1:])
      extract()
      if not TEST:
            __SCREAMING_SNAKE_CASE    :   Any       =datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)