

"""simple docstring"""

import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings("""ignore""")

_UpperCamelCase		    =						"""K-MEANS-CLUST/ """



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__=None      ) ->	int:
 if seed is not None:  # useful for obtaining consistent results
  np.random.seed(lowercase__      )
 lowerCAmelCase__      :							Optional[int]    =							data.shape[0]  # number of data points

 # Pick K indices from range [0, N).
 lowerCAmelCase__      :							str    =							np.random.randint(0				,				lowercase__				,				lowercase__      )

 # Keep centroids as dense format, as many entries will be nonzero due to averaging.
 # As long as at least one document in a cluster contains a word,
 # it will carry a nonzero weight in the TF-IDF vector of the centroid.
 lowerCAmelCase__      :							int    =							data[rand_indices, :]

 return centroids



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	List[Any]:
 return pairwise_distances(lowercase__				,				lowercase__				,				metric="euclidean"      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	Union[str, Any]:
 # Compute distances between each data point and the set of centroids:
 # Fill in the blank (RHS only)
 lowerCAmelCase__      :							Union[str, Any]    =							centroid_pairwise_dist(lowercase__				,				lowercase__      )

 # Compute cluster assignments for each data point:
 # Fill in the blank (RHS only)
 lowerCAmelCase__      :							List[Any]    =							np.argmin(lowercase__				,				axis=1      )

 return cluster_assignment



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	Union[str, Any]:
 lowerCAmelCase__      :							List[str]    =							[]
 for i in range(lowercase__      ):
  # Select all data points that belong to cluster i. Fill in the blank (RHS only)
  lowerCAmelCase__      :							Any    =							data[cluster_assignment == i]
  # Compute the mean of the data points. Fill in the blank (RHS only)
  lowerCAmelCase__      :							str    =							member_data_points.mean(axis=0      )
  new_centroids.append(lowercase__      )
 lowerCAmelCase__      :							Optional[Any]    =							np.array(lowercase__      )

 return new_centroids



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							List[Any]    =							0.0
 for i in range(lowercase__      ):
  # Select all data points that belong to cluster i. Fill in the blank (RHS only)
  lowerCAmelCase__      :							List[Any]    =							data[cluster_assignment == i, :]

  if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
   # Compute distances from centroid to data points (RHS only)
   lowerCAmelCase__      :							Any    =							pairwise_distances(
       lowercase__				,				[centroids[i]]				,				metric="euclidean"      )
   lowerCAmelCase__      :							int    =							distances**2
   heterogeneity += np.sum(lowercase__      )

 return heterogeneity



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	Dict:
 plt.figure(figsize=(7, 4)      )
 plt.plot(lowercase__				,				linewidth=4      )
 plt.xlabel("# Iterations"      )
 plt.ylabel("Heterogeneity"      )
 plt.title(F"""Heterogeneity of clustering over time, K={k:d}"""      )
 plt.rcParams.update({"font.size": 1_6}      )
 plt.show()



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__=5_0_0				,				lowercase__=None				,				lowercase__=False      ) ->	List[Any]:
 lowerCAmelCase__      :							Any    =							initial_centroids[:]
 lowerCAmelCase__      :							Optional[Any]    =							None

 for itr in range(lowercase__      ):
  if verbose:
   print(lowercase__				,				end=""      )

  # 1. Make cluster assignments using nearest centroids
  lowerCAmelCase__      :							int    =							assign_clusters(lowercase__				,				lowercase__      )

  # 2. Compute a new centroid for each of the k clusters, averaging all data
  #    points assigned to that cluster.
  lowerCAmelCase__      :							List[Any]    =							revise_centroids(lowercase__				,				lowercase__				,				lowercase__      )

  # Check for convergence: if none of the assignments changed, stop
  if (
      prev_cluster_assignment is not None
      and (prev_cluster_assignment == cluster_assignment).all()
  ):
   break

  # Print number of new assignments
  if prev_cluster_assignment is not None:
   lowerCAmelCase__      :							Any    =							np.sum(prev_cluster_assignment != cluster_assignment      )
   if verbose:
    print(
        F"""    {num_changed:5d} elements changed their cluster assignment."""      )

        # Record heterogeneity convergence metric
  if record_heterogeneity is not None:
   # YOUR CODE HERE
   lowerCAmelCase__      :							Union[str, Any]    =							compute_heterogeneity(lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      )
   record_heterogeneity.append(lowercase__      )

  lowerCAmelCase__      :							Union[str, Any]    =							cluster_assignment[:]

 return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
    from sklearn import datasets as ds

    _UpperCamelCase		    =						ds.load_iris()
    _UpperCamelCase		    =						3
    _UpperCamelCase		    =						[]
    _UpperCamelCase		    =						get_initial_centroids(dataset["""data"""], k, seed=0)
    _UpperCamelCase    ,     _UpperCamelCase		    =						kmeans(
        dataset["""data"""],
        k,
        initial_centroids,
        maxiter=4_0_0,
        record_heterogeneity=heterogeneity,
        verbose=True,
    )
    plot_heterogeneity(heterogeneity, k)



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__=None      ) ->	pd.DataFrame:
 # Fill missing values with given rules
 if fill_missing_report:
  lowerCAmelCase__      :							Tuple    =							df.fillna(value=lowercase__      )
 lowerCAmelCase__      :							int    =							1
 lowerCAmelCase__      :							List[Any]    =							df.select_dtypes(np.number      ).columns
 lowerCAmelCase__      :							List[str]    =							(
     df.groupby(["Cluster"]      )[  # construct report dataframe
         numeric_cols
     ]  # group by cluster number
     .agg(
         [
             ("sum", np.sum),
             ("mean_with_zeros", lambda lowercase__      : np.mean(np.nan_to_num(lowercase__      )      )),
             ("mean_without_zeros", lambda lowercase__      : x.replace(0				,				np.NaN      ).mean()),
             (
                 "mean_25-75",
                 lambda lowercase__      : np.mean(
                     np.nan_to_num(
                         sorted(lowercase__      )[
                             round(len(lowercase__      ) * 2_5 / 1_0_0      ) : round(len(lowercase__      ) * 7_5 / 1_0_0      )
                         ]      )      ),
             ),
             ("mean_with_na", np.mean),
             ("min", lambda lowercase__      : x.min()),
             ("5%", lambda lowercase__      : x.quantile(0.05      )),
             ("25%", lambda lowercase__      : x.quantile(0.25      )),
             ("50%", lambda lowercase__      : x.quantile(0.50      )),
             ("75%", lambda lowercase__      : x.quantile(0.75      )),
             ("95%", lambda lowercase__      : x.quantile(0.95      )),
             ("max", lambda lowercase__      : x.max()),
             ("count", lambda lowercase__      : x.count()),
             ("stdev", lambda lowercase__      : x.std()),
             ("mode", lambda lowercase__      : x.mode()[0]),
             ("median", lambda lowercase__      : x.median()),
             ("# > 0", lambda lowercase__      : (x > 0).sum()),
         ]      )
     .T.reset_index()
     .rename(index=lowercase__				,				columns={"level_0": "Features", "level_1": "Type"}      )
 )  # rename columns
 # calculate the size of cluster(count of clientID's)
 lowerCAmelCase__      :							str    =							report[
     (report["Features"] == "dummy") & (report["Type"] == "count")
 ].copy()  # avoid SettingWithCopyWarning
 lowerCAmelCase__      :							Tuple    =							(
     "ClusterSize"  # rename created cluster df to match report column names
 )
 lowerCAmelCase__      :							int    =							"# of Customers"
 lowerCAmelCase__      :							Dict    =							pd.DataFrame(
     clustersize.iloc[:, 2:].values
     / clustersize.iloc[:, 2:].values.sum()      )
 lowerCAmelCase__      :							int    =							"% of Customers"  # rename created cluster df to match report column names
 lowerCAmelCase__      :							Any    =							"ClusterProportion"
 lowerCAmelCase__      :							Optional[int]    =							clusterproportion.columns.tolist()
 lowerCAmelCase__      :							Optional[int]    =							cols[-2:] + cols[:-2]
 lowerCAmelCase__      :							Optional[int]    =							clusterproportion[cols]  # rearrange columns to match report
 lowerCAmelCase__      :							Optional[int]    =							report.columns
 lowerCAmelCase__      :							Dict    =							pd.DataFrame(
     abs(
         report[report["Type"] == "count"].iloc[:, 2:].values
         - clustersize.iloc[:, 2:].values      )      )  # generating df with count of nan values
 lowerCAmelCase__      :							List[Any]    =							0
 lowerCAmelCase__      :							List[Any]    =							"# of nan"
 lowerCAmelCase__      :							str    =							report[
     report["Type"] == "count"
 ].Features.tolist()  # filling values in order to match report
 lowerCAmelCase__      :							Optional[int]    =							a.columns.tolist()
 lowerCAmelCase__      :							Dict    =							cols[-2:] + cols[:-2]
 lowerCAmelCase__      :							str    =							a[cols]  # rearrange columns to match report
 lowerCAmelCase__      :							Any    =							report.columns  # rename columns to match report
 lowerCAmelCase__      :							Dict    =							report.drop(
     report[report.Type == "count"].index      )  # drop count values except cluster size
 lowerCAmelCase__      :							str    =							pd.concat(
     [report, a, clustersize, clusterproportion]				,				axis=0      )  # concat report with clustert size and nan values
 lowerCAmelCase__      :							List[str]    =							report["Features"].isin(lowercase__      )
 lowerCAmelCase__      :							str    =							report.columns.tolist()
 lowerCAmelCase__      :							str    =							cols[0:2] + cols[-1:] + cols[2:-1]
 lowerCAmelCase__      :							List[str]    =							report[cols]
 lowerCAmelCase__      :							Tuple    =							{
     "ClusterSize": 9,
     "ClusterProportion": 8,
     "mean_with_zeros": 7,
     "mean_with_na": 6,
     "max": 5,
     "50%": 4,
     "min": 3,
     "25%": 2,
     "75%": 1,
     "# of nan": 0,
     "# > 0": -1,
     "sum_with_na": -2,
 }
 lowerCAmelCase__      :							Dict    =							(
     report.assign(
         Sortera=lambda lowercase__      : x.Type.map(lowercase__      )				,				Sortera=lambda lowercase__      : list(reversed(range(len(lowercase__      )      )      )      )				,				)
     .sort_values(["Sorter1", "Mark", "Sorter2"]				,				ascending=lowercase__      )
     .drop(["Sorter1", "Sorter2"]				,				axis=1      )
 )
 lowerCAmelCase__      :							int    =							""
 lowerCAmelCase__      :							List[Any]    =							report.reset_index()
 lowerCAmelCase__      :							Union[str, Any]    =							report.drop(columns=["index"]      )
 return report


if __name__ == "__main__":
    import doctest

    doctest.testmod()









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





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							Optional[Any]    =							arg

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

        lowerCAmelCase__      :							Optional[int]    =							CONFIG
        if torch.cuda.is_available():
         lowerCAmelCase__      :							Union[str, Any]    =							"cuda"
        lowerCAmelCase__      :							Dict    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Tuple    =							os.path.realpath(snake_case      )
        lowerCAmelCase__      :							Any    =							Preprocess(self.config      )
        lowerCAmelCase__      :							str    =							GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned" ,				config=self.config      )
        lowerCAmelCase__      :							int    =							batch_size if batch_size != 0 else 1
        lowerCAmelCase__      :							Optional[int]    =							DEFAULT_SCHEMA






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							[]
        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(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

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






       def __call__(      self      ):





        """simple docstring"""

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




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							stream.read().split("\n"      )
 return data


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






