'''simple docstring'''

import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


UpperCamelCase__     :	Optional[int]				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				nn.ModuleList  ,			_A					:				nn.ModuleList  ,			_A					:				List[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  nn.ModuleList([src_layers[i] for i in layers_to_copy]    )
   assert len(_A    ) == len(_A    ), F'''{len(_A    )} != {len(_A    )}'''
   dest_layers.load_state_dict(layers_to_copy.state_dict()    )


UpperCamelCase__     :	Optional[int]				=			{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
UpperCamelCase__     :	str				=			{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}







def __UpperCamelCase(   _A					:				str  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   try:
      UpperCAmelCase__							:				Union[str, Any]   				=  LAYERS_TO_COPY[n_teacher][n_student]
      return val
   except KeyError:
      if n_student != n_teacher:
         warnings.warn(
             F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
             F''' {n_student}'''    )
      return list(range(_A    )    )







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   if n_student > n_teacher:
      raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'''    )
   elif n_teacher == n_student:
      return list(range(_A    )    )
   elif n_student == 1:
      return [n_teacher - 1]
   else:
      return LAYERS_TO_SUPERVISE[n_teacher][n_student]







def __UpperCamelCase(   _A					:				Union[str, PreTrainedModel]  ,			_A					:				Union[str, Path] = "student"  ,			_A					:				Union[int, None] = None  ,			_A					:				Union[int, None] = None  ,			_A					:				Any=False  ,			_A					:				Union[str, Any]=None  ,			_A					:				Any=None  ,			**_A					:				Any  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
   assert (e is not None) or (d is not None), _msg
   if isinstance(_A  ,			_A    ):
      AutoTokenizer.from_pretrained(_A    ).save_pretrained(_A    )  # purely for convenience
      UpperCAmelCase__							:				List[Any]   				=  AutoModelForSeqaSeqLM.from_pretrained(_A    ).eval()
   else:
      assert isinstance(_A  ,			_A    ), F'''teacher must be a model or string got type {type(_A    )}'''
   UpperCAmelCase__							:				Optional[Any]   				=  teacher.config.to_diff_dict()

   try:
      UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  teacher.config.encoder_layers, teacher.config.decoder_layers
      if e is None:
         UpperCAmelCase__							:				Union[str, Any]   				=  teacher_e
      if d is None:
         UpperCAmelCase__							:				Optional[int]   				=  teacher_d
      init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}    )
   except AttributeError:  # T5
      if hasattr(teacher.config  ,			'''num_encoder_layers'''    ):
         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[Any]   				=  teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
      else:
         UpperCAmelCase__				,      UpperCAmelCase__							:				Any   				=  teacher.config.num_layers, teacher.config.num_decoder_layers
      if e is None:
         UpperCAmelCase__							:				str   				=  teacher_e
      if d is None:
         UpperCAmelCase__							:				Tuple   				=  teacher_d
      if hasattr(teacher.config  ,			'''num_encoder_layers'''    ):
         init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}    )
      else:
         init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}    )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
   init_kwargs.update(_A    )

   # Copy weights
   UpperCAmelCase__							:				str   				=  teacher.config_class(**_A    )
   UpperCAmelCase__							:				Optional[Any]   				=  AutoModelForSeqaSeqLM.from_config(_A    )
   # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
   UpperCAmelCase__							:				int   				=  student.load_state_dict(teacher.state_dict()  ,			strict=_A    )
   assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

   if copy_first_teacher_layers:  # Our copying is done. We just log and save
      UpperCAmelCase__				,      UpperCAmelCase__							:				Union[str, Any]   				=  list(range(_A    )    ), list(range(_A    )    )
      logger.info(
          F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
          F''' {save_path}'''    )
      student.save_pretrained(_A    )
      return student, e_layers_to_copy, d_layers_to_copy

   # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
   if e_layers_to_copy is None:
      UpperCAmelCase__							:				List[int]   				=  pick_layers_to_copy(_A  ,			_A    )
   if d_layers_to_copy is None:
      UpperCAmelCase__							:				List[int]   				=  pick_layers_to_copy(_A  ,			_A    )

   try:
      if hasattr(
          _A  ,			'''prophetnet'''    ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
         copy_layers(teacher.prophetnet.encoder.layers  ,			student.prophetnet.encoder.layers  ,			_A    )
         copy_layers(teacher.prophetnet.decoder.layers  ,			student.prophetnet.decoder.layers  ,			_A    )
      else:
         copy_layers(teacher.model.encoder.layers  ,			student.model.encoder.layers  ,			_A    )
         copy_layers(teacher.model.decoder.layers  ,			student.model.decoder.layers  ,			_A    )
   except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
      copy_layers(teacher.encoder.block  ,			student.encoder.block  ,			_A    )
      copy_layers(teacher.decoder.block  ,			student.decoder.block  ,			_A    )
   logger.info(
       F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''    )
   UpperCAmelCase__							:				List[str]   				=  {
       '''teacher_type''': teacher.config.model_type,
       '''copied_encoder_layers''': e_layers_to_copy,
       '''copied_decoder_layers''': d_layers_to_copy,
   }
   student.save_pretrained(_A    )
   # Save information about copying for easier reproducibility

   return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
  fire.Fire(create_student_by_copying_alternating_layers)





'''simple docstring'''

import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


UpperCamelCase__     :	Union[str, Any]				=			re.compile(r'\b(a|an|the)\b', re.UNICODE)

UpperCamelCase__     :	List[Any]				=			None







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''    )
   parser.add_argument('''data_file'''  ,			metavar='''data.json'''  ,			help='''Input data JSON file.'''    )
   parser.add_argument('''pred_file'''  ,			metavar='''pred.json'''  ,			help='''Model predictions.'''    )
   parser.add_argument(
       '''--out-file'''  ,			'''-o'''  ,			metavar='''eval.json'''  ,			help='''Write accuracy metrics to file (default is stdout).'''    )
   parser.add_argument(
       '''--na-prob-file'''  ,			'''-n'''  ,			metavar='''na_prob.json'''  ,			help='''Model estimates of probability of no answer.'''    )
   parser.add_argument(
       '''--na-prob-thresh'''  ,			'''-t'''  ,			type=_A  ,			default=1.0  ,			help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''  ,			)
   parser.add_argument(
       '''--out-image-dir'''  ,			'''-p'''  ,			metavar='''out_images'''  ,			default=_A  ,			help='''Save precision-recall curves to directory.'''    )
   parser.add_argument('''--verbose'''  ,			'''-v'''  ,			action='''store_true'''    )
   if len(sys.argv    ) == 1:
      parser.print_help()
      sys.exit(1    )
   return parser.parse_args()







def __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				Union[str, Any]   				=  bool(qa['''answers''']['''text''']    )
   return qid_to_has_ans







def __UpperCamelCase(   _A					:				Dict    ):
   '''simple docstring'''

   def remove_articles(_A					:				Union[str, Any]    ):
      return ARTICLES_REGEX.sub(''' '''  ,			_A    )

   def white_space_fix(_A					:				Optional[int]    ):
      return " ".join(text.split()    )

   def remove_punc(_A					:				Optional[Any]    ):
      UpperCAmelCase__							:				Any   				=  set(string.punctuation    )
      return "".join(ch for ch in text if ch not in exclude    )

   def lower(_A					:				Union[str, Any]    ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   if not s:
      return []
   return normalize_answer(_A    ).split()







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str    ):
   '''simple docstring'''
   return int(normalize_answer(_A    ) == normalize_answer(_A    )    )







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Tuple   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Any   				=  collections.Counter(_A    ) & collections.Counter(_A    )
   UpperCAmelCase__							:				List[Any]   				=  sum(common.values()    )
   if len(_A    ) == 0 or len(_A    ) == 0:
      # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
      return int(gold_toks == pred_toks    )
   if num_same == 0:
      return 0
   UpperCAmelCase__							:				Optional[Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Tuple   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  (2 * precision * recall) / (precision + recall)
   return fa







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  {}
   UpperCAmelCase__							:				Any   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				str   				=  qa['''id''']
            UpperCAmelCase__							:				List[Any]   				=  [t for t in qa['''answers''']['''text'''] if normalize_answer(_A    )]
            if not gold_answers:
               # For unanswerable questions, only correct answer is empty string
               UpperCAmelCase__							:				Tuple   				=  ['''''']
            if qid not in preds:
               print(F'''Missing prediction for {qid}'''    )
               continue
            UpperCAmelCase__							:				Union[str, Any]   				=  preds[qid]
            # Take max over all gold answers
            UpperCAmelCase__							:				List[str]   				=  max(compute_exact(_A  ,			_A    ) for a in gold_answers    )
            UpperCAmelCase__							:				List[str]   				=  max(compute_fa(_A  ,			_A    ) for a in gold_answers    )
   return exact_scores, fa_scores







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[str]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  {}
   for qid, s in scores.items():
      UpperCAmelCase__							:				Dict   				=  na_probs[qid] > na_prob_thresh
      if pred_na:
         UpperCAmelCase__							:				Any   				=  float(not qid_to_has_ans[qid]    )
      else:
         UpperCAmelCase__							:				List[str]   				=  s
   return new_scores







def __UpperCamelCase(   _A					:				str  ,			_A					:				Optional[Any]  ,			_A					:				Any=None    ):
   '''simple docstring'''
   if not qid_list:
      UpperCAmelCase__							:				List[Any]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_0_0.0 * sum(exact_scores.values()    ) / total),
              ('''f1''', 1_0_0.0 * sum(fa_scores.values()    ) / total),
              ('''total''', total),
          ]    )
   else:
      UpperCAmelCase__							:				List[str]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_0_0.0 * sum(exact_scores[k] for k in qid_list    ) / total),
              ('''f1''', 1_0_0.0 * sum(fa_scores[k] for k in qid_list    ) / total),
              ('''total''', total),
          ]    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				List[Any]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   for k in new_eval:
      UpperCAmelCase__							:				List[str]   				=  new_eval[k]







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Any  ,			_A					:				Optional[int]  ,			_A					:				int    ):
   '''simple docstring'''
   plt.step(_A  ,			_A  ,			color='''b'''  ,			alpha=0.2  ,			where='''post'''    )
   plt.fill_between(_A  ,			_A  ,			step='''post'''  ,			alpha=0.2  ,			color='''b'''    )
   plt.xlabel('''Recall'''    )
   plt.ylabel('''Precision'''    )
   plt.xlim([0.0, 1.0_5]    )
   plt.ylim([0.0, 1.0_5]    )
   plt.title(_A    )
   plt.savefig(_A    )
   plt.clf()







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Tuple  ,			_A					:				Any  ,			_A					:				Any  ,			_A					:				Tuple=None  ,			_A					:				Union[str, Any]=None    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   UpperCAmelCase__							:				Tuple   				=  0.0
   UpperCAmelCase__							:				Any   				=  1.0
   UpperCAmelCase__							:				Any   				=  0.0
   UpperCAmelCase__							:				Union[str, Any]   				=  [1.0]
   UpperCAmelCase__							:				int   				=  [0.0]
   UpperCAmelCase__							:				Optional[Any]   				=  0.0
   for i, qid in enumerate(_A    ):
      if qid_to_has_ans[qid]:
         true_pos += scores[qid]
      UpperCAmelCase__							:				Optional[Any]   				=  true_pos / float(i + 1    )
      UpperCAmelCase__							:				int   				=  true_pos / float(_A    )
      if i == len(_A    ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
         # i.e., if we can put a threshold after this point
         avg_prec += cur_p * (cur_r - recalls[-1])
         precisions.append(_A    )
         recalls.append(_A    )
   if out_image:
      plot_pr_curve(_A  ,			_A  ,			_A  ,			_A    )
   return {"ap": 1_0_0.0 * avg_prec}







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Any  ,			_A					:				Dict  ,			_A					:				Any    ):
   '''simple docstring'''
   if out_image_dir and not os.path.exists(_A    ):
      os.makedirs(_A    )
   UpperCAmelCase__							:				List[str]   				=  sum(1 for v in qid_to_has_ans.values() if v    )
   if num_true_pos == 0:
      return
   UpperCAmelCase__							:				Dict   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_exact.png'''    )  ,			title='''Precision-Recall curve for Exact Match score'''  ,			)
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_f1.png'''    )  ,			title='''Precision-Recall curve for F1 score'''  ,			)
   UpperCAmelCase__							:				Tuple   				=  {k: float(_A    ) for k, v in qid_to_has_ans.items()}
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_oracle.png'''    )  ,			title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''  ,			)
   merge_eval(_A  ,			_A  ,			'''pr_exact'''    )
   merge_eval(_A  ,			_A  ,			'''pr_f1'''    )
   merge_eval(_A  ,			_A  ,			'''pr_oracle'''    )







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Dict  ,			_A					:				Dict  ,			_A					:				Tuple    ):
   '''simple docstring'''
   if not qid_list:
      return
   UpperCAmelCase__							:				Optional[Any]   				=  [na_probs[k] for k in qid_list]
   UpperCAmelCase__							:				Union[str, Any]   				=  np.ones_like(_A    ) / float(len(_A    )    )
   plt.hist(_A  ,			weights=_A  ,			bins=20  ,			range=(0.0, 1.0)    )
   plt.xlabel('''Model probability of no-answer'''    )
   plt.ylabel('''Proportion of dataset'''    )
   plt.title(F'''Histogram of no-answer probability: {name}'''    )
   plt.savefig(os.path.join(_A  ,			F'''na_prob_hist_{name}.png'''    )    )
   plt.clf()







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				List[str]  ,			_A					:				Optional[int]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]    )
   UpperCAmelCase__							:				List[str]   				=  num_no_ans
   UpperCAmelCase__							:				Any   				=  cur_score
   UpperCAmelCase__							:				List[str]   				=  0.0
   UpperCAmelCase__							:				Dict   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   for i, qid in enumerate(_A    ):
      if qid not in scores:
         continue
      if qid_to_has_ans[qid]:
         UpperCAmelCase__							:				int   				=  scores[qid]
      else:
         if preds[qid]:
            UpperCAmelCase__							:				Any   				=  -1
         else:
            UpperCAmelCase__							:				Dict   				=  0
      cur_score += diff
      if cur_score > best_score:
         UpperCAmelCase__							:				Optional[Any]   				=  cur_score
         UpperCAmelCase__							:				Tuple   				=  na_probs[qid]
   return 1_0_0.0 * best_score / len(_A    ), best_thresh







def __UpperCamelCase(   _A					:				str  ,			_A					:				str  ,			_A					:				int  ,			_A					:				int  ,			_A					:				Tuple  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				List[str]   				=  best_exact
   UpperCAmelCase__							:				Any   				=  exact_thresh
   UpperCAmelCase__							:				Dict   				=  best_fa
   UpperCAmelCase__							:				Dict   				=  fa_thresh







def __UpperCamelCase(   ):
   '''simple docstring'''
   with open(OPTS.data_file    ) as f:
      UpperCAmelCase__							:				Dict   				=  json.load(_A    )
      UpperCAmelCase__							:				str   				=  dataset_json['''data''']
   with open(OPTS.pred_file    ) as f:
      UpperCAmelCase__							:				Any   				=  json.load(_A    )
   if OPTS.na_prob_file:
      with open(OPTS.na_prob_file    ) as f:
         UpperCAmelCase__							:				Optional[Any]   				=  json.load(_A    )
   else:
      UpperCAmelCase__							:				Dict   				=  {k: 0.0 for k in preds}
   UpperCAmelCase__							:				int   				=  make_qid_to_has_ans(_A    )  # maps qid to True/False
   UpperCAmelCase__							:				Any   				=  [k for k, v in qid_to_has_ans.items() if v]
   UpperCAmelCase__							:				Optional[Any]   				=  [k for k, v in qid_to_has_ans.items() if not v]
   UpperCAmelCase__				,      UpperCAmelCase__							:				Union[str, Any]   				=  get_raw_scores(_A  ,			_A    )
   UpperCAmelCase__							:				Optional[int]   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Dict   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Union[str, Any]   				=  make_eval_dict(_A  ,			_A    )
   if has_ans_qids:
      UpperCAmelCase__							:				Optional[int]   				=  make_eval_dict(_A  ,			_A  ,			qid_list=_A    )
      merge_eval(_A  ,			_A  ,			'''HasAns'''    )
   if no_ans_qids:
      UpperCAmelCase__							:				Dict   				=  make_eval_dict(_A  ,			_A  ,			qid_list=_A    )
      merge_eval(_A  ,			_A  ,			'''NoAns'''    )
   if OPTS.na_prob_file:
      find_all_best_thresh(_A  ,			_A  ,			_A  ,			_A  ,			_A  ,			_A    )
   if OPTS.na_prob_file and OPTS.out_image_dir:
      run_precision_recall_analysis(_A  ,			_A  ,			_A  ,			_A  ,			_A  ,			OPTS.out_image_dir    )
      histogram_na_prob(_A  ,			_A  ,			OPTS.out_image_dir  ,			'''hasAns'''    )
      histogram_na_prob(_A  ,			_A  ,			OPTS.out_image_dir  ,			'''noAns'''    )
   if OPTS.out_file:
      with open(OPTS.out_file  ,			'''w'''    ) as f:
         json.dump(_A  ,			_A    )
   else:
      print(json.dumps(_A  ,			indent=2    )    )


if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=			parse_args()
  if OPTS.out_image_dir:
    import matplotlib

    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
  main()




