'''simple docstring'''

import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
  from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


UpperCamelCase__     :	Any				=     logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__     :	List[Any]				=     256


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[str]      							=   ['''melgan''']




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,)			->			None:



         '''simple docstring'''



         super().__init__()

         # From MELGAN
         UpperCAmelCase__							:				int   				=  math.log(1e-5							)  # Matches MelGAN training.
         UpperCAmelCase__							:				Tuple   				=  4.0  # Largest value for most examples
         UpperCAmelCase__							:				List[Any]   				=  128

         self.register_modules(
             notes_encoder=_SCREAMING_SNAKE_CASE					,continuous_encoder=_SCREAMING_SNAKE_CASE					,decoder=_SCREAMING_SNAKE_CASE					,scheduler=_SCREAMING_SNAKE_CASE					,melgan=_SCREAMING_SNAKE_CASE					,)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=(-1.0, 1.0)					,lowerCamelCase_=False							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				Any   				=  output_range
         if clip:
            UpperCAmelCase__							:				Union[str, Any]   				=  torch.clip(_SCREAMING_SNAKE_CASE					,self.min_value					,self.max_value							)
         # Scale to [0, 1].
         UpperCAmelCase__							:				List[str]   				=  (features - self.min_value) / (self.max_value - self.min_value)
         # Scale to [min_out, max_out].
         return zero_one * (max_out - min_out) + min_out




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=(-1.0, 1.0)					,lowerCamelCase_=False							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  input_range
         UpperCAmelCase__							:				Union[str, Any]   				=  torch.clip(_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE							) if clip else outputs
         # Scale to [0, 1].
         UpperCAmelCase__							:				Any   				=  (outputs - min_out) / (max_out - min_out)
         # Scale to [self.min_value, self.max_value].
         return zero_one * (self.max_value - self.min_value) + self.min_value




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  input_tokens > 0
         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[Any]   				=  self.notes_encoder(
             encoder_input_tokens=_SCREAMING_SNAKE_CASE					,encoder_inputs_mask=_SCREAMING_SNAKE_CASE							)

         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  self.continuous_encoder(
             encoder_inputs=_SCREAMING_SNAKE_CASE					,encoder_inputs_mask=_SCREAMING_SNAKE_CASE							)

         return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  noise_time
         if not torch.is_tensor(_SCREAMING_SNAKE_CASE							):
            UpperCAmelCase__							:				Dict   				=  torch.tensor([timesteps]					,dtype=torch.long					,device=input_tokens.device							)
         elif torch.is_tensor(_SCREAMING_SNAKE_CASE							) and len(timesteps.shape							) == 0:
            UpperCAmelCase__							:				List[Any]   				=  timesteps[None].to(input_tokens.device							)

         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         UpperCAmelCase__							:				List[str]   				=  timesteps * torch.ones(input_tokens.shape[0]					,dtype=timesteps.dtype					,device=timesteps.device							)

         UpperCAmelCase__							:				str   				=  self.decoder(
             encodings_and_masks=_SCREAMING_SNAKE_CASE					,decoder_input_tokens=_SCREAMING_SNAKE_CASE					,decoder_noise_time=_SCREAMING_SNAKE_CASE							)
         return logits




      @torch.no_grad()
      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = 100					,lowerCamelCase_ = True					,lowerCamelCase_ = "numpy"					,lowerCamelCase_ = None					,lowerCamelCase_ = 1					,)			->			Union[AudioPipelineOutput, Tuple]:



         '''simple docstring'''



         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE							) or callback_steps <= 0)
         ):
            raise ValueError(
                f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                f''' {type(_SCREAMING_SNAKE_CASE							)}.'''							)

         UpperCAmelCase__							:				Optional[Any]   				=  np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,dtype=np.floataa							)
         UpperCAmelCase__							:				Any   				=  np.zeros([1, 0, self.n_dims]					,np.floataa							)
         UpperCAmelCase__							:				Union[str, Any]   				=  torch.ones((1, TARGET_FEATURE_LENGTH)					,dtype=_SCREAMING_SNAKE_CASE					,device=self.device							)

         for i, encoder_input_tokens in enumerate(_SCREAMING_SNAKE_CASE							):
            if i == 0:
               UpperCAmelCase__							:				List[Any]   				=  torch.from_numpy(pred_mel[:1].copy()							).to(
                   device=self.device					,dtype=self.decoder.dtype							)
               # The first chunk has no previous context.
               UpperCAmelCase__							:				str   				=  torch.zeros((1, TARGET_FEATURE_LENGTH)					,dtype=_SCREAMING_SNAKE_CASE					,device=self.device							)
            else:
               # The full song pipeline does not feed in a context feature, so the mask
               # will be all 0s after the feature converter. Because we know we're
               # feeding in a full context chunk from the previous prediction, set it
               # to all 1s.
               UpperCAmelCase__							:				Dict   				=  ones

            UpperCAmelCase__							:				Union[str, Any]   				=  self.scale_features(
                _SCREAMING_SNAKE_CASE					,output_range=[-1.0, 1.0]					,clip=_SCREAMING_SNAKE_CASE							)

            UpperCAmelCase__							:				int   				=  self.encode(
                input_tokens=torch.IntTensor([encoder_input_tokens]							).to(device=self.device							)					,continuous_inputs=_SCREAMING_SNAKE_CASE					,continuous_mask=_SCREAMING_SNAKE_CASE					,)

            # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
            UpperCAmelCase__							:				List[Any]   				=  randn_tensor(
                shape=encoder_continuous_inputs.shape					,generator=_SCREAMING_SNAKE_CASE					,device=self.device					,dtype=self.decoder.dtype					,)

            # set step values
            self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE							)

            # Denoising diffusion loop
            for j, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
               UpperCAmelCase__							:				Union[str, Any]   				=  self.decode(
                   encodings_and_masks=_SCREAMING_SNAKE_CASE					,input_tokens=_SCREAMING_SNAKE_CASE					,noise_time=t / self.scheduler.config.num_train_timesteps					,)

               # Compute previous output: x_t -> x_t-1
               UpperCAmelCase__							:				Tuple   				=  self.scheduler.step(_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,generator=_SCREAMING_SNAKE_CASE							).prev_sample

            UpperCAmelCase__							:				Tuple   				=  self.scale_to_features(_SCREAMING_SNAKE_CASE					,input_range=[-1.0, 1.0]							)
            UpperCAmelCase__							:				int   				=  mel[:1]
            UpperCAmelCase__							:				Tuple   				=  mel.cpu().float().numpy()

            UpperCAmelCase__							:				Tuple   				=  np.concatenate([full_pred_mel, pred_mel[:1]]					,axis=1							)

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
               callback(_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE							)

            logger.info('''Generated segment'''					,_SCREAMING_SNAKE_CASE							)

         if output_type == "numpy" and not is_onnx_available():
            raise ValueError(
                '''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'''							)
         elif output_type == "numpy" and self.melgan is None:
            raise ValueError(
                '''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'''							)

         if output_type == "numpy":
            UpperCAmelCase__							:				List[str]   				=  self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
         else:
            UpperCAmelCase__							:				Any   				=  full_pred_mel

         if not return_dict:
            return (output,)

         return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE							)





'''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()




