
import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case :		Optional[int]							      =  logging.getLogger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Union[str, Any]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     np.argmax(_snake_case      ,   axis=1   )
	return np.sum(outputs == labels   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]   )				->		Dict:







	'''simple docstring'''


	with open(_snake_case      ,   encoding="utf_8"   ) as f:
		__magic_name__		:		List[str]	   =     csv.reader(_snake_case   )
		__magic_name__		:		List[Any]	   =     []
		next(_snake_case   )  # skip the first line
		for line in tqdm(_snake_case   ):
			output.append((" ".join(line[1:5]   ), line[5], line[6], int(line[-1]   ) - 1)   )
	return output






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Tuple      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Tuple      ,   _snake_case      :  Optional[int]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     []
	for dataset in encoded_datasets:
		__magic_name__		:		Union[str, Any]	   =     len(_snake_case   )
		__magic_name__		:		Dict	   =     np.zeros((n_batch, 2, input_len)      ,   dtype=np.intaa   )
		__magic_name__		:		List[str]	   =     np.zeros((n_batch, 2)      ,   dtype=np.intaa   )
		__magic_name__		:		Optional[int]	   =     np.full((n_batch, 2, input_len)      ,   fill_value=-100      ,   dtype=np.intaa   )
		__magic_name__		:		int	   =     np.zeros((n_batch,)      ,   dtype=np.intaa   )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(_snake_case   ):
			__magic_name__		:		Dict	   =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__magic_name__		:		Dict	   =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__magic_name__		:		str	   =     with_conta
			__magic_name__		:		Tuple	   =     with_conta
			__magic_name__		:		Union[str, Any]	   =     len(_snake_case   ) - 1
			__magic_name__		:		int	   =     len(_snake_case   ) - 1
			__magic_name__		:		Optional[Any]	   =     with_conta
			__magic_name__		:		Optional[Any]	   =     with_conta
			__magic_name__		:		Optional[int]	   =     mc_label
		__magic_name__		:		str	   =     (input_ids, mc_token_ids, lm_labels, mc_labels)
		tensor_datasets.append(tuple(torch.tensor(_snake_case   ) for t in all_inputs   )   )
	return tensor_datasets






def   lowerCAmelCase_						(					)				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     argparse.ArgumentParser()
	parser.add_argument("--model_name"      ,   type=_snake_case      ,   default="openai-gpt"      ,   help="pretrained model name"   )
	parser.add_argument("--do_train"      ,   action="store_true"      ,   help="Whether to run training."   )
	parser.add_argument("--do_eval"      ,   action="store_true"      ,   help="Whether to run eval on the dev set."   )
	parser.add_argument(
	    "--output_dir"      ,   default=_snake_case      ,   type=_snake_case      ,   required=_snake_case      ,   help="The output directory where the model predictions and checkpoints will be written."      ,   )
	parser.add_argument("--train_dataset"      ,   type=_snake_case      ,   default=""   )
	parser.add_argument("--eval_dataset"      ,   type=_snake_case      ,   default=""   )
	parser.add_argument("--seed"      ,   type=_snake_case      ,   default=42   )
	parser.add_argument("--num_train_epochs"      ,   type=_snake_case      ,   default=3   )
	parser.add_argument("--train_batch_size"      ,   type=_snake_case      ,   default=8   )
	parser.add_argument("--eval_batch_size"      ,   type=_snake_case      ,   default=16   )
	parser.add_argument("--adam_epsilon"      ,   default=1E-8      ,   type=_snake_case      ,   help="Epsilon for Adam optimizer."   )
	parser.add_argument("--max_grad_norm"      ,   type=_snake_case      ,   default=1   )
	parser.add_argument(
	    "--max_steps"      ,   default=-1      ,   type=_snake_case      ,   help=(
	        "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
	    )      ,   )
	parser.add_argument(
	    "--gradient_accumulation_steps"      ,   type=_snake_case      ,   default=1      ,   help="Number of updates steps to accumulate before                        performing a backward/update pass."      ,   )
	parser.add_argument("--learning_rate"      ,   type=_snake_case      ,   default=6.25E-5   )
	parser.add_argument("--warmup_steps"      ,   default=0      ,   type=_snake_case      ,   help="Linear warmup over warmup_steps."   )
	parser.add_argument("--lr_schedule"      ,   type=_snake_case      ,   default="warmup_linear"   )
	parser.add_argument("--weight_decay"      ,   type=_snake_case      ,   default=0.01   )
	parser.add_argument("--lm_coef"      ,   type=_snake_case      ,   default=0.9   )
	parser.add_argument("--n_valid"      ,   type=_snake_case      ,   default=374   )

	parser.add_argument("--server_ip"      ,   type=_snake_case      ,   default=""      ,   help="Can be used for distant debugging."   )
	parser.add_argument("--server_port"      ,   type=_snake_case      ,   default=""      ,   help="Can be used for distant debugging."   )
	__magic_name__		:		List[Any]	   =     parser.parse_args()
	print(_snake_case   )

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print("Waiting for debugger attach"   )
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,   redirect_output=_snake_case   )
		ptvsd.wait_for_attach()

	random.seed(args.seed   )
	np.random.seed(args.seed   )
	torch.manual_seed(args.seed   )
	torch.cuda.manual_seed_all(args.seed   )

	__magic_name__		:		Dict	   =     torch.device("cuda" if torch.cuda.is_available() else "cpu"   )
	__magic_name__		:		Optional[int]	   =     torch.cuda.device_count()
	logger.info("device: {}, n_gpu {}".format(_snake_case      ,   _snake_case   )   )

	if not args.do_train and not args.do_eval:
		raise ValueError("At least one of `do_train` or `do_eval` must be True."   )

	if not os.path.exists(args.output_dir   ):
		os.makedirs(args.output_dir   )

	# Load tokenizer and model
	# This loading functions also add new tokens and embeddings called `special tokens`
	# These new embeddings will be fine-tuned on the RocStories dataset
	__magic_name__		:		List[Any]	   =     ["_start_", "_delimiter_", "_classify_"]
	__magic_name__		:		Optional[int]	   =     OpenAIGPTTokenizer.from_pretrained(args.model_name   )
	tokenizer.add_tokens(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     tokenizer.convert_tokens_to_ids(_snake_case   )
	__magic_name__		:		List[str]	   =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
	model.resize_token_embeddings(len(_snake_case   )   )
	model.to(_snake_case   )

	# Load and encode the datasets
	def tokenize_and_encode(_snake_case      :  str   ):
		if isinstance(_snake_case      ,   _snake_case   ):
			return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case   )   )
		elif isinstance(_snake_case      ,   _snake_case   ):
			return obj
		return [tokenize_and_encode(_snake_case   ) for o in obj]

	logger.info("Encoding dataset..."   )
	__magic_name__		:		Optional[int]	   =     load_rocstories_dataset(args.train_dataset   )
	__magic_name__		:		str	   =     load_rocstories_dataset(args.eval_dataset   )
	__magic_name__		:		int	   =     (train_dataset, eval_dataset)
	__magic_name__		:		List[str]	   =     tokenize_and_encode(_snake_case   )

	# Compute the max input length for the Transformer
	__magic_name__		:		Optional[Any]	   =     model.config.n_positions // 2 - 2
	__magic_name__		:		Optional[int]	   =     max(
	    len(story[:max_length]   ) + max(len(conta[:max_length]   )      ,   len(conta[:max_length]   )   ) + 3
	    for dataset in encoded_datasets
	    for story, conta, conta, _ in dataset   )
	__magic_name__		:		List[str]	   =     min(_snake_case      ,   model.config.n_positions   )  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	__magic_name__		:		List[Any]	   =     pre_process_datasets(_snake_case      ,   _snake_case      ,   _snake_case      ,   *_snake_case   )
	__magic_name__   ,    __magic_name__		:		Optional[int]	   =     tensor_datasets[0], tensor_datasets[1]

	__magic_name__		:		Tuple	   =     TensorDataset(*_snake_case   )
	__magic_name__		:		Union[str, Any]	   =     RandomSampler(_snake_case   )
	__magic_name__		:		Dict	   =     DataLoader(_snake_case      ,   sampler=_snake_case      ,   batch_size=args.train_batch_size   )

	__magic_name__		:		Any	   =     TensorDataset(*_snake_case   )
	__magic_name__		:		Optional[Any]	   =     SequentialSampler(_snake_case   )
	__magic_name__		:		int	   =     DataLoader(_snake_case      ,   sampler=_snake_case      ,   batch_size=args.eval_batch_size   )

	# Prepare optimizer
	if args.do_train:
		if args.max_steps > 0:
			__magic_name__		:		Tuple	   =     args.max_steps
			__magic_name__		:		List[str]	   =     args.max_steps // (len(_snake_case   ) // args.gradient_accumulation_steps) + 1
		else:
			__magic_name__		:		List[str]	   =     len(_snake_case   ) // args.gradient_accumulation_steps * args.num_train_epochs

		__magic_name__		:		str	   =     list(model.named_parameters()   )
		__magic_name__		:		Dict	   =     ["bias", "LayerNorm.bias", "LayerNorm.weight"]
		__magic_name__		:		str	   =     [
		    {
		        "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay   )],
		        "weight_decay": args.weight_decay,
		    },
		    {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay   )], "weight_decay": 0.0},
		]
		__magic_name__		:		str	   =     AdamW(_snake_case      ,   lr=args.learning_rate      ,   eps=args.adam_epsilon   )
		__magic_name__		:		List[str]	   =     get_linear_schedule_with_warmup(
		    _snake_case      ,   num_warmup_steps=args.warmup_steps      ,   num_training_steps=_snake_case   )

	if args.do_train:
		__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs   )      ,   desc="Epoch"   ):
			__magic_name__		:		List[str]	   =     0
			__magic_name__		:		Tuple	   =     0
			__magic_name__		:		Dict	   =     tqdm(_snake_case      ,   desc="Training"   )
			for step, batch in enumerate(_snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tuple(t.to(_snake_case   ) for t in batch   )
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Dict	   =     batch
				__magic_name__		:		Optional[Any]	   =     model(_snake_case      ,   mc_token_ids=_snake_case      ,   lm_labels=_snake_case      ,   mc_labels=_snake_case   )
				__magic_name__		:		Optional[Any]	   =     args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				__magic_name__		:		List[str]	   =     (
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				__magic_name__		:		int	   =     "Training loss: {:.2e} lr: {:.2e}".format(_snake_case      ,   scheduler.get_lr()[0]   )

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		__magic_name__		:		Dict	   =     model.module if hasattr(_snake_case      ,   "module"   ) else model  # Only save the model itself

		# If we save using the predefined names, we can load using `from_pretrained`
		__magic_name__		:		List[Any]	   =     os.path.join(args.output_dir      ,   _snake_case   )
		__magic_name__		:		Dict	   =     os.path.join(args.output_dir      ,   _snake_case   )

		torch.save(model_to_save.state_dict()      ,   _snake_case   )
		model_to_save.config.to_json_file(_snake_case   )
		tokenizer.save_vocabulary(args.output_dir   )

		# Load a trained model and vocabulary that you have fine-tuned
		__magic_name__		:		Dict	   =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir   )
		__magic_name__		:		Optional[int]	   =     OpenAIGPTTokenizer.from_pretrained(args.output_dir   )
		model.to(_snake_case   )

	if args.do_eval:
		model.eval()
		__magic_name__   ,    __magic_name__		:		Any	   =     0, 0
		__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     0, 0
		for batch in tqdm(_snake_case      ,   desc="Evaluating"   ):
			__magic_name__		:		int	   =     tuple(t.to(_snake_case   ) for t in batch   )
			__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     batch
			with torch.no_grad():
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Dict	   =     model(
				    _snake_case      ,   mc_token_ids=_snake_case      ,   lm_labels=_snake_case      ,   mc_labels=_snake_case   )

			__magic_name__		:		Tuple	   =     mc_logits.detach().cpu().numpy()
			__magic_name__		:		Any	   =     mc_labels.to("cpu"   ).numpy()
			__magic_name__		:		str	   =     accuracy(_snake_case      ,   _snake_case   )

			eval_loss += mc_loss.mean().item()
			eval_accuracy += tmp_eval_accuracy

			nb_eval_examples += input_ids.size(0   )
			nb_eval_steps += 1

		__magic_name__		:		Tuple	   =     eval_loss / nb_eval_steps
		__magic_name__		:		List[Any]	   =     eval_accuracy / nb_eval_examples
		__magic_name__		:		int	   =     tr_loss / nb_tr_steps if args.do_train else None
		__magic_name__		:		Any	   =     {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

		__magic_name__		:		int	   =     os.path.join(args.output_dir      ,   "eval_results.txt"   )
		with open(_snake_case      ,   "w"   ) as writer:
			logger.info("***** Eval results *****"   )
			for key in sorted(result.keys()   ):
				logger.info("  %s = %s"      ,   _snake_case      ,   str(result[key]   )   )
				writer.write("%s = %s\n" % (key, str(result[key]   ))   )


if __name__ == "__main__":
				main()


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

import numpy as np


snake_case :		Dict							      =  re.compile(R"\b(a|an|the)\b", re.UNICODE)

snake_case :		Optional[int]							      =  None






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







	'''simple docstring'''


	__magic_name__		:		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=_snake_case      ,   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=_snake_case      ,   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   lowerCAmelCase_						(					_snake_case      :  Optional[Any]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     {}
	for article in dataset:
		for p in article["paragraphs"]:
			for qa in p["qas"]:
				__magic_name__		:		str	   =     bool(qa["answers"]["text"]   )
	return qid_to_has_ans






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]   )				->		Optional[Any]:







	'''simple docstring'''


	def remove_articles(_snake_case      :  List[str]   ):
		return ARTICLES_REGEX.sub(" "      ,   _snake_case   )

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

	def remove_punc(_snake_case      :  Optional[int]   ):
		__magic_name__		:		Dict	   =     set(string.punctuation   )
		return "".join(ch for ch in text if ch not in exclude   )

	def lower(_snake_case      :  str   ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(_snake_case   )   )   )   )






def   lowerCAmelCase_						(					_snake_case      :  Any   )				->		Optional[Any]:







	'''simple docstring'''


	if not s:
		return []
	return normalize_answer(_snake_case   ).split()






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Dict   )				->		Tuple:







	'''simple docstring'''


	return int(normalize_answer(_snake_case   ) == normalize_answer(_snake_case   )   )






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  int   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Any	   =     get_tokens(_snake_case   )
	__magic_name__		:		Optional[int]	   =     get_tokens(_snake_case   )
	__magic_name__		:		Tuple	   =     collections.Counter(_snake_case   ) & collections.Counter(_snake_case   )
	__magic_name__		:		Tuple	   =     sum(common.values()   )
	if len(_snake_case   ) == 0 or len(_snake_case   ) == 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
	__magic_name__		:		Dict	   =     1.0 * num_same / len(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     1.0 * num_same / len(_snake_case   )
	__magic_name__		:		List[Any]	   =     (2 * precision * recall) / (precision + recall)
	return fa






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  Dict   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     {}
	for qid, s in scores.items():
		__magic_name__		:		Dict	   =     na_probs[qid] > na_prob_thresh
		if pred_na:
			__magic_name__		:		str	   =     float(not qid_to_has_ans[qid]   )
		else:
			__magic_name__		:		Optional[int]	   =     s
	return new_scores






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple=None   )				->		Tuple:







	'''simple docstring'''


	if not qid_list:
		__magic_name__		:		Any	   =     len(_snake_case   )
		return collections.OrderedDict(
		    [
		        ("exact", 100.0 * sum(exact_scores.values()   ) / total),
		        ("f1", 100.0 * sum(fa_scores.values()   ) / total),
		        ("total", total),
		    ]   )
	else:
		__magic_name__		:		Tuple	   =     len(_snake_case   )
		return collections.OrderedDict(
		    [
		        ("exact", 100.0 * sum(exact_scores[k] for k in qid_list   ) / total),
		        ("f1", 100.0 * sum(fa_scores[k] for k in qid_list   ) / total),
		        ("total", total),
		    ]   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  str      ,   _snake_case      :  str   )				->		Dict:







	'''simple docstring'''


	for k in new_eval:
		__magic_name__		:		int	   =     new_eval[k]






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Union[str, Any]   )				->		str:







	'''simple docstring'''


	plt.step(_snake_case      ,   _snake_case      ,   color="b"      ,   alpha=0.2      ,   where="post"   )
	plt.fill_between(_snake_case      ,   _snake_case      ,   step="post"      ,   alpha=0.2      ,   color="b"   )
	plt.xlabel("Recall"   )
	plt.ylabel("Precision"   )
	plt.xlim([0.0, 1.05]   )
	plt.ylim([0.0, 1.05]   )
	plt.title(_snake_case   )
	plt.savefig(_snake_case   )
	plt.clf()






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Any      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[int]=None      ,   _snake_case      :  int=None   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     sorted(_snake_case      ,   key=lambda _snake_case   : na_probs[k]   )
	__magic_name__		:		Optional[int]	   =     0.0
	__magic_name__		:		str	   =     1.0
	__magic_name__		:		str	   =     0.0
	__magic_name__		:		List[str]	   =     [1.0]
	__magic_name__		:		str	   =     [0.0]
	__magic_name__		:		Optional[Any]	   =     0.0
	for i, qid in enumerate(_snake_case   ):
		if qid_to_has_ans[qid]:
			true_pos += scores[qid]
		__magic_name__		:		List[str]	   =     true_pos / float(i + 1   )
		__magic_name__		:		Any	   =     true_pos / float(_snake_case   )
		if i == len(_snake_case   ) - 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(_snake_case   )
			recalls.append(_snake_case   )
	if out_image:
		plot_pr_curve(_snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )
	return {"ap": 100.0 * avg_prec}






def   lowerCAmelCase_						(					_snake_case      :  Tuple      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Any      ,   _snake_case      :  List[Any]   )				->		Union[str, Any]:







	'''simple docstring'''


	if out_image_dir and not os.path.exists(_snake_case   ):
		os.makedirs(_snake_case   )
	__magic_name__		:		Any	   =     sum(1 for v in qid_to_has_ans.values() if v   )
	if num_true_pos == 0:
		return
	__magic_name__		:		str	   =     make_precision_recall_eval(
	    _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   out_image=os.path.join(_snake_case      ,   "pr_exact.png"   )      ,   title="Precision-Recall curve for Exact Match score"      ,   )
	__magic_name__		:		Union[str, Any]	   =     make_precision_recall_eval(
	    _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   out_image=os.path.join(_snake_case      ,   "pr_f1.png"   )      ,   title="Precision-Recall curve for F1 score"      ,   )
	__magic_name__		:		str	   =     {k: float(_snake_case   ) for k, v in qid_to_has_ans.items()}
	__magic_name__		:		str	   =     make_precision_recall_eval(
	    _snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case      ,   out_image=os.path.join(_snake_case      ,   "pr_oracle.png"   )      ,   title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"      ,   )
	merge_eval(_snake_case      ,   _snake_case      ,   "pr_exact"   )
	merge_eval(_snake_case      ,   _snake_case      ,   "pr_f1"   )
	merge_eval(_snake_case      ,   _snake_case      ,   "pr_oracle"   )






def   lowerCAmelCase_						(					_snake_case      :  int      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  List[str]      ,   _snake_case      :  Optional[Any]   )				->		Dict:







	'''simple docstring'''


	if not qid_list:
		return
	__magic_name__		:		Dict	   =     [na_probs[k] for k in qid_list]
	__magic_name__		:		str	   =     np.ones_like(_snake_case   ) / float(len(_snake_case   )   )
	plt.hist(_snake_case      ,   weights=_snake_case      ,   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(_snake_case      ,   F'''na_prob_hist_{name}.png'''   )   )
	plt.clf()






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[str]      ,   _snake_case      :  Dict   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]   )
	__magic_name__		:		List[str]	   =     num_no_ans
	__magic_name__		:		Dict	   =     cur_score
	__magic_name__		:		Dict	   =     0.0
	__magic_name__		:		Any	   =     sorted(_snake_case      ,   key=lambda _snake_case   : na_probs[k]   )
	for i, qid in enumerate(_snake_case   ):
		if qid not in scores:
			continue
		if qid_to_has_ans[qid]:
			__magic_name__		:		Union[str, Any]	   =     scores[qid]
		else:
			if preds[qid]:
				__magic_name__		:		List[Any]	   =     -1
			else:
				__magic_name__		:		Optional[int]	   =     0
		cur_score += diff
		if cur_score > best_score:
			__magic_name__		:		Optional[int]	   =     cur_score
			__magic_name__		:		List[Any]	   =     na_probs[qid]
	return 100.0 * best_score / len(_snake_case   ), best_thresh






def   lowerCAmelCase_						(					_snake_case      :  int      ,   _snake_case      :  str      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Dict   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__   ,    __magic_name__		:		List[str]	   =     find_best_thresh(_snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )
	__magic_name__   ,    __magic_name__		:		int	   =     find_best_thresh(_snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )
	__magic_name__		:		Optional[int]	   =     best_exact
	__magic_name__		:		List[Any]	   =     exact_thresh
	__magic_name__		:		Dict	   =     best_fa
	__magic_name__		:		Any	   =     fa_thresh






def   lowerCAmelCase_						(					)				->		int:







	'''simple docstring'''


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


if __name__ == "__main__":
				snake_case :		int							      =  parse_args()
				if OPTS.out_image_dir:
								import matplotlib

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