
import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
				import torch.distributed.checkpoint as dist_cp
				from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
				from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
				from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
				from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


snake_case :		Union[str, Any]							      =  get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  int      ,   _snake_case      :  Optional[int]=0   )				->		int:







	'''simple docstring'''


	os.makedirs(_snake_case      ,   exist_ok=_snake_case   )
	with FSDP.state_dict_type(
	    _snake_case      ,   fsdp_plugin.state_dict_type      ,   fsdp_plugin.state_dict_config      ,   fsdp_plugin.optim_state_dict_config   ):
		__magic_name__		:		Optional[Any]	   =     model.state_dict()
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			__magic_name__		:		Optional[Any]	   =     F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
			__magic_name__		:		List[Any]	   =     os.path.join(_snake_case      ,   _snake_case   )
			if accelerator.process_index == 0:
				logger.info(F'''Saving model to {output_model_file}'''   )
				torch.save(_snake_case      ,   _snake_case   )
				logger.info(F'''Model saved to {output_model_file}'''   )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			__magic_name__		:		Optional[int]	   =     (
			    F'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
			    if model_index == 0
			    else F'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
			)
			__magic_name__		:		Tuple	   =     os.path.join(_snake_case      ,   _snake_case   )
			logger.info(F'''Saving model to {output_model_file}'''   )
			torch.save(_snake_case      ,   _snake_case   )
			logger.info(F'''Model saved to {output_model_file}'''   )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			__magic_name__		:		Any	   =     os.path.join(_snake_case      ,   F'''{MODEL_NAME}_{model_index}'''   )
			os.makedirs(_snake_case      ,   exist_ok=_snake_case   )
			logger.info(F'''Saving model to {ckpt_dir}'''   )
			__magic_name__		:		Union[str, Any]	   =     {"model": state_dict}

			dist_cp.save_state_dict(
			    state_dict=_snake_case      ,   storage_writer=dist_cp.FileSystemWriter(_snake_case   )      ,   planner=DefaultSavePlanner()      ,   )
			logger.info(F'''Model saved to {ckpt_dir}'''   )






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  int      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]=0   )				->		int:







	'''simple docstring'''


	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    _snake_case      ,   fsdp_plugin.state_dict_type      ,   fsdp_plugin.state_dict_config      ,   fsdp_plugin.optim_state_dict_config   ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if type(_snake_case   ) != FSDP and accelerator.process_index != 0:
				if not fsdp_plugin.sync_module_states:
					raise ValueError(
					    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
					    "initializing FSDP object"   )
				return
			__magic_name__		:		int	   =     F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
			__magic_name__		:		List[Any]	   =     os.path.join(_snake_case      ,   _snake_case   )
			logger.info(F'''Loading model from {input_model_file}'''   )
			__magic_name__		:		Any	   =     torch.load(_snake_case   )
			logger.info(F'''Model loaded from {input_model_file}'''   )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			__magic_name__		:		str	   =     (
			    F'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
			    if model_index == 0
			    else F'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
			)
			__magic_name__		:		Optional[int]	   =     os.path.join(_snake_case      ,   _snake_case   )
			logger.info(F'''Loading model from {input_model_file}'''   )
			__magic_name__		:		int	   =     torch.load(_snake_case   )
			logger.info(F'''Model loaded from {input_model_file}'''   )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			__magic_name__		:		Any	   =     (
			    os.path.join(_snake_case      ,   F'''{MODEL_NAME}_{model_index}'''   )
			    if F'''{MODEL_NAME}''' not in input_dir
			    else input_dir
			)
			logger.info(F'''Loading model from {ckpt_dir}'''   )
			__magic_name__		:		int	   =     {"model": model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=_snake_case      ,   storage_reader=dist_cp.FileSystemReader(_snake_case   )      ,   planner=DefaultLoadPlanner()      ,   )
			__magic_name__		:		str	   =     state_dict["model"]
			logger.info(F'''Model loaded from {ckpt_dir}'''   )
		model.load_state_dict(_snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  List[str]      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Tuple=0   )				->		int:







	'''simple docstring'''


	os.makedirs(_snake_case      ,   exist_ok=_snake_case   )
	with FSDP.state_dict_type(
	    _snake_case      ,   fsdp_plugin.state_dict_type      ,   fsdp_plugin.state_dict_config      ,   fsdp_plugin.optim_state_dict_config   ):
		__magic_name__		:		Optional[int]	   =     FSDP.optim_state_dict(_snake_case      ,   _snake_case   )
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				__magic_name__		:		List[Any]	   =     (
				    F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
				)
				__magic_name__		:		Any	   =     os.path.join(_snake_case      ,   _snake_case   )
				logger.info(F'''Saving Optimizer state to {output_optimizer_file}'''   )
				torch.save(_snake_case      ,   _snake_case   )
				logger.info(F'''Optimizer state saved in {output_optimizer_file}'''   )
		else:
			__magic_name__		:		Optional[int]	   =     os.path.join(_snake_case      ,   F'''{OPTIMIZER_NAME}_{optimizer_index}'''   )
			os.makedirs(_snake_case      ,   exist_ok=_snake_case   )
			logger.info(F'''Saving Optimizer state to {ckpt_dir}'''   )
			dist_cp.save_state_dict(
			    state_dict={"optimizer": optim_state}      ,   storage_writer=dist_cp.FileSystemWriter(_snake_case   )      ,   planner=DefaultSavePlanner()      ,   )
			logger.info(F'''Optimizer state saved in {ckpt_dir}'''   )






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]=0   )				->		int:







	'''simple docstring'''


	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    _snake_case      ,   fsdp_plugin.state_dict_type      ,   fsdp_plugin.state_dict_config      ,   fsdp_plugin.optim_state_dict_config   ):
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			__magic_name__		:		Optional[Any]	   =     None
			# below check should work but currently it isn't working (mostly opytorch issue),
			# in the meantime disabling it at the cost of excess memory usage
			# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
			__magic_name__		:		Optional[int]	   =     (
			    F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
			)
			__magic_name__		:		str	   =     os.path.join(_snake_case      ,   _snake_case   )
			logger.info(F'''Loading Optimizer state from {input_optimizer_file}'''   )
			__magic_name__		:		Optional[int]	   =     torch.load(_snake_case   )
			logger.info(F'''Optimizer state loaded from {input_optimizer_file}'''   )
		else:
			__magic_name__		:		Dict	   =     (
			    os.path.join(_snake_case      ,   F'''{OPTIMIZER_NAME}_{optimizer_index}'''   )
			    if F'''{OPTIMIZER_NAME}''' not in input_dir
			    else input_dir
			)
			logger.info(F'''Loading Optimizer from {ckpt_dir}'''   )
			__magic_name__		:		List[str]	   =     load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()      ,   optimizer_key="optimizer"      ,   storage_reader=dist_cp.FileSystemReader(_snake_case   )      ,   )
			__magic_name__		:		Union[str, Any]	   =     optim_state["optimizer"]
			logger.info(F'''Optimizer loaded from {ckpt_dir}'''   )
		__magic_name__		:		Dict	   =     FSDP.optim_state_dict_to_load(_snake_case      ,   _snake_case      ,   _snake_case   )
		optimizer.load_state_dict(_snake_case   )


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()
