import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_lowerCamelCase   :				int     			=						logging.getLogger(__name__)


@dataclass
class   lowercase      :
							lowercase__  :    str									=							field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}       )
							lowercase__  :    Optional[str]									=							field(
							    default=a			,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}       )
							lowercase__  :    Optional[str]									=							field(
							    default=a			,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}       )
							lowercase__  :    Optional[str]									=							field(
							    default=a			,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,    )
							lowercase__  :    bool									=							field(default=a			,    metadata={"""help""": """Whether tp freeze the encoder."""}       )
							lowercase__  :    bool									=							field(default=a			,    metadata={"""help""": """Whether  to freeze the embeddings."""}       )



@dataclass
class   lowercase      :
							lowercase__  :    str									=							field(
							    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}       )
							lowercase__  :    Optional[str]									=							field(
							    default="""summarization"""			,    metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}			,    )
							lowercase__  :    Optional[int]									=							field(
							    default=1_024			,    metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			,    )
							lowercase__  :    Optional[int]									=							field(
							    default=128			,    metadata={
							        """help""": (
							            """The maximum total sequence length for target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			,    )
							lowercase__  :    Optional[int]									=							field(
							    default=142			,    metadata={
							        """help""": (
							            """The maximum total sequence length for validation target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded. """
							            """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
							            """during ``evaluate`` and ``predict``."""
							        )
							    }			,    )
							lowercase__  :    Optional[int]									=							field(
							    default=142			,    metadata={
							        """help""": (
							            """The maximum total sequence length for test target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			,    )
							lowercase__  :    Optional[int]									=							field(default=-1			,    metadata={"""help""": """# training examples. -1 means use all."""}       )
							lowercase__  :    Optional[int]									=							field(default=-1			,    metadata={"""help""": """# validation examples. -1 means use all."""}       )
							lowercase__  :    Optional[int]									=							field(default=-1			,    metadata={"""help""": """# test examples. -1 means use all."""}       )
							lowercase__  :    Optional[str]									=							field(default=a			,    metadata={"""help""": """Source language id for translation."""}       )
							lowercase__  :    Optional[str]									=							field(default=a			,    metadata={"""help""": """Target language id for translation."""}       )
							lowercase__  :    Optional[int]									=							field(default=a			,    metadata={"""help""": """# num_beams to use for evaluation."""}       )
							lowercase__  :    bool									=							field(
							    default=a			,    metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}			,    )





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							str					):
					logger.info(F"***** {split} metrics *****"					)
					for key in sorted(metrics.keys()					):
										logger.info(F"  {key} = {metrics[key]}"					)
					save_json(UpperCAmelCase__ ,	os.path.join(UpperCAmelCase__ ,	F"{split}_results.json"					)					)





def 		__lowerCamelCase							():
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					SCREAMING_SNAKE_CASE           =    HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

					if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
					else:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    parser.parse_args_into_dataclasses()

					check_output_dir(UpperCAmelCase__					)

					# Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,	datefmt="%m/%d/%Y %H:%M:%S" ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,	)
					logger.warning(
					    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" ,	training_args.local_rank ,	training_args.device ,	training_args.n_gpu ,	bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					) ,	training_args.fpaa ,	)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank					):
										transformers.utils.logging.set_verbosity_info()
					logger.info("Training/evaluation parameters %s" ,	UpperCAmelCase__					)

					# Set seed
					set_seed(training_args.seed					)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					SCREAMING_SNAKE_CASE           =    AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)

					SCREAMING_SNAKE_CASE           =    ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
					for p in extra_model_params:
										if getattr(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					):
															assert hasattr(UpperCAmelCase__ ,	UpperCAmelCase__					), F"({config.__class__.__name__}) doesn't have a `{p}` attribute"
															setattr(UpperCAmelCase__ ,	UpperCAmelCase__ ,	getattr(UpperCAmelCase__ ,	UpperCAmelCase__					)					)

					SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)
					SCREAMING_SNAKE_CASE           =    AutoModelForSeqaSeqLM.from_pretrained(
					    model_args.model_name_or_path ,	from_tf=".ckpt" in model_args.model_name_or_path ,	config=UpperCAmelCase__ ,	cache_dir=model_args.cache_dir ,	)

					# use task specific params
					use_task_specific_params(UpperCAmelCase__ ,	data_args.task					)

					# set num_beams for evaluation
					if data_args.eval_beams is None:
										SCREAMING_SNAKE_CASE           =    model.config.num_beams

					# set decoder_start_token_id for MBart
					if model.config.decoder_start_token_id is None and isinstance(UpperCAmelCase__ ,	(MBartTokenizer, MBartTokenizerFast)					):
										assert (
										    data_args.tgt_lang is not None and data_args.src_lang is not None
										), "mBart requires --tgt_lang and --src_lang"
										if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    tokenizer.lang_code_to_id[data_args.tgt_lang]
										else:
															SCREAMING_SNAKE_CASE           =    tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

					if model_args.freeze_embeds:
										freeze_embeds(UpperCAmelCase__					)
					if model_args.freeze_encoder:
										freeze_params(model.get_encoder()					)
										assert_all_frozen(model.get_encoder()					)

					SCREAMING_SNAKE_CASE           =    SeqaSeqDataset

					# Get datasets
					SCREAMING_SNAKE_CASE           =    (
					    dataset_class(
					        UpperCAmelCase__ ,	type_path="train" ,	data_dir=data_args.data_dir ,	n_obs=data_args.n_train ,	max_target_length=data_args.max_target_length ,	max_source_length=data_args.max_source_length ,	prefix=model.config.prefix or "" ,	)
					    if training_args.do_train
					    else None
					)
					SCREAMING_SNAKE_CASE           =    (
					    dataset_class(
					        UpperCAmelCase__ ,	type_path="val" ,	data_dir=data_args.data_dir ,	n_obs=data_args.n_val ,	max_target_length=data_args.val_max_target_length ,	max_source_length=data_args.max_source_length ,	prefix=model.config.prefix or "" ,	)
					    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
					    else None
					)
					SCREAMING_SNAKE_CASE           =    (
					    dataset_class(
					        UpperCAmelCase__ ,	type_path="test" ,	data_dir=data_args.data_dir ,	n_obs=data_args.n_test ,	max_target_length=data_args.test_max_target_length ,	max_source_length=data_args.max_source_length ,	prefix=model.config.prefix or "" ,	)
					    if training_args.do_predict
					    else None
					)

					# Initialize our Trainer
					SCREAMING_SNAKE_CASE           =    (
					    build_compute_metrics_fn(data_args.task ,	UpperCAmelCase__					) if training_args.predict_with_generate else None
					)
					SCREAMING_SNAKE_CASE           =    SeqaSeqTrainer(
					    model=UpperCAmelCase__ ,	args=UpperCAmelCase__ ,	data_args=UpperCAmelCase__ ,	train_dataset=UpperCAmelCase__ ,	eval_dataset=UpperCAmelCase__ ,	data_collator=SeqaSeqDataCollator(
					        UpperCAmelCase__ ,	UpperCAmelCase__ ,	model.config.decoder_start_token_id ,	training_args.tpu_num_cores					) ,	compute_metrics=UpperCAmelCase__ ,	tokenizer=UpperCAmelCase__ ,	)

					SCREAMING_SNAKE_CASE           =    {}
					# Training
					if training_args.do_train:
										logger.info("*** Train ***"					)

										SCREAMING_SNAKE_CASE           =    trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
										SCREAMING_SNAKE_CASE           =    train_result.metrics
										SCREAMING_SNAKE_CASE           =    data_args.n_train

										trainer.save_model()  # this also saves the tokenizer

										if trainer.is_world_process_zero():
															handle_metrics("train" ,	UpperCAmelCase__ ,	training_args.output_dir					)
															all_metrics.update(UpperCAmelCase__					)

															# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
															trainer.state.save_to_json(os.path.join(training_args.output_dir ,	"trainer_state.json"					)					)

															# For convenience, we also re-save the tokenizer to the same directory,
															# so that you can share your model easily on huggingface.co/models =)
															tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
					if training_args.do_eval:
										logger.info("*** Evaluate ***"					)

										SCREAMING_SNAKE_CASE           =    trainer.evaluate(metric_key_prefix="val"					)
										SCREAMING_SNAKE_CASE           =    data_args.n_val
										SCREAMING_SNAKE_CASE           =    round(metrics["val_loss"] ,	4					)

										if trainer.is_world_process_zero():
															handle_metrics("val" ,	UpperCAmelCase__ ,	training_args.output_dir					)
															all_metrics.update(UpperCAmelCase__					)

					if training_args.do_predict:
										logger.info("*** Predict ***"					)

										SCREAMING_SNAKE_CASE           =    trainer.predict(test_dataset=UpperCAmelCase__ ,	metric_key_prefix="test"					)
										SCREAMING_SNAKE_CASE           =    test_output.metrics
										SCREAMING_SNAKE_CASE           =    data_args.n_test

										if trainer.is_world_process_zero():
															SCREAMING_SNAKE_CASE           =    round(metrics["test_loss"] ,	4					)
															handle_metrics("test" ,	UpperCAmelCase__ ,	training_args.output_dir					)
															all_metrics.update(UpperCAmelCase__					)

															if training_args.predict_with_generate:
																				SCREAMING_SNAKE_CASE           =    tokenizer.batch_decode(
																				    test_output.predictions ,	skip_special_tokens=UpperCAmelCase__ ,	clean_up_tokenization_spaces=UpperCAmelCase__					)
																				SCREAMING_SNAKE_CASE           =    lmap(str.strip ,	UpperCAmelCase__					)
																				write_txt_file(UpperCAmelCase__ ,	os.path.join(training_args.output_dir ,	"test_generations.txt"					)					)

					if trainer.is_world_process_zero():
										save_json(UpperCAmelCase__ ,	os.path.join(training_args.output_dir ,	"all_results.json"					)					)

					return all_metrics





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any]					):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
							main()





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

import numpy as np


_lowerCamelCase   :				Dict     			=						re.compile(r'''\b(a|an|the)\b''', re.UNICODE)

_lowerCamelCase   :				Optional[int]     			=						None





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    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=UpperCAmelCase__ ,	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=UpperCAmelCase__ ,	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							(UpperCAmelCase__       :							Optional[int]					):
					SCREAMING_SNAKE_CASE           =    {}
					for article in dataset:
										for p in article["paragraphs"]:
															for qa in p["qas"]:
																				SCREAMING_SNAKE_CASE           =    bool(qa["answers"]["text"]					)
					return qid_to_has_ans





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any]					):

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

					def white_space_fix(UpperCAmelCase__       :							Dict					):
										return " ".join(text.split()					)

					def remove_punc(UpperCAmelCase__       :							str					):
										SCREAMING_SNAKE_CASE           =    set(string.punctuation					)
										return "".join(ch for ch in text if ch not in exclude					)

					def lower(UpperCAmelCase__       :							Dict					):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__					)					)					)					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					if not s:
										return []
					return normalize_answer(UpperCAmelCase__					).split()





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							List[str]					):
					return int(normalize_answer(UpperCAmelCase__					) == normalize_answer(UpperCAmelCase__					)					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    get_tokens(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    get_tokens(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    collections.Counter(UpperCAmelCase__					) & collections.Counter(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    sum(common.values()					)
					if len(UpperCAmelCase__					) == 0 or len(UpperCAmelCase__					) == 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
					SCREAMING_SNAKE_CASE           =    1.0 * num_same / len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    1.0 * num_same / len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    (2 * precision * recall) / (precision + recall)
					return fa





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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    {}
					for qid, s in scores.items():
										SCREAMING_SNAKE_CASE           =    na_probs[qid] > na_prob_thresh
										if pred_na:
															SCREAMING_SNAKE_CASE           =    float(not qid_to_has_ans[qid]					)
										else:
															SCREAMING_SNAKE_CASE           =    s
					return new_scores





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict=None					):
					if not qid_list:
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
										return collections.OrderedDict(
										    [
										        ("exact", 100.0 * sum(exact_scores.values()					) / total),
										        ("f1", 100.0 * sum(fa_scores.values()					) / total),
										        ("total", total),
										    ]					)
					else:
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
										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							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							List[str]					):
					for k in new_eval:
										SCREAMING_SNAKE_CASE           =    new_eval[k]





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							List[str]					):
					plt.step(UpperCAmelCase__ ,	UpperCAmelCase__ ,	color="b" ,	alpha=0.2 ,	where="post"					)
					plt.fill_between(UpperCAmelCase__ ,	UpperCAmelCase__ ,	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(UpperCAmelCase__					)
					plt.savefig(UpperCAmelCase__					)
					plt.clf()





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							int=None ,	UpperCAmelCase__       :							str=None					):
					SCREAMING_SNAKE_CASE           =    sorted(UpperCAmelCase__ ,	key=lambda UpperCAmelCase__					: na_probs[k]					)
					SCREAMING_SNAKE_CASE           =    0.0
					SCREAMING_SNAKE_CASE           =    1.0
					SCREAMING_SNAKE_CASE           =    0.0
					SCREAMING_SNAKE_CASE           =    [1.0]
					SCREAMING_SNAKE_CASE           =    [0.0]
					SCREAMING_SNAKE_CASE           =    0.0
					for i, qid in enumerate(UpperCAmelCase__					):
										if qid_to_has_ans[qid]:
															true_pos += scores[qid]
										SCREAMING_SNAKE_CASE           =    true_pos / float(i + 1					)
										SCREAMING_SNAKE_CASE           =    true_pos / float(UpperCAmelCase__					)
										if i == len(UpperCAmelCase__					) - 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(UpperCAmelCase__					)
															recalls.append(UpperCAmelCase__					)
					if out_image:
										plot_pr_curve(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					return {"ap": 100.0 * avg_prec}





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





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							int					):
					if not qid_list:
										return
					SCREAMING_SNAKE_CASE           =    [na_probs[k] for k in qid_list]
					SCREAMING_SNAKE_CASE           =    np.ones_like(UpperCAmelCase__					) / float(len(UpperCAmelCase__					)					)
					plt.hist(UpperCAmelCase__ ,	weights=UpperCAmelCase__ ,	bins=2_0 ,	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(UpperCAmelCase__ ,	F"na_prob_hist_{name}.png"					)					)
					plt.clf()





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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    find_best_thresh(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    find_best_thresh(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    best_exact
					SCREAMING_SNAKE_CASE           =    exact_thresh
					SCREAMING_SNAKE_CASE           =    best_fa
					SCREAMING_SNAKE_CASE           =    fa_thresh





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


if __name__ == "__main__":
							_lowerCamelCase   :				Optional[Any]     			=						parse_args()
							if OPTS.out_image_dir:
														import matplotlib

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




