




'''simple docstring'''


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

import numpy as np


_snake_case				:				str       							=		re.compile(R'\b(a|an|the)\b', re.UNICODE)

_snake_case				:				Optional[Any]       							=		None
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    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 		snake_case_   (UpperCamelCase	:  str				):





							'''simple docstring'''


							_a     					=    {}
							for article in dataset:
														for p in article["paragraphs"]:
																					for qa in p["qas"]:
																												_a     					=    bool(qa['''answers''']['''text''']				)
							return qid_to_has_ans
def 		snake_case_   (UpperCamelCase	:  Optional[Any]				):





							'''simple docstring'''


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

							def white_space_fix(UpperCamelCase	:  str				):
														return " ".join(text.split()				)

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

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

							return white_space_fix(remove_articles(remove_punc(lower(_a				)				)				)				)
def 		snake_case_   (UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							if not s:
														return []
							return normalize_answer(_a				).split()
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  Dict				):





							'''simple docstring'''


							return int(normalize_answer(_a				) == normalize_answer(_a				)				)
def 		snake_case_   (UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  str				):





							'''simple docstring'''


							_a     					=    get_tokens(_a				)
							_a     					=    get_tokens(_a				)
							_a     					=    collections.Counter(_a				) & collections.Counter(_a				)
							_a     					=    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
							_a     					=    1.0 * num_same / len(_a				)
							_a     					=    1.0 * num_same / len(_a				)
							_a     					=    (2 * precision * recall) / (precision + recall)
							return fa
def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Union[str, Any]				):





							'''simple docstring'''


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





							'''simple docstring'''


							_a     					=    {}
							for qid, s in scores.items():
														_a     					=    na_probs[qid] > na_prob_thresh
														if pred_na:
																					_a     					=    float(not qid_to_has_ans[qid]				)
														else:
																					_a     					=    s
							return new_scores
def 		snake_case_   (UpperCamelCase	:  str			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  Tuple=None				):





							'''simple docstring'''


							if not qid_list:
														_a     					=    len(_a				)
														return collections.OrderedDict(
														    [
														        ('''exact''', 100.0 * sum(exact_scores.values()				) / total),
														        ('''f1''', 100.0 * sum(fa_scores.values()				) / total),
														        ('''total''', total),
														    ]				)
							else:
														_a     					=    len(_a				)
														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 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							for k in new_eval:
														_a     					=    new_eval[k]
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  int			,						UpperCamelCase	:  int			,						UpperCamelCase	:  Union[str, Any]				):





							'''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, 1.05]				)
							plt.ylim([0.0, 1.05]				)
							plt.title(_a				)
							plt.savefig(_a				)
							plt.clf()
def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Tuple			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Optional[int]=None			,						UpperCamelCase	:  List[Any]=None				):





							'''simple docstring'''


							_a     					=    sorted(_a			,						key=lambda UpperCamelCase				: na_probs[k]				)
							_a     					=    0.0
							_a     					=    1.0
							_a     					=    0.0
							_a     					=    [1.0]
							_a     					=    [0.0]
							_a     					=    0.0
							for i, qid in enumerate(_a				):
														if qid_to_has_ans[qid]:
																					true_pos += scores[qid]
														_a     					=    true_pos / float(i + 1				)
														_a     					=    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": 100.0 * avg_prec}
def 		snake_case_   (UpperCamelCase	:  str			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  Optional[Any]				):





							'''simple docstring'''


							if out_image_dir and not os.path.exists(_a				):
														os.makedirs(_a				)
							_a     					=    sum(1 for v in qid_to_has_ans.values() if v				)
							if num_true_pos == 0:
														return
							_a     					=    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'''			,						)
							_a     					=    make_precision_recall_eval(
							    _a			,						_a			,						_a			,						_a			,						out_image=os.path.join(_a			,						'''pr_f1.png'''				)			,						title='''Precision-Recall curve for F1 score'''			,						)
							_a     					=    {k: float(_a				) for k, v in qid_to_has_ans.items()}
							_a     					=    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 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  str			,						UpperCamelCase	:  str				):





							'''simple docstring'''


							if not qid_list:
														return
							_a     					=    [na_probs[k] for k in qid_list]
							_a     					=    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 		snake_case_   (UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  str			,						UpperCamelCase	:  str			,						UpperCamelCase	:  Union[str, Any]				):





							'''simple docstring'''


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





							'''simple docstring'''


							_a					,							_a     					=    find_best_thresh(_a			,						_a			,						_a			,						_a				)
							_a					,							_a     					=    find_best_thresh(_a			,						_a			,						_a			,						_a				)
							_a     					=    best_exact
							_a     					=    exact_thresh
							_a     					=    best_fa
							_a     					=    fa_thresh
def 		snake_case_   ():





							'''simple docstring'''


							with open(OPTS.data_file				) as f:
														_a     					=    json.load(_a				)
														_a     					=    dataset_json['''data''']
							with open(OPTS.pred_file				) as f:
														_a     					=    json.load(_a				)
							if OPTS.na_prob_file:
														with open(OPTS.na_prob_file				) as f:
																					_a     					=    json.load(_a				)
							else:
														_a     					=    {k: 0.0 for k in preds}
							_a     					=    make_qid_to_has_ans(_a				)  # maps qid to True/False
							_a     					=    [k for k, v in qid_to_has_ans.items() if v]
							_a     					=    [k for k, v in qid_to_has_ans.items() if not v]
							_a					,							_a     					=    get_raw_scores(_a			,						_a				)
							_a     					=    apply_no_ans_threshold(_a			,						_a			,						_a			,						OPTS.na_prob_thresh				)
							_a     					=    apply_no_ans_threshold(_a			,						_a			,						_a			,						OPTS.na_prob_thresh				)
							_a     					=    make_eval_dict(_a			,						_a				)
							if has_ans_qids:
														_a     					=    make_eval_dict(_a			,						_a			,						qid_list=_a				)
														merge_eval(_a			,						_a			,						'''HasAns'''				)
							if no_ans_qids:
														_a     					=    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__":
			_snake_case				:				Dict       							=		parse_args()
			if OPTS.out_image_dir:
						import matplotlib

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







'''simple docstring'''


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed

class       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    iter(self.dataset )
														_a     					=    True
														while more_examples:
																					_a					,							_a     					=    [], 0
																					while True:
																												if buffer_len >= self.input_characters:
																																			break
																												try:
																																			buffer.append(next(lowerCAmelCase_ )['''content'''] )
																																			buffer_len += len(buffer[-1] )
																												except StopIteration:
																																			_a     					=    False
																																			break
																					_a     					=    tokenizer(lowerCAmelCase_							,      truncation=lowerCAmelCase_ )['''input_ids''']
																					_a     					=    []
																					for tokenized_input in tokenized_inputs:
																												all_token_ids.extend(tokenized_input + [self.concat_token_id] )
																					for i in range(0							,      len(lowerCAmelCase_ )							,      self.seq_length ):
																												_a     					=    all_token_ids[i : i + self.seq_length]
																												if len(lowerCAmelCase_ ) == self.seq_length:
																																			yield torch.tensor(lowerCAmelCase_ )



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							model.eval()
							_a     					=    []
							for step, batch in enumerate(UpperCamelCase				):
														with torch.no_grad():
																					_a     					=    model(UpperCamelCase			,						labels=UpperCamelCase				)
														_a     					=    outputs.loss.repeat(args.batch_size				)
														losses.append(accelerator.gather(UpperCamelCase				)				)

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							_a     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

# Logging
_snake_case				:				Any       							=		logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

# Prepare everything with our `accelerator`.
_snake_case							,    _snake_case				:				Optional[int]       							=		accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_snake_case							,    _snake_case				:				int       							=		evaluate(args)

logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')

