






"""simple docstring"""







import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__a							:						List[Any]   							=			getLogger(__name__)


__a							:						Union[str, Any]   							=			'cuda' if torch.cuda.is_available() else 'cpu'





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_ = 8			,  lowerCamelCase_ = DEFAULT_DEVICE			,  lowerCamelCase_=False			,  lowerCamelCase_="summarization"			,  lowerCamelCase_=None			,  **lowerCamelCase_			,  ):
					a__ 							=	Path(lowerCamelCase_).open('''w'''			,  encoding='''utf-8''')
					a__ 							=	str(lowerCamelCase_)
					a__ 							=	AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_).to(lowerCamelCase_)
					if fpaa:
										a__ 							=	model.half()

					a__ 							=	AutoTokenizer.from_pretrained(lowerCamelCase_)
					logger.info(f'Inferred tokenizer type: {tokenizer.__class__}')  # if this is wrong, check config.model_type.

					a__ 							=	time.time()
					# update config with task specific params
					use_task_specific_params(lowerCamelCase_			,  lowerCamelCase_)
					if prefix is None:
										a__ 							=	prefix or getattr(model.config			,  '''prefix'''			,  '''''') or ''''''
					for examples_chunk in tqdm(list(chunks(lowerCamelCase_			,  lowerCamelCase_))):
										a__ 							=	[prefix + text for text in examples_chunk]
										a__ 							=	tokenizer(lowerCamelCase_			,  return_tensors='''pt'''			,  truncation=lowerCamelCase_			,  padding='''longest''').to(lowerCamelCase_)
										a__ 							=	model.generate(
										    input_ids=batch.input_ids			,  attention_mask=batch.attention_mask			,  **lowerCamelCase_			,  )
										a__ 							=	tokenizer.batch_decode(lowerCamelCase_			,  skip_special_tokens=lowerCamelCase_			,  clean_up_tokenization_spaces=lowerCamelCase_)
										for hypothesis in dec:
															fout.write(hypothesis + '''\n''')
															fout.flush()
					fout.close()
					a__ 							=	int(time.time() - start_time)  # seconds
					a__ 							=	len(lowerCamelCase_)
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,  4)}





def 							SCREAMING_SNAKE_CASE							(   ):
					return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''')





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_=True):
					a__ 							=	argparse.ArgumentParser()
					parser.add_argument('''model_name'''			,  type=lowerCamelCase_			,  help='''like facebook/bart-large-cnn,t5-base, etc.''')
					parser.add_argument('''input_path'''			,  type=lowerCamelCase_			,  help='''like cnn_dm/test.source''')
					parser.add_argument('''save_path'''			,  type=lowerCamelCase_			,  help='''where to save summaries''')
					parser.add_argument('''--reference_path'''			,  type=lowerCamelCase_			,  required=lowerCamelCase_			,  help='''like cnn_dm/test.target''')
					parser.add_argument('''--score_path'''			,  type=lowerCamelCase_			,  required=lowerCamelCase_			,  default='''metrics.json'''			,  help='''where to save metrics''')
					parser.add_argument('''--device'''			,  type=lowerCamelCase_			,  required=lowerCamelCase_			,  default=lowerCamelCase_			,  help='''cuda, cuda:1, cpu etc.''')
					parser.add_argument(
					    '''--prefix'''			,  type=lowerCamelCase_			,  required=lowerCamelCase_			,  default=lowerCamelCase_			,  help='''will be added to the begininng of src examples''')
					parser.add_argument('''--task'''			,  type=lowerCamelCase_			,  default='''summarization'''			,  help='''used for task_specific_params + metrics''')
					parser.add_argument('''--bs'''			,  type=lowerCamelCase_			,  default=8			,  required=lowerCamelCase_			,  help='''batch size''')
					parser.add_argument(
					    '''--n_obs'''			,  type=lowerCamelCase_			,  default=-1			,  required=lowerCamelCase_			,  help='''How many observations. Defaults to all.''')
					parser.add_argument('''--fp16'''			,  action='''store_true''')
					parser.add_argument('''--dump-args'''			,  action='''store_true'''			,  help='''print the custom hparams with the results''')
					parser.add_argument(
					    '''--info'''			,  nargs='''?'''			,  type=lowerCamelCase_			,  const=datetime_now()			,  help=(
					        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
					        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
					    )			,  )
					# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
					a__							,a__ 							=	parser.parse_known_args()
					a__ 							=	parse_numeric_n_bool_cl_kwargs(lowerCamelCase_)
					if parsed_args and verbose:
										print(f'parsed the following generate kwargs: {parsed_args}')
					with open(args.input_path) as f:
										a__ 							=	[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										a__ 							=	examples[: args.n_obs]
					Path(args.save_path).parent.mkdir(exist_ok=lowerCamelCase_)
					if args.reference_path is None and Path(args.score_path).exists():
										warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.')
					a__ 							=	generate_summaries_or_translations(
					    lowerCamelCase_			,  args.save_path			,  args.model_name			,  batch_size=args.bs			,  device=args.device			,  fpaa=args.fpaa			,  task=args.task			,  prefix=args.prefix			,  **lowerCamelCase_			,  )

					if args.reference_path is None:
										return {}

					# Compute scores
					a__ 							=	calculate_bleu if '''translation''' in args.task else calculate_rouge
					a__ 							=	[x.rstrip() for x in open(args.save_path).readlines()]
					a__ 							=	[x.rstrip() for x in open(args.reference_path).readlines()][: len(lowerCamelCase_)]
					a__ 							=	score_fn(lowerCamelCase_			,  lowerCamelCase_)
					scores.update(lowerCamelCase_)

					if args.dump_args:
										scores.update(lowerCamelCase_)
					if args.info:
										a__ 							=	args.info

					if verbose:
										print(lowerCamelCase_)

					if args.score_path is not None:
										json.dump(lowerCamelCase_			,  open(args.score_path			,  '''w'''))

					return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)










"""simple docstring"""







import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__a							:						Optional[int]   							=			{0: 'null', 1: 'short', 2: 'long', 3: 'yes', 4: 'no'}
__a							:						Dict   							=			set(''.join(['‘', '’', '´', '`', '.', ',', '-', '"']))





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=0			,  lowerCamelCase_=None):
					return [" ".join(x.split(''' ''')[begin:end]) for x in answers if len(x.split(''' ''')) > 1]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=False):
					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										a__ 							=	(
										    given_answers + get_sub_answers(lowerCamelCase_			,  begin=1) + get_sub_answers(lowerCamelCase_			,  end=-1)
										)
					a__ 							=	[]
					for answer in given_answers:
										a__ 							=	answer.replace('''_'''			,  ''' ''').lower()
										a__ 							=	''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias)
										answers.append(''' '''.join(alias.split()).strip())
					return set(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_=1			,  lowerCamelCase_=100):
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)

					a__ 							=	best_end_idx[:, None] - best_start_idx[None, :]
					a__ 							=	jnp.logical_or(widths < 0			,  widths > max_size)
					a__ 							=	(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
					a__ 							=	jnp.argmax(lowerCamelCase_).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					a__ 							=	sample['''question''']['''text''']
					a__ 							=	sample['''document''']['''tokens''']['''token''']
					a__ 							=	sample['''document''']['''tokens''']['''is_html''']
					a__ 							=	sample['''annotations''']['''long_answer''']
					a__ 							=	sample['''annotations''']['''short_answers''']

					a__ 							=	''' '''.join([context[i] for i in range(len(lowerCamelCase_)) if not is_html[i]])

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					a__ 							=	[]
					for s in short_answers:
										short_targets.extend(s['''text'''])
					a__ 							=	list(set(lowerCamelCase_))

					a__ 							=	[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										a__ 							=	context[s['''start_token'''] : s['''end_token''']]
										a__ 							=	is_html[s['''start_token'''] : s['''end_token''']]
										a__ 							=	''' '''.join([answer[i] for i in range(len(lowerCamelCase_)) if not html[i]])
										if new_answer not in long_targets:
															long_targets.append(lowerCamelCase_)

					a__ 							=	'''long_short''' if len(short_targets + long_targets) > 0 else '''null'''

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}





def 							SCREAMING_SNAKE_CASE							(   ):
					a__ 							=	load_from_disk('''natural-questions-validation''')
					a__ 							=	dataset.map(lowerCamelCase_).remove_columns(['''annotations''', '''document''', '''id'''])
					print(lowerCamelCase_)

					a__ 							=	dataset.filter(lambda lowerCamelCase_: (len(x['''question''']) + len(x['''context'''])) < 4 * 4096)
					a__ 							=	short_validation_dataset.filter(lambda lowerCamelCase_: x["category"] != "null")
					short_validation_dataset

					a__ 							=	'''vasudevgupta/flax-bigbird-natural-questions'''
					a__ 							=	FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase_)
					a__ 							=	BigBirdTokenizerFast.from_pretrained(lowerCamelCase_)

					@jax.jit
					def forward(*lowerCamelCase_			,  **lowerCamelCase_):
										a__							,a__							,a__ 							=	model(*lowerCamelCase_			,  **lowerCamelCase_)
										return start_logits, end_logits, jnp.argmax(lowerCamelCase_			,  axis=-1)

					def evaluate(lowerCamelCase_):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										a__ 							=	tokenizer(
										    example['''question''']			,  example['''context''']			,  return_tensors='''np'''			,  max_length=4096			,  padding='''max_length'''			,  truncation=lowerCamelCase_			,  )

										a__							,a__							,a__ 							=	forward(**lowerCamelCase_)

										a__ 							=	CATEGORY_MAPPING[category.item()]

										a__ 							=	example['''long'''] + example['''short''']
										if example["category"] in ["yes", "no", "null"]:
															a__ 							=	[example['''category''']]
										a__ 							=	example['''category'''] != '''null'''
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															a__ 							=	[predicted_category]
															a__ 							=	example['''output'''] == example['''targets''']
															a__ 							=	predicted_category != '''null'''
															return example

										a__ 							=	38 if predicted_category == '''short''' else 1024
										a__							,a__ 							=	get_best_valid_start_end_idx(
										    start_scores[0]			,  end_scores[0]			,  top_k=8			,  max_size=lowerCamelCase_)

										a__ 							=	inputs['''input_ids'''][0].tolist()
										a__ 							=	[tokenizer.decode(input_ids[start_score : end_score + 1])]

										a__ 							=	expand_to_aliases(example['''targets''']			,  make_sub_answers=lowerCamelCase_)
										a__ 							=	expand_to_aliases(example['''output'''])

										# some preprocessing to both prediction and answer
										a__ 							=	{''''''.join(a.split()) for a in answers}
										a__ 							=	{''''''.join(p.split()) for p in predictions}
										a__ 							=	{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

										# if there is a common element, it's a exact match
										a__ 							=	len(list(answers & predictions)) > 0
										a__ 							=	predicted_category != '''null''' and len(lowerCamelCase_) > 0

										return example

					a__ 							=	short_validation_dataset.map(lowerCamelCase_)

					a__ 							=	len(lowerCamelCase_)
					a__ 							=	len(short_validation_dataset.filter(lambda lowerCamelCase_: x["match"] == 1))
					print('''EM score:'''			,  (matched / total) * 100			,  '''%''')


if __name__ == "__main__":
	main()


