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

from transformers import BigBirdTokenizerFast


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

def 	__lowerCamelCase							(				__a    : str      ,							__a    : Optional[Any]=0      ,							__a    : Optional[int]=None		)      ->       Optional[Any]:
	return [" ".join(x.split(" "		)[begin:end]		) for x in answers if len(x.split(" "		)		) > 1]

def 	__lowerCamelCase							(				__a    : Optional[Any]      ,							__a    : Any=False		)      ->       Any:
	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"
		_lowercase   =(
		    given_answers + get_sub_answers(__a      ,							begin=1		) + get_sub_answers(__a      ,							end=-1		)
		)
	_lowercase   =[]
	for answer in given_answers:
		_lowercase   =answer.replace("_"      ,							" "		).lower()
		_lowercase   ="".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias		)
		answers.append(" ".join(alias.split()		).strip()		)
	return set(__a		)

def 	__lowerCamelCase							(				__a    : Any      ,							__a    : Tuple      ,							__a    : Dict=1      ,							__a    : str=100		)      ->       Dict:
	_lowercase   ,  _lowercase   =jax.lax.top_k(__a      ,							__a		)
	_lowercase   ,  _lowercase   =jax.lax.top_k(__a      ,							__a		)

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

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

def 	__lowerCamelCase							(				__a    : Optional[int]		)      ->       str:
	_lowercase   =sample["question"]["text"]
	_lowercase   =sample["document"]["tokens"]["token"]
	_lowercase   =sample["document"]["tokens"]["is_html"]
	_lowercase   =sample["annotations"]["long_answer"]
	_lowercase   =sample["annotations"]["short_answers"]

	_lowercase   =" ".join([context[i] for i in range(len(__a		)		) 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",
			}

	_lowercase   =[]
	for s in short_answers:
		short_targets.extend(s["text"]		)
	_lowercase   =list(set(__a		)		)

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

	_lowercase   ="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 	__lowerCamelCase							(				)      ->       Optional[int]:
	_lowercase   =load_from_disk("natural-questions-validation"		)
	_lowercase   =dataset.map(__a		).remove_columns(["annotations", "document", "id"]		)
	print(__a		)

	_lowercase   =dataset.filter(lambda __a		: (len(x["question"]		) + len(x["context"]		)) < 4 * 4_096		)
	_lowercase   =short_validation_dataset.filter(lambda __a		: x["category"] != "null"		)
	short_validation_dataset

	_lowercase   ="vasudevgupta/flax-bigbird-natural-questions"
	_lowercase   =FlaxBigBirdForNaturalQuestions.from_pretrained(__a		)
	_lowercase   =BigBirdTokenizerFast.from_pretrained(__a		)

	@jax.jit
	def forward(*__a    : Tuple      ,							**__a    : int		):
		_lowercase   ,  _lowercase   ,  _lowercase   =model(*__a      ,							**__a		)
		return start_logits, end_logits, jnp.argmax(__a      ,							axis=-1		)

	def evaluate(__a    : Optional[int]		):
		# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
		_lowercase   =tokenizer(
		    example["question"]      ,							example["context"]      ,							return_tensors="np"      ,							max_length=4_096      ,							padding="max_length"      ,							truncation=__a      ,							)

		_lowercase   ,  _lowercase   ,  _lowercase   =forward(**__a		)

		_lowercase   =CATEGORY_MAPPING[category.item()]

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

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

		_lowercase   =38 if predicted_category == "short" else 1_024
		_lowercase   ,  _lowercase   =get_best_valid_start_end_idx(
		    start_scores[0]      ,							end_scores[0]      ,							top_k=8      ,							max_size=__a		)

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

		_lowercase   =expand_to_aliases(example["targets"]      ,							make_sub_answers=__a		)
		_lowercase   =expand_to_aliases(example["output"]		)

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

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

		return example

	_lowercase   =short_validation_dataset.map(__a		)

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


if __name__ == "__main__":
						main()












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   def __init__(      self					:	Optional[int],			*_lowerCamelCase					:	int,			**_lowerCamelCase					:	str    ):

     '''simple docstring'''
     warnings.warn(
         '''The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
         ''' use ViltImageProcessor instead.''',			_lowerCamelCase,			)
     super().__init__(*_lowerCamelCase,			**_lowerCamelCase    )




