'''simple docstring'''




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

from transformers import BigBirdTokenizerFast


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




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




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




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   List[Any] , UpperCAmelCase__			:   int=1 , UpperCAmelCase__			:   Optional[Any]=100					)     -> Dict:
							lowercase_		,		lowercase_   :     Union[str, Any]									=			jax.lax.top_k(UpperCAmelCase__ , UpperCAmelCase__					)
							lowercase_		,		lowercase_   :     List[str]									=			jax.lax.top_k(UpperCAmelCase__ , UpperCAmelCase__					)

							lowercase_   :     Tuple									=			best_end_idx[:, None] - best_start_idx[None, :]
							lowercase_   :     Tuple									=			jnp.logical_or(widths < 0 , widths > max_size					)
							lowercase_   :     Any									=			(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
							lowercase_   :     Union[str, Any]									=			jnp.argmax(UpperCAmelCase__					).item()

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




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any]					)     -> List[str]:
							lowercase_   :     Optional[int]									=			sample["""question"""]["""text"""]
							lowercase_   :     int									=			sample["""document"""]["""tokens"""]["""token"""]
							lowercase_   :     List[str]									=			sample["""document"""]["""tokens"""]["""is_html"""]
							lowercase_   :     List[Any]									=			sample["""annotations"""]["""long_answer"""]
							lowercase_   :     Optional[Any]									=			sample["""annotations"""]["""short_answers"""]

							lowercase_   :     Optional[Any]									=			""" """.join([context[i] for i in range(len(UpperCAmelCase__					)					) 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_   :     Optional[Any]									=			[]
							for s in short_answers:
														short_targets.extend(s["""text"""]					)
							lowercase_   :     Union[str, Any]									=			list(set(UpperCAmelCase__					)					)

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

							lowercase_   :     Union[str, Any]									=			"""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_   :     Dict									=			load_from_disk("""natural-questions-validation"""					)
							lowercase_   :     Tuple									=			dataset.map(UpperCAmelCase__					).remove_columns(["""annotations""", """document""", """id"""]					)
							print(UpperCAmelCase__					)

							lowercase_   :     str									=			dataset.filter(lambda UpperCAmelCase__					: (len(x["""question"""]					) + len(x["""context"""]					)) < 4 * 4096					)
							lowercase_   :     List[str]									=			short_validation_dataset.filter(lambda UpperCAmelCase__					: x["category"] != "null"					)
							short_validation_dataset

							lowercase_   :     Optional[Any]									=			"""vasudevgupta/flax-bigbird-natural-questions"""
							lowercase_   :     Optional[Any]									=			FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCAmelCase__					)
							lowercase_   :     Optional[Any]									=			BigBirdTokenizerFast.from_pretrained(UpperCAmelCase__					)

							@jax.jit
							def forward(*UpperCAmelCase__			:   Dict , **UpperCAmelCase__			:   Optional[int]					):
														lowercase_		,		lowercase_		,		lowercase_   :     List[Any]									=			model(*UpperCAmelCase__ , **UpperCAmelCase__					)
														return start_logits, end_logits, jnp.argmax(UpperCAmelCase__ , axis=-1					)

							def evaluate(UpperCAmelCase__			:   int					):
														# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
														lowercase_   :     List[Any]									=			tokenizer(
														    example["""question"""] , example["""context"""] , return_tensors="""np""" , max_length=4096 , padding="""max_length""" , truncation=UpperCAmelCase__ , )

														lowercase_		,		lowercase_		,		lowercase_   :     List[str]									=			forward(**UpperCAmelCase__					)

														lowercase_   :     Any									=			CATEGORY_MAPPING[category.item()]

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

														if predicted_category in ["yes", "no", "null"]:
																					lowercase_   :     List[str]									=			[predicted_category]
																					lowercase_   :     int									=			example["""output"""] == example["""targets"""]
																					lowercase_   :     Dict									=			predicted_category != """null"""
																					return example

														lowercase_   :     Union[str, Any]									=			38 if predicted_category == """short""" else 1024
														lowercase_		,		lowercase_   :     Union[str, Any]									=			get_best_valid_start_end_idx(
														    start_scores[0] , end_scores[0] , top_k=8 , max_size=UpperCAmelCase__					)

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

														lowercase_   :     List[Any]									=			expand_to_aliases(example["""targets"""] , make_sub_answers=UpperCAmelCase__					)
														lowercase_   :     Tuple									=			expand_to_aliases(example["""output"""]					)

														# some preprocessing to both prediction and answer
														lowercase_   :     Optional[int]									=			{"""""".join(a.split()					) for a in answers}
														lowercase_   :     Union[str, Any]									=			{"""""".join(p.split()					) for p in predictions}
														lowercase_   :     Any									=			{s for s in predictions if s not in ["""``""", """''""", """`""", """'"""]}

														# if there is a common element, it's a exact match
														lowercase_   :     Optional[Any]									=			len(list(answers & predictions					)					) > 0
														lowercase_   :     List[str]									=			predicted_category != """null""" and len(UpperCAmelCase__					) > 0

														return example

							lowercase_   :     str									=			short_validation_dataset.map(UpperCAmelCase__					)

							lowercase_   :     Tuple									=			len(UpperCAmelCase__					)
							lowercase_   :     List[str]									=			len(short_validation_dataset.filter(lambda UpperCAmelCase__					: x["match"] == 1					)					)
							print("""EM score:""" , (matched / total) * 100 , """%"""					)


if __name__ == "__main__":
						main()






'''simple docstring'''




import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:					int													=		[(1,)]




def 				lowerCamelCase      ( )     -> Dict:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow:
														assert True
							else:
														assert False, "should have been skipped"




def 				lowerCamelCase      ( )     -> List[str]:
							lowercase_   :     Union[str, Any]									=			bool(os.getenv("""RUN_SLOW""" , 0					)					)
							if run_slow and torch_device == "cuda":
														assert True
							else:
														assert False, "should have been skipped"





@require_torch
class        __magic_name__		(       unittest.TestCase):







			@slow
			@require_torch_gpu
			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										check_slow_torch_cuda()







			@require_torch_gpu
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										check_slow_torch_cuda()







			@parameterized.expand(lowercase_       )
			@slow
			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    Dict=None       ):
										check_slow()




@slow
@require_torch_gpu
def 				lowerCamelCase      ( )     -> Union[str, Any]:
							check_slow_torch_cuda()




@require_torch_gpu
@slow
def 				lowerCamelCase      ( )     -> Any:
							check_slow_torch_cuda()




@slow
@pytest.mark.parametrize("""param""" , [1]					)
def 				lowerCamelCase      ( UpperCAmelCase__			:   List[str]					)     -> List[Any]:
							check_slow()




@pytest.mark.parametrize("""param""" , [1]					)
@slow
def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[Any]					)     -> Any:
							check_slow()





