


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



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=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_     :  Optional[int]				      =     (
              given_answers + get_sub_answers(UpperCAmelCase__	,	begin=1			) + get_sub_answers(UpperCAmelCase__	,	end=-1			)
          )
     a_     :  int				      =     []
     for answer in given_answers:
          a_     :  str				      =     answer.replace("""_"""	,	""" """			).lower()
          a_     :  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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase=1	,	__UpperCamelCase=1_0_0			):
     a_     :  Union[str, Any]				      =     jax.lax.top_k(UpperCAmelCase__	,	UpperCAmelCase__			)
     a_     :  List[str]				      =     jax.lax.top_k(UpperCAmelCase__	,	UpperCAmelCase__			)

     a_     :  Tuple				      =     best_end_idx[:, None] - best_start_idx[None, :]
     a_     :  Tuple				      =     jnp.logical_or(widths < 0	,	widths > max_size			)
     a_     :  Any				      =     (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
     a_     :  Union[str, Any]				      =     jnp.argmax(UpperCAmelCase__			).item()

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



def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[int]				      =     sample["""question"""]["""text"""]
     a_     :  int				      =     sample["""document"""]["""tokens"""]["""token"""]
     a_     :  List[str]				      =     sample["""document"""]["""tokens"""]["""is_html"""]
     a_     :  List[Any]				      =     sample["""annotations"""]["""long_answer"""]
     a_     :  Optional[Any]				      =     sample["""annotations"""]["""short_answers"""]

     a_     :  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",
               }

     a_     :  Optional[Any]				      =     []
     for s in short_answers:
          short_targets.extend(s["""text"""]			)
     a_     :  Union[str, Any]				      =     list(set(UpperCAmelCase__			)			)

     a_     :  List[Any]				      =     []
     for s in long_answers:
          if s["start_token"] == -1:
               continue
          a_     :  List[Any]				      =     context[s["""start_token"""] : s["""end_token"""]]
          a_     :  str				      =     is_html[s["""start_token"""] : s["""end_token"""]]
          a_     :  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__			)

     a_     :  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 			_a      (    ):
     a_     :  Dict				      =     load_from_disk("""natural-questions-validation"""			)
     a_     :  Tuple				      =     dataset.map(UpperCAmelCase__			).remove_columns(["""annotations""", """document""", """id"""]			)
     print(UpperCAmelCase__			)

     a_     :  str				      =     dataset.filter(lambda __UpperCamelCase			: (len(x["""question"""]			) + len(x["""context"""]			)) < 4 * 4_0_9_6			)
     a_     :  List[str]				      =     short_validation_dataset.filter(lambda __UpperCamelCase			: x["category"] != "null"			)
     short_validation_dataset

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

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

     def evaluate(__UpperCamelCase			):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          a_     :  List[Any]				      =     tokenizer(
              example["""question"""]	,	example["""context"""]	,	return_tensors="""np"""	,	max_length=4_0_9_6	,	padding="""max_length"""	,	truncation=UpperCAmelCase__	,	)

          a_     :  List[str]				      =     forward(**UpperCAmelCase__			)

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

          a_     :  Optional[Any]				      =     example["""long"""] + example["""short"""]
          if example["category"] in ["yes", "no", "null"]:
               a_     :  Optional[Any]				      =     [example["""category"""]]
          a_     :  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"]:
               a_     :  List[str]				      =     [predicted_category]
               a_     :  int				      =     example["""output"""] == example["""targets"""]
               a_     :  Dict				      =     predicted_category != """null"""
               return example

          a_     :  Union[str, Any]				      =     3_8 if predicted_category == """short""" else 1_0_2_4
          a_     :  Union[str, Any]				      =     get_best_valid_start_end_idx(
              start_scores[0]	,	end_scores[0]	,	top_k=8	,	max_size=UpperCAmelCase__			)

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

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

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

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

          return example

     a_     :  str				      =     short_validation_dataset.map(UpperCAmelCase__			)

     a_     :  Tuple				      =     len(UpperCAmelCase__			)
     a_     :  List[str]				      =     len(short_validation_dataset.filter(lambda __UpperCamelCase			: x["match"] == 1			)			)
     print("""EM score:"""	,	(matched / total) * 1_0_0	,	"""%"""			)


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()





