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




def lowercase_		(				_lowercase     ,    _lowercase=0     ,    _lowercase=None			)       ->							Any:
					'''simple docstring'''

					return [" ".join(x.split(''' '''			)[begin:end]			) for x in answers if len(x.split(''' '''			)			) > 1]




def lowercase_		(				_lowercase     ,    _lowercase=False			)       ->							str:
					'''simple docstring'''

					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"
										lowerCamelCase_   : Optional[Any]       =						(
										    given_answers + get_sub_answers(_lowercase     ,    begin=1			) + get_sub_answers(_lowercase     ,    end=-1			)
										)
					lowerCamelCase_   : List[Any]       =						[]
					for answer in given_answers:
										lowerCamelCase_   : Optional[int]       =						answer.replace('''_'''     ,    ''' '''			).lower()
										lowerCamelCase_   : Tuple       =						''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias			)
										answers.append(''' '''.join(alias.split()			).strip()			)
					return set(_lowercase			)




def lowercase_		(				_lowercase     ,    _lowercase     ,    _lowercase=1     ,    _lowercase=100			)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_, lowerCamelCase_   : Dict       =						jax.lax.top_k(_lowercase     ,    _lowercase			)
					lowerCamelCase_, lowerCamelCase_   : Any       =						jax.lax.top_k(_lowercase     ,    _lowercase			)

					lowerCamelCase_   : Optional[int]       =						best_end_idx[:, None] - best_start_idx[None, :]
					lowerCamelCase_   : Any       =						jnp.logical_or(widths < 0     ,    widths > max_size			)
					lowerCamelCase_   : Any       =						(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
					lowerCamelCase_   : Any       =						jnp.argmax(_lowercase			).item()

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




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Union[str, Any]       =						sample['''question''']['''text''']
					lowerCamelCase_   : Optional[int]       =						sample['''document''']['''tokens''']['''token''']
					lowerCamelCase_   : str       =						sample['''document''']['''tokens''']['''is_html''']
					lowerCamelCase_   : Any       =						sample['''annotations''']['''long_answer''']
					lowerCamelCase_   : List[Any]       =						sample['''annotations''']['''short_answers''']

					lowerCamelCase_   : int       =						''' '''.join([context[i] for i in range(len(_lowercase			)			) 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",
															}

					lowerCamelCase_   : Tuple       =						[]
					for s in short_answers:
										short_targets.extend(s['''text''']			)
					lowerCamelCase_   : Optional[Any]       =						list(set(_lowercase			)			)

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

					lowerCamelCase_   : Tuple       =						'''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 lowercase_		(				)       ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_   : Tuple       =						load_from_disk('''natural-questions-validation'''			)
					lowerCamelCase_   : int       =						dataset.map(_lowercase			).remove_columns(['''annotations''', '''document''', '''id''']			)
					print(_lowercase			)

					lowerCamelCase_   : Union[str, Any]       =						dataset.filter(lambda _lowercase			: (len(x['''question''']			) + len(x['''context''']			)) < 4 * 4_096			)
					lowerCamelCase_   : Optional[Any]       =						short_validation_dataset.filter(lambda _lowercase			: x["category"] != "null"			)
					short_validation_dataset

					lowerCamelCase_   : Tuple       =						'''vasudevgupta/flax-bigbird-natural-questions'''
					lowerCamelCase_   : Union[str, Any]       =						FlaxBigBirdForNaturalQuestions.from_pretrained(_lowercase			)
					lowerCamelCase_   : Optional[Any]       =						BigBirdTokenizerFast.from_pretrained(_lowercase			)

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

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

										lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : Tuple       =						forward(**_lowercase			)

										lowerCamelCase_   : List[Any]       =						CATEGORY_MAPPING[category.item()]

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

										if predicted_category in ["yes", "no", "null"]:
															lowerCamelCase_   : Union[str, Any]       =						[predicted_category]
															lowerCamelCase_   : Union[str, Any]       =						example['''output'''] == example['''targets''']
															lowerCamelCase_   : Optional[Any]       =						predicted_category != '''null'''
															return example

										lowerCamelCase_   : Optional[int]       =						38 if predicted_category == '''short''' else 1_024
										lowerCamelCase_, lowerCamelCase_   : Dict       =						get_best_valid_start_end_idx(
										    start_scores[0]     ,    end_scores[0]     ,    top_k=8     ,    max_size=_lowercase			)

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

										lowerCamelCase_   : Optional[Any]       =						expand_to_aliases(example['''targets''']     ,    make_sub_answers=_lowercase			)
										lowerCamelCase_   : Union[str, Any]       =						expand_to_aliases(example['''output''']			)

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

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

										return example

					lowerCamelCase_   : Any       =						short_validation_dataset.map(_lowercase			)

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


if __name__ == "__main__":
					main()




'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


