
'''simple docstring'''







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

from transformers import BigBirdTokenizerFast


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



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



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



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Tuple=1						,      _SCREAMING_SNAKE_CASE		: List[str]=100	):
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			jax.lax.top_k(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			jax.lax.top_k(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

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

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



def       _a		(						_SCREAMING_SNAKE_CASE		: int	):
						_SCREAMING_SNAKE_CASE					=			sample["question"]["text"]
						_SCREAMING_SNAKE_CASE					=			sample["document"]["tokens"]["token"]
						_SCREAMING_SNAKE_CASE					=			sample["document"]["tokens"]["is_html"]
						_SCREAMING_SNAKE_CASE					=			sample["annotations"]["long_answer"]
						_SCREAMING_SNAKE_CASE					=			sample["annotations"]["short_answers"]

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

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

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

						_SCREAMING_SNAKE_CASE					=			"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		(						):
						_SCREAMING_SNAKE_CASE					=			load_from_disk("natural-questions-validation"	)
						_SCREAMING_SNAKE_CASE					=			dataset.map(_SCREAMING_SNAKE_CASE	).remove_columns(["annotations", "document", "id"]	)
						print(_SCREAMING_SNAKE_CASE	)

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

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

						@jax.jit
						def forward(*_SCREAMING_SNAKE_CASE		: int						,      **_SCREAMING_SNAKE_CASE		: Union[str, Any]	):
												_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			model(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE	)
												return start_logits, end_logits, jnp.argmax(_SCREAMING_SNAKE_CASE						,      axis=-1	)

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

												_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			forward(**_SCREAMING_SNAKE_CASE	)

												_SCREAMING_SNAKE_CASE					=			CATEGORY_MAPPING[category.item()]

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

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

												_SCREAMING_SNAKE_CASE					=			38 if predicted_category == "short" else 1024
												_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			get_best_valid_start_end_idx(
												    start_scores[0]						,      end_scores[0]						,      top_k=8						,      max_size=_SCREAMING_SNAKE_CASE	)

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

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

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

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

												return example

						_SCREAMING_SNAKE_CASE					=			short_validation_dataset.map(_SCREAMING_SNAKE_CASE	)

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


if __name__ == "__main__":
						main()

'''simple docstring'''







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case					: List[str]     			=					[[12, 10], [3, 9]]
_snake_case					: Any     			=					[[3, 4], [7, 4]]
_snake_case					: List[str]     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case					: Optional[Any]     			=					[1]
_snake_case					: str     			=					[2]

_snake_case					: Optional[int]     			=					logging.getLogger()
_snake_case					: Optional[Any]     			=					logging.DEBUG
_snake_case					: Tuple     			=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} returned integer'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_addition.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) + np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} with different matrix dims'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[Any]						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} returned integer'	)
																		matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) - np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												logger.info(F'\n\t{test_multiplication.__name__} returned integer'	)
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'	)
												_SCREAMING_SNAKE_CASE					=			(np.matmul(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.multiply(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(
																		    F'\n\t{test_multiplication.__name__} does not meet dim requirements'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(3.5 * np.array(_SCREAMING_SNAKE_CASE	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.scalar_multiply(_SCREAMING_SNAKE_CASE						,      3.5	)
						assert theo == act



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(np.identity(5	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.identity(5	)
						assert theo == act




@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"						,      [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_transpose.__name__} returned integer'	)
																		matop.transpose(_SCREAMING_SNAKE_CASE	)
						else:
												_SCREAMING_SNAKE_CASE					=			(np.transpose(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.transpose(_SCREAMING_SNAKE_CASE						,      return_map=_SCREAMING_SNAKE_CASE	)
												assert theo == act