




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




def     snake_case__     (			lowerCamelCase__					: List[str]      ,		lowerCamelCase__					: Tuple=0      ,		lowerCamelCase__					: Tuple=None      ) -> Optional[Any]:
					return [" ".join(x.split(''' '''      )[begin:end]      ) for x in answers if len(x.split(''' '''      )      ) > 1]




def     snake_case__     (			lowerCamelCase__					: Any      ,		lowerCamelCase__					: Any=False      ) -> List[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"
										A_			:  Union[str, Any]			=				(
										    given_answers + get_sub_answers(lowerCamelCase__      ,		begin=1      ) + get_sub_answers(lowerCamelCase__      ,		end=-1      )
										)
					A_			:  List[Any]			=				[]
					for answer in given_answers:
										A_			:  Dict			=				answer.replace('''_'''      ,		''' '''      ).lower()
										A_			:  str			=				''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias      )
										answers.append(''' '''.join(alias.split()      ).strip()      )
					return set(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: Optional[Any]      ,		lowerCamelCase__					: Dict      ,		lowerCamelCase__					: Union[str, Any]=1      ,		lowerCamelCase__					: Any=1_0_0      ) -> Any:
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )

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

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




def     snake_case__     (			lowerCamelCase__					: List[str]      ) -> Union[str, Any]:
					A_			:  str			=				sample['''question''']['''text''']
					A_			:  Tuple			=				sample['''document''']['''tokens''']['''token''']
					A_			:  List[Any]			=				sample['''document''']['''tokens''']['''is_html''']
					A_			:  Tuple			=				sample['''annotations''']['''long_answer''']
					A_			:  List[Any]			=				sample['''annotations''']['''short_answers''']

					A_			:  Optional[Any]			=				''' '''.join([context[i] for i in range(len(lowerCamelCase__      )      ) 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_			:  List[Any]			=				[]
					for s in short_answers:
										short_targets.extend(s['''text''']      )
					A_			:  Tuple			=				list(set(lowerCamelCase__      )      )

					A_			:  Tuple			=				[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										A_			:  Dict			=				context[s['''start_token'''] : s['''end_token''']]
										A_			:  Tuple			=				is_html[s['''start_token'''] : s['''end_token''']]
										A_			:  Optional[Any]			=				''' '''.join([answer[i] for i in range(len(lowerCamelCase__      )      ) if not html[i]]      )
										if new_answer not in long_targets:
															long_targets.append(lowerCamelCase__      )

					A_			:  str			=				'''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     snake_case__     (			) -> List[Any]:
					A_			:  int			=				load_from_disk('''natural-questions-validation'''      )
					A_			:  Optional[int]			=				dataset.map(lowerCamelCase__      ).remove_columns(['''annotations''', '''document''', '''id''']      )
					print(lowerCamelCase__      )

					A_			:  Tuple			=				dataset.filter(lambda lowerCamelCase__      : (len(x['''question''']      ) + len(x['''context''']      )) < 4 * 4_0_9_6      )
					A_			:  Optional[Any]			=				short_validation_dataset.filter(lambda lowerCamelCase__      : x["category"] != "null"      )
					short_validation_dataset

					A_			:  Union[str, Any]			=				'''vasudevgupta/flax-bigbird-natural-questions'''
					A_			:  List[str]			=				FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase__      )
					A_			:  int			=				BigBirdTokenizerFast.from_pretrained(lowerCamelCase__      )

					@jax.jit
					def forward(*lowerCamelCase__					: Any      ,		**lowerCamelCase__					: Optional[Any]      ):
										A_			,A_			,A_			:  Tuple			=				model(*lowerCamelCase__      ,		**lowerCamelCase__      )
										return start_logits, end_logits, jnp.argmax(lowerCamelCase__      ,		axis=-1      )

					def evaluate(lowerCamelCase__					: List[str]      ):
										# 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=lowerCamelCase__      ,		)

										A_			,A_			,A_			:  Optional[Any]			=				forward(**lowerCamelCase__      )

										A_			:  Optional[Any]			=				CATEGORY_MAPPING[category.item()]

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

										if predicted_category in ["yes", "no", "null"]:
															A_			:  int			=				[predicted_category]
															A_			:  Optional[int]			=				example['''output'''] == example['''targets''']
															A_			:  Dict			=				predicted_category != '''null'''
															return example

										A_			:  List[Any]			=				3_8 if predicted_category == '''short''' else 1_0_2_4
										A_			,A_			:  str			=				get_best_valid_start_end_idx(
										    start_scores[0]      ,		end_scores[0]      ,		top_k=8      ,		max_size=lowerCamelCase__      )

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

										A_			:  Optional[int]			=				expand_to_aliases(example['''targets''']      ,		make_sub_answers=lowerCamelCase__      )
										A_			:  str			=				expand_to_aliases(example['''output''']      )

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

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

										return example

					A_			:  Dict			=				short_validation_dataset.map(lowerCamelCase__      )

					A_			:  List[str]			=				len(lowerCamelCase__      )
					A_			:  Any			=				len(short_validation_dataset.filter(lambda lowerCamelCase__      : x["match"] == 1      )      )
					print('''EM score:'''      ,		(matched / total) * 1_0_0      ,		'''%'''      )


if __name__ == "__main__":
							main()









'''simple docstring'''




from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


snake_case__ 							=    {
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """Constraint""",
							    """ConstraintListState""",
							    """DisjunctiveConstraint""",
							    """PhrasalConstraint""",
							]
							snake_case__ 							=    [
							    """BeamHypotheses""",
							    """BeamScorer""",
							    """BeamSearchScorer""",
							    """ConstrainedBeamSearchScorer""",
							]
							snake_case__ 							=    [
							    """EpsilonLogitsWarper""",
							    """EtaLogitsWarper""",
							    """ForcedBOSTokenLogitsProcessor""",
							    """ForcedEOSTokenLogitsProcessor""",
							    """HammingDiversityLogitsProcessor""",
							    """InfNanRemoveLogitsProcessor""",
							    """LogitsProcessor""",
							    """LogitsProcessorList""",
							    """LogitsWarper""",
							    """MinLengthLogitsProcessor""",
							    """MinNewTokensLengthLogitsProcessor""",
							    """NoBadWordsLogitsProcessor""",
							    """NoRepeatNGramLogitsProcessor""",
							    """PrefixConstrainedLogitsProcessor""",
							    """RepetitionPenaltyLogitsProcessor""",
							    """SequenceBiasLogitsProcessor""",
							    """EncoderRepetitionPenaltyLogitsProcessor""",
							    """TemperatureLogitsWarper""",
							    """TopKLogitsWarper""",
							    """TopPLogitsWarper""",
							    """TypicalLogitsWarper""",
							    """EncoderNoRepeatNGramLogitsProcessor""",
							    """ExponentialDecayLengthPenalty""",
							    """LogitNormalization""",
							]
							snake_case__ 							=    [
							    """MaxNewTokensCriteria""",
							    """MaxLengthCriteria""",
							    """MaxTimeCriteria""",
							    """StoppingCriteria""",
							    """StoppingCriteriaList""",
							    """validate_stopping_criteria""",
							]
							snake_case__ 							=    [
							    """GenerationMixin""",
							    """top_k_top_p_filtering""",
							    """GreedySearchEncoderDecoderOutput""",
							    """GreedySearchDecoderOnlyOutput""",
							    """SampleEncoderDecoderOutput""",
							    """SampleDecoderOnlyOutput""",
							    """BeamSearchEncoderDecoderOutput""",
							    """BeamSearchDecoderOnlyOutput""",
							    """BeamSampleEncoderDecoderOutput""",
							    """BeamSampleDecoderOnlyOutput""",
							    """ContrastiveSearchEncoderDecoderOutput""",
							    """ContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """TFForcedBOSTokenLogitsProcessor""",
							    """TFForcedEOSTokenLogitsProcessor""",
							    """TFLogitsProcessor""",
							    """TFLogitsProcessorList""",
							    """TFLogitsWarper""",
							    """TFMinLengthLogitsProcessor""",
							    """TFNoBadWordsLogitsProcessor""",
							    """TFNoRepeatNGramLogitsProcessor""",
							    """TFRepetitionPenaltyLogitsProcessor""",
							    """TFTemperatureLogitsWarper""",
							    """TFTopKLogitsWarper""",
							    """TFTopPLogitsWarper""",
							    """TFForceTokensLogitsProcessor""",
							    """TFSuppressTokensAtBeginLogitsProcessor""",
							    """TFSuppressTokensLogitsProcessor""",
							]
							snake_case__ 							=    [
							    """TFGenerationMixin""",
							    """tf_top_k_top_p_filtering""",
							    """TFGreedySearchDecoderOnlyOutput""",
							    """TFGreedySearchEncoderDecoderOutput""",
							    """TFSampleEncoderDecoderOutput""",
							    """TFSampleDecoderOnlyOutput""",
							    """TFBeamSearchEncoderDecoderOutput""",
							    """TFBeamSearchDecoderOnlyOutput""",
							    """TFBeamSampleEncoderDecoderOutput""",
							    """TFBeamSampleDecoderOnlyOutput""",
							    """TFContrastiveSearchEncoderDecoderOutput""",
							    """TFContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__ 							=    [
							    """FlaxForcedBOSTokenLogitsProcessor""",
							    """FlaxForcedEOSTokenLogitsProcessor""",
							    """FlaxLogitsProcessor""",
							    """FlaxLogitsProcessorList""",
							    """FlaxLogitsWarper""",
							    """FlaxMinLengthLogitsProcessor""",
							    """FlaxTemperatureLogitsWarper""",
							    """FlaxTopKLogitsWarper""",
							    """FlaxTopPLogitsWarper""",
							]
							snake_case__ 							=    [
							    """FlaxGenerationMixin""",
							    """FlaxGreedySearchOutput""",
							    """FlaxSampleOutput""",
							    """FlaxBeamSearchOutput""",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

							snake_case__ 							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



