




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

from transformers import BigBirdTokenizerFast


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




def      __magic_name__							(					A						:   List[Any],    A						:   List[str]=0,    A						:   Optional[int]=None  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   List[Any],    A						:   str=False  ):






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




def      __magic_name__							(					A						:   Tuple,    A						:   Tuple,    A						:   List[str]=1,    A						:   Any=100  ):






			'''simple docstring'''







			a       =     jax.lax.top_k(A,    A  )
			a       =     jax.lax.top_k(A,    A  )

			a       =     best_end_idx[:, None] - best_start_idx[None, :]
			a       =     jnp.logical_or(widths < 0,    widths > max_size  )
			a       =     (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
			a       =     jnp.argmax(A  ).item()

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




def      __magic_name__							(					A						:   List[str]  ):






			'''simple docstring'''







			a       =     sample['question']['text']
			a       =     sample['document']['tokens']['token']
			a       =     sample['document']['tokens']['is_html']
			a       =     sample['annotations']['long_answer']
			a       =     sample['annotations']['short_answers']

			a       =     ' '.join([context[i] for i in range(len(A  )  ) 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       =     []
			for s in short_answers:
						short_targets.extend(s["text"]  )
			a       =     list(set(A  )  )

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

			a       =     '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      __magic_name__							(					):






			'''simple docstring'''







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

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

			a       =     'vasudevgupta/flax-bigbird-natural-questions'
			a       =     FlaxBigBirdForNaturalQuestions.from_pretrained(A  )
			a       =     BigBirdTokenizerFast.from_pretrained(A  )

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

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

						a       =     forward(**A  )

						a       =     CATEGORY_MAPPING[category.item()]

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

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

						a       =     38 if predicted_category == 'short' else 1024
						a       =     get_best_valid_start_end_idx(
						    start_scores[0],    end_scores[0],    top_k=8,    max_size=A  )

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

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

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

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

						return example

			a       =     short_validation_dataset.map(A  )

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


if __name__ == "__main__":
		main()












from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__    :							List[Any]       		=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
