




'''simple docstring'''

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

from transformers import BigBirdTokenizerFast


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




def    snake_case__     (  a       ,      a=0       ,      a=None	)			->       Any:





			'''simple docstring'''






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




def    snake_case__     (  a       ,      a=False	)			->       Dict:





			'''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"
						snake_case__          =						(
						    given_answers + get_sub_answers(a       ,      begin=1	) + get_sub_answers(a       ,      end=-1	)
						)
			snake_case__          =						[]
			for answer in given_answers:
						snake_case__          =						answer.replace("""_"""       ,      """ """	).lower()
						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(a	)




def    snake_case__     (  a       ,      a       ,      a=1       ,      a=100	)			->       str:





			'''simple docstring'''






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

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

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




def    snake_case__     (  a	)			->       Any:





			'''simple docstring'''






			snake_case__          =						sample["""question"""]["""text"""]
			snake_case__          =						sample["""document"""]["""tokens"""]["""token"""]
			snake_case__          =						sample["""document"""]["""tokens"""]["""is_html"""]
			snake_case__          =						sample["""annotations"""]["""long_answer"""]
			snake_case__          =						sample["""annotations"""]["""short_answers"""]

			snake_case__          =						""" """.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",
									}

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

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

			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    snake_case__     (  )			->       Dict:





			'''simple docstring'''






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

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

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

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

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

						snake_case__    ,			snake_case__    ,			snake_case__          =						forward(**a	)

						snake_case__          =						CATEGORY_MAPPING[category.item()]

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

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

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

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

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

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

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

						return example

			snake_case__          =						short_validation_dataset.map(a	)

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


if __name__ == "__main__":
		main()

"""simple docstring"""






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
				import torch

__UpperCAmelCase 					=    logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
				from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






				def 		_snake_case			(      cls						:    Tuple						)    ->							List[Any]:


							'''simple docstring'''





							return cls






def 		_snake_case			(      lowercase__						:    Union[Tuple, List] ,	lowercase__						:    Optional[Union[List["torch.Generator"], "torch.Generator"]] = None ,	lowercase__						:    Optional["torch.device"] = None ,	lowercase__						:    Optional["torch.dtype"] = None ,	lowercase__						:    Optional["torch.layout"] = None ,	)    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    device
			lowerCAmelCase_       :Any     	=    shape[0]

			lowerCAmelCase_       :Dict     	=    layout or torch.strided
			lowerCAmelCase_       :List[str]     	=    device or torch.device("""cpu"""						)

			if generator is not None:
						lowerCAmelCase_       :Optional[Any]     	=    generator.device.type if not isinstance(lowercase__ ,	lowercase__						) else generator[0].device.type
						if gen_device_type != device.type and gen_device_type == "cpu":
									lowerCAmelCase_       :Optional[int]     	=    """cpu"""
									if device != "mps":
												logger.info(
												    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
												    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
												    f""" slighly speed up this function by passing a generator that was created on the {device} device."""						)
						elif gen_device_type != device.type and gen_device_type == "cuda":
									raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""						)

			if isinstance(lowercase__ ,	lowercase__						):
						lowerCAmelCase_       :List[str]     	=    (1,) + shape[1:]
						lowerCAmelCase_       :Optional[Any]     	=    [
						    torch.randn(lowercase__ ,	generator=generator[i] ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						)
						    for i in range(lowercase__						)
						]
						lowerCAmelCase_       :str     	=    torch.cat(lowercase__ ,	dim=0						).to(lowercase__						)
			else:
						lowerCAmelCase_       :Dict     	=    torch.randn(lowercase__ ,	generator=lowercase__ ,	device=lowercase__ ,	dtype=lowercase__ ,	layout=lowercase__						).to(lowercase__						)

			return latents






def 		_snake_case			(      lowercase__						:    Union[str, Any]						)    ->							int:


			'''simple docstring'''





			if is_torch_version("""<""" ,	"""2.0.0"""						) or not hasattr(lowercase__ ,	"""_dynamo"""						):
						return False
			return isinstance(lowercase__ ,	torch._dynamo.eval_frame.OptimizedModule						)






