



"""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 					_lowerCAmelCase (  lowercase_						,      lowercase_=0						,      lowercase_=None		):
						return [" ".join(x.split(' '		)[begin:end]		) for x in answers if len(x.split(' '		)		) > 1]

def 					_lowerCAmelCase (  lowercase_						,      lowercase_=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"
												UpperCAmelCase			 =					(
												    given_answers + get_sub_answers(lowercase_						,      begin=1		) + get_sub_answers(lowercase_						,      end=-1		)
												)
						UpperCAmelCase			 =					[]
						for answer in given_answers:
												UpperCAmelCase			 =					answer.replace('_'						,      ' '		).lower()
												UpperCAmelCase			 =					''''''.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 					_lowerCAmelCase (  lowercase_						,      lowercase_						,      lowercase_=1						,      lowercase_=100		):
						UpperCAmelCase			 =					jax.lax.top_k(lowercase_						,      lowercase_		)
						UpperCAmelCase			 =					jax.lax.top_k(lowercase_						,      lowercase_		)

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

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

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					sample['''question''']['''text''']
						UpperCAmelCase			 =					sample['''document''']['''tokens''']['''token''']
						UpperCAmelCase			 =					sample['''document''']['''tokens''']['''is_html''']
						UpperCAmelCase			 =					sample['''annotations''']['''long_answer''']
						UpperCAmelCase			 =					sample['''annotations''']['''short_answers''']

						UpperCAmelCase			 =					''' '''.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",
																		}

						UpperCAmelCase			 =					[]
						for s in short_answers:
												short_targets.extend(s['text']		)
						UpperCAmelCase			 =					list(set(lowercase_		)		)

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

						UpperCAmelCase			 =					'''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 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					load_from_disk('natural-questions-validation'		)
						UpperCAmelCase			 =					dataset.map(lowercase_		).remove_columns(['annotations', 'document', 'id']		)
						print(lowercase_		)

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

						UpperCAmelCase			 =					'''vasudevgupta/flax-bigbird-natural-questions'''
						UpperCAmelCase			 =					FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase_		)
						UpperCAmelCase			 =					BigBirdTokenizerFast.from_pretrained(lowercase_		)

						@jax.jit
						def forward(*lowercase_						,      **lowercase_		):
												UpperCAmelCase			 =					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
												UpperCAmelCase			 =					tokenizer(
												    example['question']						,      example['context']						,      return_tensors='np'						,      max_length=4096						,      padding='max_length'						,      truncation=lowercase_						,      )

												UpperCAmelCase			 =					forward(**lowercase_		)

												UpperCAmelCase			 =					CATEGORY_MAPPING[category.item()]

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

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

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

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

												UpperCAmelCase			 =					expand_to_aliases(example['targets']						,      make_sub_answers=lowercase_		)
												UpperCAmelCase			 =					expand_to_aliases(example['output']		)

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

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

												return example

						UpperCAmelCase			 =					short_validation_dataset.map(lowercase_		)

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


if __name__ == "__main__":
				main()











'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''       )

         super().__init__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''       )

         if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''       )

         # first, apply the image processor
         SCREAMING_SNAKE_CASE__      :					str							    =			self.image_processor(images=a__			,	return_tensors=a__       )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(a__			,	a__       ):
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
            SCREAMING_SNAKE_CASE__      :					Any							    =			features['''words''']

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(
             text=text if text is not None else features['''words''']			,	text_pair=text_pair if text_pair is not None else None			,	boxes=boxes if boxes is not None else features['''boxes''']			,	word_labels=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_token_type_ids=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)

         # add pixel values
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			features.pop('''pixel_values'''       )
         if return_overflowing_tokens is True:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_overflowing_images(a__			,	encoded_inputs['''overflow_to_sample_mapping''']       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			images

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

         if len(a__       ) != len(a__       ):
            raise ValueError(
                '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                f""" {len(a__       )} and {len(a__       )}"""       )

         return images_with_overflow






      def  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,	a__			,	)
         return self.image_processor_class






      @property
      def  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

