



"""simple docstring"""




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

from transformers import BigBirdTokenizerFast


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

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Optional[int]						,  SCREAMING_SNAKE_CASE__	:						int=0						,  SCREAMING_SNAKE_CASE__	:						int=None				)  ->   List[str]:





     '''simple docstring'''

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

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Union[str, Any]						,  SCREAMING_SNAKE_CASE__	:						str=False				)  ->   Optional[Any]:





     '''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"
          _UpperCAmelCase			:	List[Any]								=    (
              given_answers + get_sub_answers(SCREAMING_SNAKE_CASE__						,  begin=1				) + get_sub_answers(SCREAMING_SNAKE_CASE__						,  end=-1				)
          )
     _UpperCAmelCase			:	List[Any]								=    []
     for answer in given_answers:
          _UpperCAmelCase			:	Union[str, Any]								=    answer.replace("_"						,  " "				).lower()
          _UpperCAmelCase			:	List[str]								=    ''.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 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Any						,  SCREAMING_SNAKE_CASE__	:						int						,  SCREAMING_SNAKE_CASE__	:						Tuple=1						,  SCREAMING_SNAKE_CASE__	:						Dict=100				)  ->   str:





     '''simple docstring'''

     _UpperCAmelCase			:	Optional[Any]								=    jax.lax.top_k(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__				)
     _UpperCAmelCase			:	Any								=    jax.lax.top_k(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__				)

     _UpperCAmelCase			:	List[str]								=    best_end_idx[:, None] - best_start_idx[None, :]
     _UpperCAmelCase			:	List[Any]								=    jnp.logical_or(widths < 0						,  widths > max_size				)
     _UpperCAmelCase			:	int								=    (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
     _UpperCAmelCase			:	str								=    jnp.argmax(SCREAMING_SNAKE_CASE__				).item()

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

def 						__snake_case						(      SCREAMING_SNAKE_CASE__	:						Optional[Any]				)  ->   Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase			:	Optional[int]								=    sample['question']['text']
     _UpperCAmelCase			:	Dict								=    sample['document']['tokens']['token']
     _UpperCAmelCase			:	int								=    sample['document']['tokens']['is_html']
     _UpperCAmelCase			:	Union[str, Any]								=    sample['annotations']['long_answer']
     _UpperCAmelCase			:	List[Any]								=    sample['annotations']['short_answers']

     _UpperCAmelCase			:	List[str]								=    ' '.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",
               }

     _UpperCAmelCase			:	Union[str, Any]								=    []
     for s in short_answers:
          short_targets.extend(s["text"]				)
     _UpperCAmelCase			:	Optional[Any]								=    list(set(SCREAMING_SNAKE_CASE__				)				)

     _UpperCAmelCase			:	int								=    []
     for s in long_answers:
          if s["start_token"] == -1:
               continue
          _UpperCAmelCase			:	int								=    context[s['start_token'] : s['end_token']]
          _UpperCAmelCase			:	int								=    is_html[s['start_token'] : s['end_token']]
          _UpperCAmelCase			:	List[str]								=    ' '.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__				)

     _UpperCAmelCase			:	List[Any]								=    '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]:





     '''simple docstring'''

     _UpperCAmelCase			:	List[Any]								=    load_from_disk("natural-questions-validation"				)
     _UpperCAmelCase			:	int								=    dataset.map(SCREAMING_SNAKE_CASE__				).remove_columns(["annotations", "document", "id"]				)
     print(SCREAMING_SNAKE_CASE__				)

     _UpperCAmelCase			:	Any								=    dataset.filter(lambda SCREAMING_SNAKE_CASE__				: (len(x["question"]				) + len(x["context"]				)) < 4 * 4_096				)
     _UpperCAmelCase			:	int								=    short_validation_dataset.filter(lambda SCREAMING_SNAKE_CASE__				: x["category"] != "null"				)
     short_validation_dataset

     _UpperCAmelCase			:	Union[str, Any]								=    'vasudevgupta/flax-bigbird-natural-questions'
     _UpperCAmelCase			:	List[Any]								=    FlaxBigBirdForNaturalQuestions.from_pretrained(SCREAMING_SNAKE_CASE__				)
     _UpperCAmelCase			:	Optional[Any]								=    BigBirdTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE__				)

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

     def evaluate(SCREAMING_SNAKE_CASE__	:						Optional[Any]				):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          _UpperCAmelCase			:	Optional[Any]								=    tokenizer(
              example["question"]						,  example["context"]						,  return_tensors="np"						,  max_length=4_096						,  padding="max_length"						,  truncation=SCREAMING_SNAKE_CASE__						,  )

          _UpperCAmelCase			:	Union[str, Any]								=    forward(**SCREAMING_SNAKE_CASE__				)

          _UpperCAmelCase			:	Dict								=    CATEGORY_MAPPING[category.item()]

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

          if predicted_category in ["yes", "no", "null"]:
               _UpperCAmelCase			:	str								=    [predicted_category]
               _UpperCAmelCase			:	Optional[Any]								=    example['output'] == example['targets']
               _UpperCAmelCase			:	List[str]								=    predicted_category != 'null'
               return example

          _UpperCAmelCase			:	Union[str, Any]								=    38 if predicted_category == 'short' else 1_024
          _UpperCAmelCase			:	Union[str, Any]								=    get_best_valid_start_end_idx(
              start_scores[0]						,  end_scores[0]						,  top_k=8						,  max_size=SCREAMING_SNAKE_CASE__				)

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

          _UpperCAmelCase			:	Optional[Any]								=    expand_to_aliases(example["targets"]						,  make_sub_answers=SCREAMING_SNAKE_CASE__				)
          _UpperCAmelCase			:	Dict								=    expand_to_aliases(example["output"]				)

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

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

          return example

     _UpperCAmelCase			:	Optional[Any]								=    short_validation_dataset.map(SCREAMING_SNAKE_CASE__				)

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


if __name__ == "__main__":
 main()




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowercase_           =	datasets.utils.logging.get_logger(__name__)


class        A_					(       folder_based_builder.FolderBasedBuilderConfig    ):




      '''simple docstring'''


      __snake_case					       =   None
      __snake_case					       =   None



class        A_					(       folder_based_builder.FolderBasedBuilder    ):




      '''simple docstring'''


      __snake_case					       =   datasets.Image()
      __snake_case					       =   """image"""
      __snake_case					       =   ImageFolderConfig
      __snake_case					       =   42  # definition at the bottom of the script
      __snake_case					       =   ImageClassification(image_column="""image"""					,      label_column="""label"""    )



lowercase_           =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
lowercase_           =	IMAGE_EXTENSIONS

