code: 
  '''simple docstring'''
  
  
  
  
  
  import sys
  from dataclasses import dataclass
  from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
  
  import pandas as pd
  import pyarrow as pa
  
  import datasets
  import datasets.config
  from datasets.features.features import require_storage_cast
  from datasets.table import table_cast
  
  
  if TYPE_CHECKING:
       import sqlitea
  
       import sqlalchemy
  
  
  A         =   datasets.utils.logging.get_logger(__name__)
  
  
  
  @dataclass
  class  lowerCAmelCase__   (       datasets.BuilderConfig       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= None
    _UpperCamelCase						= None
    _UpperCamelCase						= None
    _UpperCamelCase						= True
    _UpperCamelCase						= None
    _UpperCamelCase						= None
    _UpperCamelCase						= None
    _UpperCamelCase						= 1_0000
    _UpperCamelCase						= None
  
    def 		_lowerCamelCase	(self  : int    )   ->       Tuple:
  
  
  
          if self.sql is None:
                raise ValueError("sql must be specified"    )
          if self.con is None:
                raise ValueError("con must be specified"    )
  
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Tuple = None	, )   ->       str:
          snake_case_						       =							config_kwargs.copy()
          # We need to stringify the Selectable object to make its hash deterministic
  
          # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
          snake_case_						       =							config_kwargs["sql"]
          if not isinstance(lowerCamelCase__	, lowerCamelCase__    ):
                if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                      import sqlalchemy
  
                      if isinstance(lowerCamelCase__	, sqlalchemy.sql.Selectable    ):
                            snake_case_						       =							sqlalchemy.create_engine(config_kwargs["con"].split("://"    )[0] + "://"    )
                            snake_case_						       =							str(sql.compile(dialect=engine.dialect    )    )
                            snake_case_						       =							sql_str
                      else:
                            raise TypeError(
                                f'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase__    )}: {sql}'''    )
                else:
                      raise TypeError(
                          f'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase__    )}: {sql}'''    )
          snake_case_						       =							config_kwargs["con"]
          if not isinstance(lowerCamelCase__	, lowerCamelCase__    ):
                snake_case_						       =							id(lowerCamelCase__    )
                logger.info(
                    f'''SQL connection \'con\' of type {type(lowerCamelCase__    )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''    )
  
          return super().create_config_id(lowerCamelCase__	, custom_features=lowerCamelCase__    )
  
  
    @property
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Union[str, Any]:
          snake_case_						       =							{
              "index_col": self.index_col,
              "columns": self.columns,
              "params": self.params,
              "coerce_float": self.coerce_float,
              "parse_dates": self.parse_dates,
          }
          return pd_read_sql_kwargs
  
  
  
  class  lowerCAmelCase__   (       datasets.ArrowBasedBuilder       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= SqlConfig
  
    def 		_lowerCamelCase	(self  : Any    )   ->       Tuple:
          return datasets.DatasetInfo(features=self.config.features    )
  
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : Any    )   ->       List[Any]:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN	, gen_kwargs={}    )]
  
    def 		_lowerCamelCase	(self  : Any	, lowerCAmelCase_  : str    )   ->       pa.Table:
  
  
  
          if self.config.features is not None:
                snake_case_						       =							self.config.features.arrow_schema
                if all(not require_storage_cast(lowerCamelCase__    ) for feature in self.config.features.values()    ):
                      # cheaper cast
                      snake_case_						       =							pa.Table.from_arrays([pa_table[field.name] for field in schema]	, schema=lowerCamelCase__    )
                else:
                      # more expensive cast; allows str <-> int/float or str to Audio for example
                      snake_case_						       =							table_cast(lowerCamelCase__	, lowerCamelCase__    )
          return pa_table
  
  
    def 		_lowerCamelCase	(self  : str    )   ->       List[Any]:
          snake_case_						       =							self.config.chunksize
          snake_case_						       =							pd.read_sql(
              self.config.sql	, self.config.con	, chunksize=lowerCamelCase__	, **self.config.pd_read_sql_kwargs    )
          snake_case_						       =							[sql_reader] if chunksize is None else sql_reader
          for chunk_idx, df in enumerate(lowerCamelCase__    ):
                snake_case_						       =							pa.Table.from_pandas(lowerCamelCase__    )
                yield chunk_idx, self._cast_table(lowerCamelCase__    )
  
  
style_context: 
  '''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     :							Any ,					_a     :							Union[str, Any]=0 ,					_a     :							Optional[int]=None			):
        return [" ".join(x.split(" "			)[begin:end]			) for x in answers if len(x.split(" "			)			) > 1]
  def   snake_case_       (							_a     :							Optional[int] ,					_a     :							str=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"
              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     :							Optional[Any] ,					_a     :							int ,					_a     :							Optional[Any]=1 ,					_a     :							List[Any]=100			):
        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			):
        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_       (							):
        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 * 4_096			)
        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     :							List[Any] ,					**_a     :							int			):
              snake_case_       ,	snake_case_       ,	snake_case_						       =							model(*_a ,					**_a			)
              return start_logits, end_logits, jnp.argmax(_a ,					axis=-1			)
  
        def evaluate(_a     :							List[Any]			):
              # 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=4_096 ,					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 1_024
              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()
  
  