

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

from transformers import BigBirdTokenizerFast


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





def  _snake_case		(snake_case_					:     str    ,       snake_case_					:     str=0    ,       snake_case_					:     Any=None  )							->		str:
  return [" ".join(x.split(' '  )[begin:end]  ) for x in answers if len(x.split(' '  )  ) > 1]





def  _snake_case		(snake_case_					:     Optional[Any]    ,       snake_case_					:     Optional[int]=False  )							->		Optional[Any]:
  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	:  Any		=     (
        given_answers + get_sub_answers(snake_case_    ,       begin=1  ) + get_sub_answers(snake_case_    ,       end=-1  )
    )
  __UpperCAmelCase	:  List[Any]		=     []
  for answer in given_answers:
    __UpperCAmelCase	:  Tuple		=     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(snake_case_  )





def  _snake_case		(snake_case_					:     List[Any]    ,       snake_case_					:     Any    ,       snake_case_					:     Optional[Any]=1    ,       snake_case_					:     Optional[int]=100  )							->		Tuple:
  __UpperCAmelCase,			__UpperCAmelCase	:  Optional[int]		=     jax.lax.top_k(snake_case_    ,       snake_case_  )
  __UpperCAmelCase,			__UpperCAmelCase	:  Any		=     jax.lax.top_k(snake_case_    ,       snake_case_  )

  __UpperCAmelCase	:  Any		=     best_end_idx[:, None] - best_start_idx[None, :]
  __UpperCAmelCase	:  List[str]		=     jnp.logical_or(widths < 0    ,       widths > max_size  )
  __UpperCAmelCase	:  Optional[int]		=     (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
  __UpperCAmelCase	:  Optional[Any]		=     jnp.argmax(snake_case_  ).item()

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





def  _snake_case		(snake_case_					:     List[str]  )							->		Union[str, Any]:
  __UpperCAmelCase	:  Tuple		=     sample['question']['text']
  __UpperCAmelCase	:  Tuple		=     sample['document']['tokens']['token']
  __UpperCAmelCase	:  Tuple		=     sample['document']['tokens']['is_html']
  __UpperCAmelCase	:  List[str]		=     sample['annotations']['long_answer']
  __UpperCAmelCase	:  Optional[Any]		=     sample['annotations']['short_answers']

  __UpperCAmelCase	:  int		=     ' '.join([context[i] for i in range(len(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	:  int		=     list(set(snake_case_  )  )

  __UpperCAmelCase	:  str		=     []
  for s in long_answers:
    if s["start_token"] == -1:
      continue
    __UpperCAmelCase	:  int		=     context[s['start_token'] : s['end_token']]
    __UpperCAmelCase	:  Optional[int]		=     is_html[s['start_token'] : s['end_token']]
    __UpperCAmelCase	:  Optional[Any]		=     ' '.join([answer[i] for i in range(len(snake_case_  )  ) if not html[i]]  )
    if new_answer not in long_targets:
      long_targets.append(snake_case_  )

  __UpperCAmelCase	:  str		=     '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		()							->		Optional[Any]:
  __UpperCAmelCase	:  Optional[Any]		=     load_from_disk('natural-questions-validation'  )
  __UpperCAmelCase	:  Union[str, Any]		=     dataset.map(snake_case_  ).remove_columns(['annotations', 'document', 'id']  )
  print(snake_case_  )

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

  __UpperCAmelCase	:  Any		=     'vasudevgupta/flax-bigbird-natural-questions'
  __UpperCAmelCase	:  Dict		=     FlaxBigBirdForNaturalQuestions.from_pretrained(snake_case_  )
  __UpperCAmelCase	:  int		=     BigBirdTokenizerFast.from_pretrained(snake_case_  )

  @jax.jit
  def forward(*snake_case_					:     Union[str, Any]    ,       **snake_case_					:     str  ):
    __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     model(*snake_case_    ,       **snake_case_  )
    return start_logits, end_logits, jnp.argmax(snake_case_    ,       axis=-1  )

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

    __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     forward(**snake_case_  )

    __UpperCAmelCase	:  Optional[int]		=     CATEGORY_MAPPING[category.item()]

    __UpperCAmelCase	:  List[str]		=     example['long'] + example['short']
    if example["category"] in ["yes", "no", "null"]:
      __UpperCAmelCase	:  str		=     [example['category']]
    __UpperCAmelCase	:  Dict		=     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[int]		=     example['output'] == example['targets']
      __UpperCAmelCase	:  str		=     predicted_category != 'null'
      return example

    __UpperCAmelCase	:  Any		=     38 if predicted_category == 'short' else 1024
    __UpperCAmelCase,			__UpperCAmelCase	:  int		=     get_best_valid_start_end_idx(
        start_scores[0]    ,       end_scores[0]    ,       top_k=8    ,       max_size=snake_case_  )

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

    __UpperCAmelCase	:  List[Any]		=     expand_to_aliases(example['targets']    ,       make_sub_answers=snake_case_  )
    __UpperCAmelCase	:  List[Any]		=     expand_to_aliases(example['output']  )

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

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

    return example

  __UpperCAmelCase	:  List[str]		=     short_validation_dataset.map(snake_case_  )

  __UpperCAmelCase	:  List[str]		=     len(snake_case_  )
  __UpperCAmelCase	:  Tuple		=     len(short_validation_dataset.filter(lambda snake_case_  : x["match"] == 1  )  )
  print('EM score:'    ,       (matched / total) * 100    ,       '%'  )


if __name__ == "__main__":
      main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__			:  Tuple					     =     {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Tuple					     =     [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Optional[int]					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




