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

from transformers import BigBirdTokenizerFast


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







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_=0						,      lowerCAmelCase_=None       )	->	int:
     return [" ".join(x.split(''' '''       )[begin:end]       ) for x in answers if len(x.split(''' '''       )       ) > 1]







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_=False       )	->	List[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"
          a  :    str				=						(
              given_answers + get_sub_answers(lowerCAmelCase_						,      begin=1       ) + get_sub_answers(lowerCAmelCase_						,      end=-1       )
          )
     a  :    List[str]				=						[]
     for answer in given_answers:
          a  :    List[str]				=						answer.replace('''_'''						,      ''' '''       ).lower()
          a  :    str				=						''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias       )
          answers.append(''' '''.join(alias.split()       ).strip()       )
     return set(lowerCAmelCase_       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=1						,      lowerCAmelCase_=1_0_0       )	->	List[Any]:
     a  :    Union[str, Any]				=						jax.lax.top_k(lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    str				=						jax.lax.top_k(lowerCAmelCase_						,      lowerCAmelCase_       )

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

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







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Union[str, Any]:
     a  :    Union[str, Any]				=						sample['''question''']['''text''']
     a  :    str				=						sample['''document''']['''tokens''']['''token''']
     a  :    Any				=						sample['''document''']['''tokens''']['''is_html''']
     a  :    Dict				=						sample['''annotations''']['''long_answer''']
     a  :    Optional[Any]				=						sample['''annotations''']['''short_answers''']

     a  :    List[str]				=						''' '''.join([context[i] for i in range(len(lowerCAmelCase_       )       ) 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",
               }

     a  :    int				=						[]
     for s in short_answers:
          short_targets.extend(s['''text''']       )
     a  :    str				=						list(set(lowerCAmelCase_       )       )

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

     a  :    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 	UpperCAmelCase       (		)	->	Tuple:
     a  :    Any				=						load_from_disk('''natural-questions-validation'''       )
     a  :    Dict				=						dataset.map(lowerCAmelCase_       ).remove_columns(['''annotations''', '''document''', '''id''']       )
     print(lowerCAmelCase_       )

     a  :    Dict				=						dataset.filter(lambda lowerCAmelCase_       : (len(x['''question''']       ) + len(x['''context''']       )) < 4 * 4_0_9_6       )
     a  :    Optional[Any]				=						short_validation_dataset.filter(lambda lowerCAmelCase_       : x["category"] != "null"       )
     short_validation_dataset

     a  :    Any				=						'''vasudevgupta/flax-bigbird-natural-questions'''
     a  :    Optional[Any]				=						FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCAmelCase_       )
     a  :    List[str]				=						BigBirdTokenizerFast.from_pretrained(lowerCAmelCase_       )

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

     def evaluate(lowerCAmelCase_       ):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          a  :    Union[str, Any]				=						tokenizer(
              example['''question''']						,      example['''context''']						,      return_tensors='''np'''						,      max_length=4_0_9_6						,      padding='''max_length'''						,      truncation=lowerCAmelCase_						,      )

          a  :    Optional[int]				=						forward(**lowerCAmelCase_       )

          a  :    List[str]				=						CATEGORY_MAPPING[category.item()]

          a  :    Union[str, Any]				=						example['''long'''] + example['''short''']
          if example["category"] in ["yes", "no", "null"]:
               a  :    Optional[Any]				=						[example['''category''']]
          a  :    int				=						example['''category'''] != '''null'''
          # Now target can be: "yes", "no", "null", "list of long & short answers"

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

          a  :    Dict				=						3_8 if predicted_category == '''short''' else 1_0_2_4
          a  :    Optional[int]				=						get_best_valid_start_end_idx(
              start_scores[0]						,      end_scores[0]						,      top_k=8						,      max_size=lowerCAmelCase_       )

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

          a  :    List[Any]				=						expand_to_aliases(example['''targets''']						,      make_sub_answers=lowerCAmelCase_       )
          a  :    List[Any]				=						expand_to_aliases(example['''output''']       )

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

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

          return example

     a  :    Union[str, Any]				=						short_validation_dataset.map(lowerCAmelCase_       )

     a  :    str				=						len(lowerCAmelCase_       )
     a  :    int				=						len(short_validation_dataset.filter(lambda lowerCAmelCase_       : x["match"] == 1       )       )
     print('''EM score:'''						,      (matched / total) * 1_0_0						,      '''%'''       )


if __name__ == "__main__":
  main()
# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    """--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"""
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/