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


def SCREAMING_SNAKE_CASE				(     lowercase_:	Any		,						lowercase_:	List[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"
              _UpperCamelCase     :       int  						=  (
                  given_answers + get_sub_answers(_UpperCAmelCase		,						begin=1				) + get_sub_answers(_UpperCAmelCase		,						end=-1				)
              )
       _UpperCamelCase     :       Dict  						=  []
       for answer in given_answers:
              _UpperCamelCase     :       int  						=  answer.replace('_'		,						' '				).lower()
              _UpperCamelCase     :       Optional[int]  						=  ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias				)
              answers.append(' '.join(alias.split()				).strip()				)
       return set(_UpperCAmelCase				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	Tuple		,						lowercase_:	Dict		,						lowercase_:	int=1		,						lowercase_:	Optional[Any]=100				):
       _UpperCamelCase     :       Optional[Any]  						=  jax.lax.top_k(_UpperCAmelCase		,						_UpperCAmelCase				)
       _UpperCamelCase     :       List[Any]  						=  jax.lax.top_k(_UpperCAmelCase		,						_UpperCAmelCase				)

       _UpperCamelCase     :       Any  						=  best_end_idx[:, None] - best_start_idx[None, :]
       _UpperCamelCase     :       Union[str, Any]  						=  jnp.logical_or(widths < 0		,						widths > max_size				)
       _UpperCamelCase     :       str  						=  (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
       _UpperCamelCase     :       Tuple  						=  jnp.argmax(_UpperCAmelCase				).item()

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


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[str]				):
       _UpperCamelCase     :       Tuple  						=  sample['question']['text']
       _UpperCamelCase     :       int  						=  sample['document']['tokens']['token']
       _UpperCamelCase     :       str  						=  sample['document']['tokens']['is_html']
       _UpperCamelCase     :       Dict  						=  sample['annotations']['long_answer']
       _UpperCamelCase     :       Union[str, Any]  						=  sample['annotations']['short_answers']

       _UpperCamelCase     :       Any  						=  ' '.join([context[i] for i in range(len(_UpperCAmelCase				)				) 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     :       Tuple  						=  []
       for s in short_answers:
              short_targets.extend(s['text']				)
       _UpperCamelCase     :       Any  						=  list(set(_UpperCAmelCase				)				)

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

       _UpperCamelCase     :       Dict  						=  '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 SCREAMING_SNAKE_CASE				(     ):
       _UpperCamelCase     :       Union[str, Any]  						=  load_from_disk('natural-questions-validation'				)
       _UpperCamelCase     :       Tuple  						=  dataset.map(_UpperCAmelCase				).remove_columns(['annotations', 'document', 'id']				)
       print(_UpperCAmelCase				)

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

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

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

       def evaluate(lowercase_:	Union[str, Any]				):
              # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
              _UpperCamelCase     :       List[str]  						=  tokenizer(
                  example['question']		,						example['context']		,						return_tensors='np'		,						max_length=4096		,						padding='max_length'		,						truncation=_UpperCAmelCase		,						)

              _UpperCamelCase     :       Optional[int]  						=  forward(**_UpperCAmelCase				)

              _UpperCamelCase     :       str  						=  CATEGORY_MAPPING[category.item()]

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

              _UpperCamelCase     :       Optional[Any]  						=  38 if predicted_category == 'short' else 1024
              _UpperCamelCase     :       str  						=  get_best_valid_start_end_idx(
                  start_scores[0]		,						end_scores[0]		,						top_k=8		,						max_size=_UpperCAmelCase				)

              _UpperCamelCase     :       Optional[int]  						=  inputs['input_ids'][0].tolist()
              _UpperCamelCase     :       str  						=  [tokenizer.decode(input_ids[start_score : end_score + 1]				)]

              _UpperCamelCase     :       Optional[int]  						=  expand_to_aliases(example['targets']		,						make_sub_answers=_UpperCAmelCase				)
              _UpperCamelCase     :       List[Any]  						=  expand_to_aliases(example['output']				)

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

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

              return example

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

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


if __name__ == "__main__":
 main()




from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims



