

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


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  int=0							,				_UpperCamelCase		:  Dict=None       )   ->    Optional[int]:
     """simple docstring"""




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


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  Union[str, Any]=False       )   ->    List[str]:
     """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"
          _SCREAMING_SNAKE_CASE												=(
              given_answers + get_sub_answers(_UpperCamelCase							,				begin=1       ) + get_sub_answers(_UpperCamelCase							,				end=-1       )
          )
     _SCREAMING_SNAKE_CASE												=[]
     for answer in given_answers:
          _SCREAMING_SNAKE_CASE												=answer.replace('_'							,				' '       ).lower()
          _SCREAMING_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(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  Any=1							,				_UpperCamelCase		:  Union[str, Any]=1_00       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=jax.lax.top_k(_UpperCamelCase							,				_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=jax.lax.top_k(_UpperCamelCase							,				_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=best_end_idx[:, None] - best_start_idx[None, :]
     _SCREAMING_SNAKE_CASE												=jnp.logical_or(widths < 0							,				widths > max_size       )
     _SCREAMING_SNAKE_CASE												=(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
     _SCREAMING_SNAKE_CASE												=jnp.argmax(_UpperCamelCase       ).item()

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


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]       )   ->    Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=sample['question']['text']
     _SCREAMING_SNAKE_CASE												=sample['document']['tokens']['token']
     _SCREAMING_SNAKE_CASE												=sample['document']['tokens']['is_html']
     _SCREAMING_SNAKE_CASE												=sample['annotations']['long_answer']
     _SCREAMING_SNAKE_CASE												=sample['annotations']['short_answers']

     _SCREAMING_SNAKE_CASE												=' '.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",
               }

     _SCREAMING_SNAKE_CASE												=[]
     for s in short_answers:
          short_targets.extend(s['text']       )
     _SCREAMING_SNAKE_CASE												=list(set(_UpperCamelCase       )       )

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

     _SCREAMING_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  _lowerCAmelCase      (		)   ->    Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=load_from_disk('natural-questions-validation'       )
     _SCREAMING_SNAKE_CASE												=dataset.map(_UpperCamelCase       ).remove_columns(['annotations', 'document', 'id']       )
     print(_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=dataset.filter(lambda _UpperCamelCase       : (len(x['question']       ) + len(x['context']       )) < 4 * 40_96       )
     _SCREAMING_SNAKE_CASE												=short_validation_dataset.filter(lambda _UpperCamelCase       : x["category"] != "null"       )
     short_validation_dataset

     _SCREAMING_SNAKE_CASE												='vasudevgupta/flax-bigbird-natural-questions'
     _SCREAMING_SNAKE_CASE												=FlaxBigBirdForNaturalQuestions.from_pretrained(_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=BigBirdTokenizerFast.from_pretrained(_UpperCamelCase       )

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

     def evaluate(_UpperCamelCase		:  Optional[Any]       ):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          _SCREAMING_SNAKE_CASE												=tokenizer(
              example['question']							,				example['context']							,				return_tensors='np'							,				max_length=40_96							,				padding='max_length'							,				truncation=_UpperCamelCase							,				)

          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=forward(**_UpperCamelCase       )

          _SCREAMING_SNAKE_CASE												=CATEGORY_MAPPING[category.item()]

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

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

          _SCREAMING_SNAKE_CASE												=38 if predicted_category == 'short' else 10_24
          _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=get_best_valid_start_end_idx(
              start_scores[0]							,				end_scores[0]							,				top_k=8							,				max_size=_UpperCamelCase       )

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

          _SCREAMING_SNAKE_CASE												=expand_to_aliases(example['targets']							,				make_sub_answers=_UpperCamelCase       )
          _SCREAMING_SNAKE_CASE												=expand_to_aliases(example['output']       )

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

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

          return example

     _SCREAMING_SNAKE_CASE												=short_validation_dataset.map(_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=len(_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=len(short_validation_dataset.filter(lambda _UpperCamelCase       : x["match"] == 1       )       )
     print('EM score:'							,				(matched / total) * 1_00							,				'%'       )


if __name__ == "__main__":
     main()



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase				:   Dict      				=  logging.get_logger(__name__)

lowerCamelCase				:   Dict      				=  {
    "uw-madison/nystromformer-512": "https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}







class 							A__					( A__						):
  A__					=       'nystromformer'





  def __init__(	self   :			List[str]							,      _a   :			Dict=3_0000							,      _a   :			Dict=768							,      _a   :			Union[str, Any]=12							,      _a   :			Optional[int]=12							,      _a   :			Union[str, Any]=3072							,      _a   :			Optional[Any]="gelu_new"							,      _a   :			Dict=0.1							,      _a   :			Dict=0.1							,      _a   :			Union[str, Any]=510							,      _a   :			Any=2							,      _a   :			List[Any]=64							,      _a   :			int=64							,      _a   :			Optional[Any]=65							,      _a   :			str=False							,      _a   :			List[Any]=0.02							,      _a   :			List[str]=1e-5							,      _a   :			Optional[Any]=1							,      _a   :			Optional[Any]=0							,      _a   :			List[Any]=2							,      **_a   :			List[Any]							,      )			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=segment_means_seq_len
       _SCREAMING_SNAKE_CASE												=num_landmarks
       _SCREAMING_SNAKE_CASE												=conv_kernel_size
       _SCREAMING_SNAKE_CASE												=inv_coeff_init_option
       _SCREAMING_SNAKE_CASE												=layer_norm_eps
       super().__init__(pad_token_id=_a							,      bos_token_id=_a							,      eos_token_id=_a							,      **_a				)
