


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






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






def    lowerCAmelCase__ (       lowerCamelCase  :    Dict						,lowerCamelCase  :    Tuple=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"
              _A  :			str											=      (
                  given_answers + get_sub_answers(lowerCamelCase						,begin=1     ) + get_sub_answers(lowerCamelCase						,end=-1     )
              )
       _A  :			List[Any]											=      []
       for answer in given_answers:
              _A  :			List[str]											=      answer.replace('_'						,' '     ).lower()
              _A  :			Any											=      ''.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    lowerCAmelCase__ (       lowerCamelCase  :    List[Any]						,lowerCamelCase  :    Optional[int]						,lowerCamelCase  :    List[str]=1						,lowerCamelCase  :    Optional[int]=100     ):
       _A						,				_A  :			Dict											=      jax.lax.top_k(lowerCamelCase						,lowerCamelCase     )
       _A						,				_A  :			Dict											=      jax.lax.top_k(lowerCamelCase						,lowerCamelCase     )

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

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






def    lowerCAmelCase__ (       lowerCamelCase  :    Dict     ):
       _A  :			Optional[Any]											=      sample['question']['text']
       _A  :			Any											=      sample['document']['tokens']['token']
       _A  :			List[str]											=      sample['document']['tokens']['is_html']
       _A  :			str											=      sample['annotations']['long_answer']
       _A  :			int											=      sample['annotations']['short_answers']

       _A  :			Optional[int]											=      ' '.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  :			Tuple											=      []
       for s in short_answers:
              short_targets.extend(s['text']     )
       _A  :			Dict											=      list(set(lowerCamelCase     )     )

       _A  :			List[str]											=      []
       for s in long_answers:
              if s["start_token"] == -1:
                     continue
              _A  :			Tuple											=      context[s['start_token'] : s['end_token']]
              _A  :			Optional[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  :			List[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    lowerCAmelCase__ (       ):
       _A  :			Optional[Any]											=      load_from_disk('natural-questions-validation'     )
       _A  :			int											=      dataset.map(lowerCamelCase     ).remove_columns(['annotations', 'document', 'id']     )
       print(lowerCamelCase     )

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

       _A  :			Tuple											=      'vasudevgupta/flax-bigbird-natural-questions'
       _A  :			List[str]											=      FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase     )
       _A  :			str											=      BigBirdTokenizerFast.from_pretrained(lowerCamelCase     )

       @jax.jit
       def forward(*lowerCamelCase  :    Optional[Any]						,**lowerCamelCase  :    Any     ):
              _A						,				_A						,				_A  :			Tuple											=      model(*lowerCamelCase						,**lowerCamelCase     )
              return start_logits, end_logits, jnp.argmax(lowerCamelCase						,axis=-1     )

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

              _A						,				_A						,				_A  :			Tuple											=      forward(**lowerCamelCase     )

              _A  :			List[Any]											=      CATEGORY_MAPPING[category.item()]

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

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

              _A  :			Dict											=      38 if predicted_category == 'short' else 1024
              _A						,				_A  :			List[Any]											=      get_best_valid_start_end_idx(
                  start_scores[0]						,end_scores[0]						,top_k=8						,max_size=lowerCamelCase     )

              _A  :			Optional[Any]											=      inputs['input_ids'][0].tolist()
              _A  :			Union[str, Any]											=      [tokenizer.decode(input_ids[start_score : end_score + 1]     )]

              _A  :			Union[str, Any]											=      expand_to_aliases(example['targets']						,make_sub_answers=lowerCamelCase     )
              _A  :			str											=      expand_to_aliases(example['output']     )

              # some preprocessing to both prediction and answer
              _A  :			Tuple											=      {''.join(a.split()     ) for a in answers}
              _A  :			Optional[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  :			str											=      short_validation_dataset.map(lowerCamelCase     )

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


if __name__ == "__main__":
   main()










'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


A   :  Optional[Any]       		=				-1 if torch_device == '''cpu''' else 0


@is_pipeline_test
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""
    a									=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    a									=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			str											=      SummarizationPipeline(model=SCREAMING_SNAKE_CASE   ,							tokenizer=SCREAMING_SNAKE_CASE)
           return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Optional[int]):
           _A  :			str											=      summarizer.model

           _A  :			Tuple											=      summarizer('(CNN)The Palestinian Authority officially became')
           self.assertEqual(SCREAMING_SNAKE_CASE   ,							[{'summary_text': ANY(SCREAMING_SNAKE_CASE)}])

           _A  :			Optional[Any]											=      summarizer(
               '(CNN)The Palestinian Authority officially became '   ,							num_beams=2   ,							min_length=2   ,							max_length=5   ,							)
           self.assertEqual(SCREAMING_SNAKE_CASE   ,							[{'summary_text': ANY(SCREAMING_SNAKE_CASE)}])

           # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
           _A  :			Union[str, Any]											=      [
               'SwitchTransformersConfig',
               'T5Config',
               'LongT5Config',
               'LEDConfig',
               'PegasusXConfig',
               'FSMTConfig',
               'M2M100Config',
               'ProphetNetConfig',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
           ]
           if model.config.__class__.__name__ not in model_can_handle_longer_seq:
                  # Too long and exception is expected.
                  # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
                  # the embedding layer.
                  if not (
                      isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                      and get_gpu_count() > 0
                      and len(summarizer.model.trainable_weights) > 0
                  ):
                         with self.assertRaises(SCREAMING_SNAKE_CASE):
                                _A  :			Any											=      summarizer('This ' * 1000)
           _A  :			int											=      summarizer('This ' * 1000   ,							truncation=TruncationStrategy.ONLY_FIRST)


    @require_torch
    def      A					(						self    :							int):
           _A  :			Tuple											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='pt')
           _A  :			List[str]											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_tf
    def      A					(						self    :							int):
           _A  :			Optional[Any]											=      pipeline(task='summarization'   ,							model='sshleifer/tiny-mbart'   ,							framework='tf')
           _A  :			Any											=      summarizer('This is a small test')
           self.assertEqual(
               SCREAMING_SNAKE_CASE   ,							[
                   {
                       'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
                   }
               ]   ,							)


    @require_torch
    @slow
    def      A					(						self    :							List[Any]):
           _A  :			int											=      pipeline(task='summarization'   ,							device=SCREAMING_SNAKE_CASE)
           _A  :			int											=      (
               ' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'
               ' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'
               ' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'
               ' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'
               ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
               ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
               ' situation in Palestinian territories, paving the way for possible war crimes investigations against'
               ' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'
               ' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'
               ' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'
               ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
               ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
               ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
               ' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'
               ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
               ' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'
               ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
               ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
               ' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'
               ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
               ' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'
               ' decision to join a treaty to which over 100 countries around the world are members." In January, when'
               ' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'
               ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
               ' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'
               ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
               ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
               ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
               ' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'
               ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
               ' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'
               ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
               ' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'
               ' will include alleged war crimes committed since June. The International Criminal Court was set up in'
               ' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'
               ' and Faith Karimi contributed to this report.'
           )
           _A  :			Optional[Any]											=      (
               ' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'
               ' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'
               ' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'
               ' says governments seeking to penalize Palestine should end pressure .'
           )
           _A  :			Union[str, Any]											=      summarizer(SCREAMING_SNAKE_CASE)
           self.assertEqual(result[0]['summary_text']   ,							SCREAMING_SNAKE_CASE)






