




'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase__								=						logging.get_logger(__name__)

lowercase__								=						{
    '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json''',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''mbart'''
      __lowercase					=	['''past_key_values''']
      __lowercase					=	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






      def __init__(self			,	a__=5_0_2_6_5			,	a__=1_0_2_4			,	a__=1_2			,	a__=4_0_9_6			,	a__=1_6			,	a__=1_2			,	a__=4_0_9_6			,	a__=1_6			,	a__=0.0			,	a__=0.0			,	a__=True			,	a__=True			,	a__="gelu"			,	a__=1_0_2_4			,	a__=0.1			,	a__=0.0			,	a__=0.0			,	a__=0.02			,	a__=0.0			,	a__=False			,	a__=1			,	a__=0			,	a__=2			,	a__=2			,	**a__			,	)							-> int:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			vocab_size
         SCREAMING_SNAKE_CASE__      :					int							    =			max_position_embeddings
         SCREAMING_SNAKE_CASE__      :					str							    =			d_model
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			encoder_ffn_dim
         SCREAMING_SNAKE_CASE__      :					str							    =			encoder_layers
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			encoder_attention_heads
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			decoder_ffn_dim
         SCREAMING_SNAKE_CASE__      :					str							    =			decoder_layers
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			decoder_attention_heads
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			dropout
         SCREAMING_SNAKE_CASE__      :					Dict							    =			attention_dropout
         SCREAMING_SNAKE_CASE__      :					Dict							    =			activation_dropout
         SCREAMING_SNAKE_CASE__      :					str							    =			activation_function
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			init_std
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			encoder_layerdrop
         SCREAMING_SNAKE_CASE__      :					str							    =			decoder_layerdrop
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			classifier_dropout
         SCREAMING_SNAKE_CASE__      :					Dict							    =			use_cache
         SCREAMING_SNAKE_CASE__      :					Any							    =			encoder_layers
         SCREAMING_SNAKE_CASE__      :					Any							    =			scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=a__			,	bos_token_id=a__			,	eos_token_id=a__			,	is_encoder_decoder=a__			,	forced_eos_token_id=a__			,	**a__			,	)






class     lowercase  ( UpperCAmelCase_):






      @property
      def  A__(self       )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ]       )

            if self.use_past:
               SCREAMING_SNAKE_CASE__      :					str							    =			{0: '''batch'''}
               SCREAMING_SNAKE_CASE__      :					int							    =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            else:
               SCREAMING_SNAKE_CASE__      :					str							    =			{0: '''batch''', 1: '''decoder_sequence'''}
               SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{0: '''batch''', 1: '''decoder_sequence'''}

            if self.use_past:
               self.fill_with_past_key_values_(a__			,	direction='''inputs'''       )
         elif self.task == "causal-lm":
            # TODO: figure this case out.
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ]       )
            if self.use_past:
               SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.num_layers
               for i in range(a__       ):
                  SCREAMING_SNAKE_CASE__      :					Tuple							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
                  SCREAMING_SNAKE_CASE__      :					List[Any]							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                    ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                ]       )

         return common_inputs






      @property
      def  A__(self       )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			super().outputs
         else:
            SCREAMING_SNAKE_CASE__      :					Dict							    =			super(a__			,	self       ).outputs
            if self.use_past:
               SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.num_layers
               for i in range(a__       ):
                  SCREAMING_SNAKE_CASE__      :					int							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
                  SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__			,	a__			,	a__			,	a__			,	a__       )

         # Generate decoder inputs
         SCREAMING_SNAKE_CASE__      :					str							    =			seq_length if not self.use_past else 1
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__			,	a__			,	a__			,	a__			,	a__       )
         SCREAMING_SNAKE_CASE__      :					int							    =			{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         SCREAMING_SNAKE_CASE__      :					int							    =			dict(**a__			,	**a__       )

         if self.use_past:
            if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
            else:
               import torch
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			common_inputs['''input_ids'''].shape
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			common_inputs['''decoder_input_ids'''].shape[1]
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.num_attention_heads
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            SCREAMING_SNAKE_CASE__      :					str							    =			decoder_seq_length + 3
            SCREAMING_SNAKE_CASE__      :					int							    =			(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.cat(
                [common_inputs['''decoder_attention_mask'''], torch.ones(a__			,	a__       )]			,	dim=1       )

            SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.num_layers
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			min(a__			,	a__       )
            SCREAMING_SNAKE_CASE__      :					Dict							    =			max(a__			,	a__       ) - min_num_layers
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

            for _ in range(a__       ):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(a__       ),
                       torch.zeros(a__       ),
                       torch.zeros(a__       ),
                       torch.zeros(a__       ),
                   )       )
            # TODO: test this.
            SCREAMING_SNAKE_CASE__      :					str							    =			encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
            for _ in range(a__			,	a__       ):
               common_inputs["past_key_values"].append((torch.zeros(a__       ), torch.zeros(a__       ))       )
         return common_inputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__			,	a__			,	a__			,	a__			,	a__       )

         if self.use_past:
            if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
            else:
               import torch
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					int							    =			common_inputs['''input_ids'''].shape
            # Not using the same length for past_key_values
            SCREAMING_SNAKE_CASE__      :					Dict							    =			seqlen + 2
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.num_layers
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.num_attention_heads
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			common_inputs['''attention_mask'''].dtype
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			torch.cat(
                [common_inputs['''attention_mask'''], torch.ones(a__			,	a__			,	dtype=a__       )]			,	dim=1       )
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[
                (torch.zeros(a__       ), torch.zeros(a__       )) for _ in range(a__       )
            ]
         return common_inputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			compute_effective_axis_dimension(
             a__			,	fixed_dimension=OnnxConfig.default_fixed_batch			,	num_token_to_add=0       )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			tokenizer.num_special_tokens_to_add(a__       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			compute_effective_axis_dimension(
             a__			,	fixed_dimension=OnnxConfig.default_fixed_sequence			,	num_token_to_add=a__       )

         # Generate dummy inputs according to compute batch and sequence
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[''' '''.join([tokenizer.unk_token]       ) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE__      :					str							    =			dict(tokenizer(a__			,	return_tensors=a__       )       )
         return common_inputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                a__			,	batch_size=a__			,	seq_length=a__			,	is_pair=a__			,	framework=a__       )

         elif self.task == "causal-lm":
            SCREAMING_SNAKE_CASE__      :					Dict							    =			self._generate_dummy_inputs_for_causal_lm(
                a__			,	batch_size=a__			,	seq_length=a__			,	is_pair=a__			,	framework=a__       )
         else:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                a__			,	batch_size=a__			,	seq_length=a__			,	is_pair=a__			,	framework=a__       )

         return common_inputs






      def  A__(self			,	a__			,	a__			,	a__			,	a__       )							-> Tuple:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					Any							    =			super()._flatten_past_key_values_(a__			,	a__			,	a__			,	a__       )
         else:
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			super(a__			,	self       )._flatten_past_key_values_(
                a__			,	a__			,	a__			,	a__       )







'''simple docstring'''





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

from transformers import BigBirdTokenizerFast


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


def 					UpperCamelCase						(	snake_case						,  snake_case=0						,  snake_case=None ):




   '''simple docstring'''

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


def 					UpperCamelCase						(	snake_case						,  snake_case=False ):




   '''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__      :					Tuple							    =			(
          given_answers + get_sub_answers(snake_case						,  begin=1 ) + get_sub_answers(snake_case						,  end=-1 )
      )
   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for answer in given_answers:
      SCREAMING_SNAKE_CASE__      :					int							    =			answer.replace('''_'''						,  ''' ''' ).lower()
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			''''''.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 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case=1						,  snake_case=1_00 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			jax.lax.top_k(snake_case						,  snake_case )
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			jax.lax.top_k(snake_case						,  snake_case )

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

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


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			sample['''question''']['''text''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			sample['''document''']['''tokens''']['''token''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''document''']['''tokens''']['''is_html''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''annotations''']['''long_answer''']
   SCREAMING_SNAKE_CASE__      :					str							    =			sample['''annotations''']['''short_answers''']

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.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",
         }

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
   for s in short_answers:
      short_targets.extend(s['''text'''] )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			list(set(snake_case ) )

   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for s in long_answers:
      if s["start_token"] == -1:
         continue
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			context[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					Dict							    =			is_html[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.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 )

   SCREAMING_SNAKE_CASE__      :					int							    =			'''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						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			load_from_disk('''natural-questions-validation''' )
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			dataset.map(snake_case ).remove_columns(['''annotations''', '''document''', '''id'''] )
   print(snake_case )

   SCREAMING_SNAKE_CASE__      :					Dict							    =			dataset.filter(lambda snake_case : (len(x['''question'''] ) + len(x['''context'''] )) < 4 * 40_96 )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			short_validation_dataset.filter(lambda snake_case : x["category"] != "null" )
   short_validation_dataset

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

   @jax.jit
   def forward(*snake_case						,  **snake_case ):
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			model(*snake_case						,  **snake_case )
      return start_logits, end_logits, jnp.argmax(snake_case						,  axis=-1 )

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

      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			forward(**snake_case )

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			CATEGORY_MAPPING[category.item()]

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

      SCREAMING_SNAKE_CASE__      :					Any							    =			38 if predicted_category == '''short''' else 10_24
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			get_best_valid_start_end_idx(
          start_scores[0]						,  end_scores[0]						,  top_k=8						,  max_size=snake_case )

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

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

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

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

      return example

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			short_validation_dataset.map(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			len(snake_case )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(short_validation_dataset.filter(lambda snake_case : x["match"] == 1 ) )
   print('''EM score:'''						,  (matched / total) * 1_00						,  '''%''' )


if __name__ == "__main__":
       main()

