




'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       lowercase__			=	'''platform'''

       import jax
       import jax.numpy as jnp

       from transformers import MarianTokenizer
       from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


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




   '''simple docstring'''

   if attention_mask is None:
      SCREAMING_SNAKE_CASE__			:				Any       						= np.where(input_ids != config.pad_token_id	,     1	,     0   )
   if decoder_attention_mask is None:
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= np.where(decoder_input_ids != config.pad_token_id	,     1	,     0   )
   if head_mask is None:
      SCREAMING_SNAKE_CASE__			:				str       						= np.ones((config.encoder_layers, config.encoder_attention_heads)   )
   if decoder_head_mask is None:
      SCREAMING_SNAKE_CASE__			:				List[str]       						= np.ones((config.decoder_layers, config.decoder_attention_heads)   )
   if cross_attn_head_mask is None:
      SCREAMING_SNAKE_CASE__			:				Dict       						= np.ones((config.decoder_layers, config.decoder_attention_heads)   )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }






class 						lowercase     :






      def __init__(self ,  a__ ,  a__=1_3 ,  a__=7 ,  a__=True ,  a__=False ,  a__=9_9 ,  a__=1_6 ,  a__=2 ,  a__=4 ,  a__=4 ,  a__="gelu" ,  a__=0.1 ,  a__=0.1 ,  a__=3_2 ,  a__=2 ,  a__=1 ,  a__=0 ,  a__=0.02 ,  )       ->   Any:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= parent
         SCREAMING_SNAKE_CASE__			:				Any       						= batch_size
         SCREAMING_SNAKE_CASE__			:				Dict       						= seq_length
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= is_training
         SCREAMING_SNAKE_CASE__			:				int       						= use_labels
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= vocab_size
         SCREAMING_SNAKE_CASE__			:				Tuple       						= hidden_size
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= num_hidden_layers
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= num_attention_heads
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= intermediate_size
         SCREAMING_SNAKE_CASE__			:				Tuple       						= hidden_act
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= hidden_dropout_prob
         SCREAMING_SNAKE_CASE__			:				Dict       						= attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__			:				Dict       						= max_position_embeddings
         SCREAMING_SNAKE_CASE__			:				Tuple       						= eos_token_id
         SCREAMING_SNAKE_CASE__			:				Any       						= pad_token_id
         SCREAMING_SNAKE_CASE__			:				int       						= bos_token_id
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= initializer_range






      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size					) ,  3 ,  self.vocab_size					)
         SCREAMING_SNAKE_CASE__			:				str       						= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,  dtype=np.intaa					)) ,  -1					)

         SCREAMING_SNAKE_CASE__			:				str       						= shift_tokens_right(a__ ,  1 ,  2					)

         SCREAMING_SNAKE_CASE__			:				List[str]       						= MarianConfig(
             vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_id=self.eos_token_id ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  initializer_range=self.initializer_range ,  use_cache=a__ ,  )
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= prepare_marian_inputs_dict(a__ ,  a__ ,  a__					)
         return config, inputs_dict






      def    A__(self					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Tuple       						= self.prepare_config_and_inputs()
         return config, inputs_dict






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= 2_0
         SCREAMING_SNAKE_CASE__			:				List[str]       						= model_class_name(a__					)

         SCREAMING_SNAKE_CASE__			:				Tuple       						= model.encode(inputs_dict['''input_ids''']					)

         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Tuple       						= (
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         SCREAMING_SNAKE_CASE__			:				Tuple       						= model.init_cache(decoder_input_ids.shape[0] ,  a__ ,  a__					)
         SCREAMING_SNAKE_CASE__			:				Tuple       						= jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,  dtype='''i4'''					)

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )
         SCREAMING_SNAKE_CASE__			:				Any       						= model.decode(
             decoder_input_ids[:, :-1] ,  a__ ,  decoder_attention_mask=a__ ,  past_key_values=a__ ,  decoder_position_ids=a__ ,  )

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='''i4'''					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model.decode(
             decoder_input_ids[:, -1:] ,  a__ ,  decoder_attention_mask=a__ ,  past_key_values=outputs_cache.past_key_values ,  decoder_position_ids=a__ ,  )

         SCREAMING_SNAKE_CASE__			:				int       						= model.decode(a__ ,  a__					)

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
         self.parent.assertTrue(diff < 1E-3 ,  msg=f"""Max diff is {diff}"""					)






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				Dict       						= 2_0
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model_class_name(a__					)

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model.encode(inputs_dict['''input_ids''']					)

         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[str]       						= (
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         SCREAMING_SNAKE_CASE__			:				List[str]       						= jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
             ] ,  axis=-1 ,  )

         SCREAMING_SNAKE_CASE__			:				List[str]       						= model.init_cache(decoder_input_ids.shape[0] ,  a__ ,  a__					)
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model.decode(
             decoder_input_ids[:, :-1] ,  a__ ,  decoder_attention_mask=a__ ,  past_key_values=a__ ,  decoder_position_ids=a__ ,  )
         SCREAMING_SNAKE_CASE__			:				Tuple       						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='''i4'''					)
         SCREAMING_SNAKE_CASE__			:				Any       						= model.decode(
             decoder_input_ids[:, -1:] ,  a__ ,  past_key_values=outputs_cache.past_key_values ,  decoder_attention_mask=a__ ,  decoder_position_ids=a__ ,  )

         SCREAMING_SNAKE_CASE__			:				List[str]       						= model.decode(a__ ,  a__ ,  decoder_attention_mask=a__					)

         SCREAMING_SNAKE_CASE__			:				Any       						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
         self.parent.assertTrue(diff < 1E-3 ,  msg=f"""Max diff is {diff}"""					)






@require_flax
class 						lowercase     (   UpperCAmelCase_				,			unittest.TestCase				,			UpperCAmelCase_):
      __lowercase 		=  True
      __lowercase 		=  (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
      __lowercase 		=  (FlaxMarianMTModel,) if is_flax_available() else ()






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				str       						= FlaxMarianModelTester(self					)






      def    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				str       						= self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(a__ ,  a__ ,  a__					)






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(a__ ,  a__ ,  a__					)






      def    A__(self					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Dict       						= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__					):
               SCREAMING_SNAKE_CASE__			:				int       						= self._prepare_for_class(a__ ,  a__					)
               SCREAMING_SNAKE_CASE__			:				Any       						= model_class(a__					)

               @jax.jit
               def encode_jitted(a__ ,  a__=None ,  **a__					):
                  return model.encode(input_ids=a__ ,  attention_mask=a__					)

               with self.subTest('''JIT Enabled'''					):
                  SCREAMING_SNAKE_CASE__			:				int       						= encode_jitted(**a__					).to_tuple()

               with self.subTest('''JIT Disabled'''					):
                  with jax.disable_jit():
                     SCREAMING_SNAKE_CASE__			:				List[str]       						= encode_jitted(**a__					).to_tuple()

               self.assertEqual(len(a__					) ,  len(a__					)					)
               for jitted_output, output in zip(a__ ,  a__					):
                  self.assertEqual(jitted_output.shape ,  output.shape					)






      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Tuple       						= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__					):
               SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model_class(a__					)
               SCREAMING_SNAKE_CASE__			:				Tuple       						= model.encode(inputs_dict['''input_ids'''] ,  inputs_dict['''attention_mask''']					)

               SCREAMING_SNAKE_CASE__			:				int       						= {
                   '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                   '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                   '''encoder_outputs''': encoder_outputs,
               }

               @jax.jit
               def decode_jitted(a__ ,  a__ ,  a__					):
                  return model.decode(
                      decoder_input_ids=a__ ,  decoder_attention_mask=a__ ,  encoder_outputs=a__ ,  )

               with self.subTest('''JIT Enabled'''					):
                  SCREAMING_SNAKE_CASE__			:				int       						= decode_jitted(**a__					).to_tuple()

               with self.subTest('''JIT Disabled'''					):
                  with jax.disable_jit():
                     SCREAMING_SNAKE_CASE__			:				List[Any]       						= decode_jitted(**a__					).to_tuple()

               self.assertEqual(len(a__					) ,  len(a__					)					)
               for jitted_output, output in zip(a__ ,  a__					):
                  self.assertEqual(jitted_output.shape ,  output.shape					)






      @slow
      def    A__(self					)       ->   Optional[int]:
         for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				List[str]       						= model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''					)
            # FlaxMarianForSequenceClassification expects eos token in input_ids
            SCREAMING_SNAKE_CASE__			:				int       						= np.ones((1, 1)					) * model.config.eos_token_id
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model(a__					)
            self.assertIsNotNone(a__					)






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   unittest.TestCase):
      __lowercase 		=  None
      __lowercase 		=  None






      @classmethod
      def    A__(cls					)       ->   None:
         SCREAMING_SNAKE_CASE__			:				Any       						= f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
         return cls






      @cached_property
      def    A__(self					)       ->   str:
         return MarianTokenizer.from_pretrained(self.model_name					)






      @property
      def    A__(self					)       ->   int:
         return self.tokenizer.eos_token_id






      @cached_property
      def    A__(self					)       ->   Dict:
         SCREAMING_SNAKE_CASE__			:				FlaxMarianMTModel       						= FlaxMarianMTModel.from_pretrained(self.model_name					)
         self.assertEqual(model.config.decoder_start_token_id ,  model.config.pad_token_id					)
         return model






      def    A__(self ,  **a__					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.translate_src_text(**a__					)
         self.assertListEqual(self.expected_text ,  a__					)






      def    A__(self ,  **a__					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.tokenizer(self.src_text ,  padding=a__ ,  return_tensors='''np''' ,  **a__					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.model.generate(
             model_inputs.input_ids ,  attention_mask=model_inputs.attention_mask ,  num_beams=2 ,  max_length=1_2_8 ,  ).sequences
         SCREAMING_SNAKE_CASE__			:				int       						= self.tokenizer.batch_decode(a__ ,  skip_special_tokens=a__					)
         return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''en'''
      __lowercase 		=  '''fr'''
      __lowercase 		=  [
          '''I am a small frog.''',
          '''Now I can forget the 100 words of german that I know.''',
      ]
      __lowercase 		=  [
          '''Je suis une petite grenouille.''',
          '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
      ]






      @slow
      def    A__(self					)       ->   Union[str, Any]:
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''fr'''
      __lowercase 		=  '''en'''
      __lowercase 		=  [
          '''Donnez moi le micro.''',
          '''Tom et Mary étaient assis à une table.''',  # Accents
      ]
      __lowercase 		=  [
          '''Give me the microphone.''',
          '''Tom and Mary were sitting at a table.''',
      ]






      @slow
      def    A__(self					)       ->   Optional[Any]:
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''mt'''
      __lowercase 		=  '''en'''
      __lowercase 		=  ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
      __lowercase 		=  ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']






      @slow
      def    A__(self					)       ->   Optional[Any]:
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''en'''
      __lowercase 		=  '''de'''
      __lowercase 		=  [
          '''I am a small frog.''',
          '''Now I can forget the 100 words of german that I know.''',
          '''Tom asked his teacher for advice.''',
          '''That\'s how I would do it.''',
          '''Tom really admired Mary\'s courage.''',
          '''Turn around and close your eyes.''',
      ]
      __lowercase 		=  [
          '''Ich bin ein kleiner Frosch.''',
          '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
          '''Tom bat seinen Lehrer um Rat.''',
          '''So würde ich das machen.''',
          '''Tom bewunderte Marias Mut wirklich.''',
          '''Drehen Sie sich um und schließen Sie die Augen.''',
      ]






      @slow
      def    A__(self					)       ->   int:
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''en'''
      __lowercase 		=  '''zh'''
      __lowercase 		=  ['''My name is Wolfgang and I live in Berlin''']
      __lowercase 		=  ['''我叫沃尔夫冈 我住在柏林''']






      @slow
      def    A__(self					)       ->   List[Any]:
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''ru'''
      __lowercase 		=  '''fr'''
      __lowercase 		=  ['''Он показал мне рукопись своей новой пьесы.''']
      __lowercase 		=  ['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']






      @slow
      def    A__(self					)       ->   Dict:
         self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  '''en'''
      __lowercase 		=  '''ROMANCE'''
      __lowercase 		=  [
          '''>>fr<< Don\'t spend so much time watching TV.''',
          '''>>pt<< Your message has been sent.''',
          '''>>es<< He\'s two years older than me.''',
      ]
      __lowercase 		=  [
          '''Ne passez pas autant de temps à regarder la télé.''',
          '''A sua mensagem foi enviada.''',
          '''Es dos años más viejo que yo.''',
      ]






      @slow
      def    A__(self					)       ->   int:
         self._assert_generated_batch_equal_expected()







'''simple docstring'''





import argparse
import json
import re
import string
import sys
from collections import Counter


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   def remove_articles(snake_case   ):
      return re.sub(R'''\b(a|an|the)\b'''	,     ''' '''	,     snake_case   )

   def white_space_fix(snake_case   ):
      return " ".join(text.split()   )

   def remove_punc(snake_case   ):
      SCREAMING_SNAKE_CASE__			:				Optional[int]       						= set(string.punctuation   )
      return "".join(ch for ch in text if ch not in exclude   )

   def lower(snake_case   ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(snake_case   )   )   )   )


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Tuple       						= normalize_answer(snake_case   ).split()
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= normalize_answer(snake_case   ).split()
   SCREAMING_SNAKE_CASE__			:				Dict       						= Counter(snake_case   ) & Counter(snake_case   )
   SCREAMING_SNAKE_CASE__			:				int       						= sum(common.values()   )
   if num_same == 0:
      return 0
   SCREAMING_SNAKE_CASE__			:				Tuple       						= 1.0 * num_same / len(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Tuple       						= 1.0 * num_same / len(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= (2 * precision * recall) / (precision + recall)
   return fa


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   return normalize_answer(snake_case   ) == normalize_answer(snake_case   )


def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Any       						= []
   for ground_truth in ground_truths:
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= metric_fn(snake_case	,     snake_case   )
      scores_for_ground_truths.append(snake_case   )
   return max(snake_case   )


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				str       						= 0
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            SCREAMING_SNAKE_CASE__			:				Any       						= f"""Unanswered question {qa["id"]} will receive score 0."""
            print(snake_case	,     file=sys.stderr   )
            continue

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [x['''text'''] for x in qa['''answers''']]
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= predictions[qa['''id''']]

         SCREAMING_SNAKE_CASE__			:				List[str]       						= metric_max_over_ground_truths(snake_case	,     snake_case	,     snake_case   )
         if int(_exact_match   ) == 1:
            correct_ids.append(qa['''id''']   )
         exact_match += _exact_match

         fa += metric_max_over_ground_truths(snake_case	,     snake_case	,     snake_case   )

   SCREAMING_SNAKE_CASE__			:				int       						= exact_match / total
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= fa / total

   return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
       lowercase__			=	'''1.0'''
       lowercase__			=	argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
       parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
       parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
       parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
       lowercase__			=	parser.parse_args()

       with open(args.data_file) as data_file:
              lowercase__			=	json.load(data_file)
              if dataset_json["version"] != expected_version:
                     print(
                         F"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
                         file=sys.stderr,
                     )
              lowercase__			=	dataset_json['''data''']

       with open(args.pred_file) as pred_file:
              lowercase__			=	json.load(pred_file)

       lowercase__	,     lowercase__			=	evaluate(dataset, predictions)

       if args.output_correct_ids:
              print(F"Output {len(correct_ids)} correctly answered question IDs.")
              with open('''correct_ids.json''', '''w''') as f:
                     json.dump(correct_ids, f)

