




'''simple docstring'''





import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           EfficientFormerForImageClassification,
           EfficientFormerForImageClassificationWithTeacher,
           EfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_efficientformer import (
           EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor






class 						lowercase     :






      def __init__(self ,  a__ ,  a__ = 1_3 ,  a__ = 6_4 ,  a__ = 2 ,  a__ = 3 ,  a__ = 3 ,  a__ = True ,  a__ = True ,  a__ = 1_2_8 ,  a__=[1_6, 3_2, 6_4, 1_2_8] ,  a__ = 7 ,  a__ = 4 ,  a__ = 3_7 ,  a__ = "gelu" ,  a__ = 0.1 ,  a__ = 0.1 ,  a__ = 1_0 ,  a__ = 0.02 ,  a__ = 2 ,  a__ = 1 ,  a__ = 1_2_8 ,  a__ = [2, 2, 2, 2] ,  a__ = 2 ,  a__ = 2 ,  )       ->   int:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= parent
         SCREAMING_SNAKE_CASE__			:				str       						= batch_size
         SCREAMING_SNAKE_CASE__			:				List[str]       						= image_size
         SCREAMING_SNAKE_CASE__			:				List[str]       						= patch_size
         SCREAMING_SNAKE_CASE__			:				List[str]       						= num_channels
         SCREAMING_SNAKE_CASE__			:				Any       						= is_training
         SCREAMING_SNAKE_CASE__			:				List[str]       						= use_labels
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= hidden_size
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= num_hidden_layers
         SCREAMING_SNAKE_CASE__			:				Any       						= num_attention_heads
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= intermediate_size
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= hidden_act
         SCREAMING_SNAKE_CASE__			:				Any       						= hidden_dropout_prob
         SCREAMING_SNAKE_CASE__			:				Dict       						= attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= type_sequence_label_size
         SCREAMING_SNAKE_CASE__			:				Tuple       						= initializer_range
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= encoder_stride
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= num_attention_outputs
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= embed_dim
         SCREAMING_SNAKE_CASE__			:				str       						= embed_dim + 1
         SCREAMING_SNAKE_CASE__			:				Tuple       						= resolution
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= depths
         SCREAMING_SNAKE_CASE__			:				Dict       						= hidden_sizes
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dim
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= mlp_expansion_ratio






      def    A__(self					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__			:				str       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

         SCREAMING_SNAKE_CASE__			:				Dict       						= None
         if self.use_labels:
            SCREAMING_SNAKE_CASE__			:				Tuple       						= ids_tensor([self.batch_size] ,  self.type_sequence_label_size					)

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.get_config()
         return config, pixel_values, labels






      def    A__(self					)       ->   Optional[Any]:
         return EfficientFormerConfig(
             image_size=self.image_size ,  patch_size=self.patch_size ,  num_channels=self.num_channels ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  is_decoder=a__ ,  initializer_range=self.initializer_range ,  encoder_stride=self.encoder_stride ,  resolution=self.resolution ,  depths=self.depths ,  hidden_sizes=self.hidden_sizes ,  dim=self.dim ,  mlp_expansion_ratio=self.mlp_expansion_ratio ,  )






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= EfficientFormerModel(config=a__					)
         model.to(a__					)
         model.eval()
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= model(a__					)
         self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)					)






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				str       						= self.type_sequence_label_size
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= EfficientFormerForImageClassification(a__					)
         model.to(a__					)
         model.eval()
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model(a__ ,  labels=a__					)
         self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)					)

         # test greyscale images
         SCREAMING_SNAKE_CASE__			:				Any       						= 1
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= EfficientFormerForImageClassification(a__					)
         model.to(a__					)
         model.eval()

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
         SCREAMING_SNAKE_CASE__			:				Dict       						= model(a__					)
         self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)					)






      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.prepare_config_and_inputs()
         (
             (
             SCREAMING_SNAKE_CASE__
         )						,				(
             SCREAMING_SNAKE_CASE__
         )						,				(
             SCREAMING_SNAKE_CASE__
         )						,				
         )			:				Optional[int]       						= config_and_inputs
         SCREAMING_SNAKE_CASE__			:				List[str]       						= {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class 						lowercase     (   UpperCAmelCase_				,			UpperCAmelCase_				,			unittest.TestCase):
      __lowercase 		=  (
          (
              EfficientFormerModel,
              EfficientFormerForImageClassificationWithTeacher,
              EfficientFormerForImageClassification,
          )
          if is_torch_available()
          else ()
      )
      __lowercase 		=  (
          {
              '''feature-extraction''': EfficientFormerModel,
              '''image-classification''': (
                  EfficientFormerForImageClassification,
                  EfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_torch_available()
          else {}
      )
      __lowercase 		=  False

      __lowercase 		=  False
      __lowercase 		=  False
      __lowercase 		=  False






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				Dict       						= EfficientFormerModelTester(self					)
         SCREAMING_SNAKE_CASE__			:				Dict       						= ConfigTester(
             self ,  config_class=a__ ,  has_text_modality=a__ ,  hidden_size=3_7					)






      def    A__(self					)       ->   Tuple:
         self.config_tester.run_common_tests()






      @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''					)
      def    A__(self					)       ->   Any:
         pass






      @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''					)
      def    A__(self					)       ->   Optional[int]:
         pass






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

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				str       						= model_class(a__					)
            SCREAMING_SNAKE_CASE__			:				Tuple       						= inspect.signature(model.forward					)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [*signature.parameters.keys()]

            SCREAMING_SNAKE_CASE__			:				int       						= ['''pixel_values''']
            self.assertListEqual(arg_names[:1] ,  a__					)






      def    A__(self					)       ->   List[Any]:
         def check_hidden_states_output(a__ ,  a__ ,  a__					):
            SCREAMING_SNAKE_CASE__			:				List[str]       						= model_class(a__					)
            model.to(a__					)
            model.eval()

            with torch.no_grad():
               SCREAMING_SNAKE_CASE__			:				List[str]       						= model(**self._prepare_for_class(a__ ,  a__					)					)

            SCREAMING_SNAKE_CASE__			:				int       						= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= getattr(
                self.model_tester ,  '''expected_num_hidden_layers''' ,  self.model_tester.num_hidden_layers + 1					)
            self.assertEqual(len(a__					) ,  a__					)

            if hasattr(self.model_tester ,  '''encoder_seq_length'''					):
               SCREAMING_SNAKE_CASE__			:				List[str]       						= self.model_tester.encoder_seq_length
               if hasattr(self.model_tester ,  '''chunk_length'''					) and self.model_tester.chunk_length > 1:
                  SCREAMING_SNAKE_CASE__			:				Any       						= seq_length * self.model_tester.chunk_length
            else:
               SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[-1].shape[-2:]					) ,  [seq_length, self.model_tester.hidden_size] ,  )

            if config.is_encoder_decoder:
               SCREAMING_SNAKE_CASE__			:				Optional[int]       						= outputs.decoder_hidden_states

               self.assertIsInstance(a__ ,  (list, tuple)					)
               self.assertEqual(len(a__					) ,  a__					)
               SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= getattr(self.model_tester ,  '''seq_length''' ,  a__					)
               SCREAMING_SNAKE_CASE__			:				Dict       						= getattr(self.model_tester ,  '''decoder_seq_length''' ,  a__					)

               self.assertListEqual(
                   list(hidden_states[-1].shape[-2:]					) ,  [decoder_seq_length, self.model_tester.hidden_size] ,  )

         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= True
            check_hidden_states_output(a__ ,  a__ ,  a__					)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= True

            check_hidden_states_output(a__ ,  a__ ,  a__					)






      def    A__(self ,  a__ ,  a__ ,  a__=False					)       ->   Union[str, Any]:
         SCREAMING_SNAKE_CASE__			:				int       						= super()._prepare_for_class(a__ ,  a__ ,  return_labels=a__					)

         if return_labels:
            if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
               del inputs_dict["labels"]

         return inputs_dict






      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__					)






      @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''					)
      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				Any       						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*a__					)






      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__					)






      def    A__(self					)       ->   List[Any]:
         if not self.model_tester.is_training:
            return

         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Tuple       						= self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE__			:				int       						= True

         for model_class in self.all_model_classes:
            # EfficientFormerForImageClassificationWithTeacher supports inference-only
            if (
                model_class in get_values(a__					)
                or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
            ):
               continue
            SCREAMING_SNAKE_CASE__			:				int       						= model_class(a__					)
            model.to(a__					)
            model.train()
            SCREAMING_SNAKE_CASE__			:				Dict       						= self._prepare_for_class(a__ ,  a__ ,  return_labels=a__					)
            SCREAMING_SNAKE_CASE__			:				Tuple       						= model(**a__					).loss
            loss.backward()






      def    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= [
             {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
             {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
             {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
         ]

         for model_class in self.all_model_classes:
            if (
                model_class
                not in [
                    *get_values(a__					),
                ]
                or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
            ):
               continue

            for problem_type in problem_types:
               with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}"""					):
                  SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= problem_type['''title''']
                  SCREAMING_SNAKE_CASE__			:				Tuple       						= problem_type['''num_labels''']

                  SCREAMING_SNAKE_CASE__			:				List[Any]       						= model_class(a__					)
                  model.to(a__					)
                  model.train()

                  SCREAMING_SNAKE_CASE__			:				List[str]       						= self._prepare_for_class(a__ ,  a__ ,  return_labels=a__					)

                  if problem_type["num_labels"] > 1:
                     SCREAMING_SNAKE_CASE__			:				List[str]       						= inputs['''labels'''].unsqueeze(1					).repeat(1 ,  problem_type['''num_labels''']					)

                  SCREAMING_SNAKE_CASE__			:				Dict       						= inputs['''labels'''].to(problem_type['''dtype''']					)

                  # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                  # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                  # they have the same size." which is a symptom something in wrong for the regression problem.
                  # See https://github.com/huggingface/transformers/issues/11780
                  with warnings.catch_warnings(record=a__					) as warning_list:
                     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= model(**a__					).loss
                  for w in warning_list:
                     if "Using a target size that is different to the input size" in str(w.message					):
                        raise ValueError(
                            f"""Something is going wrong in the regression problem: intercepted {w.message}"""					)

                  loss.backward()






      @slow
      def    A__(self					)       ->   Any:
         for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__			:				Tuple       						= EfficientFormerModel.from_pretrained(a__					)
            self.assertIsNotNone(a__					)






      def    A__(self					)       ->   Optional[int]:
         SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE__			:				Dict       						= True

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= getattr(self.model_tester ,  '''seq_length''' ,  a__					)
         SCREAMING_SNAKE_CASE__			:				Any       						= getattr(self.model_tester ,  '''encoder_seq_length''' ,  a__					)
         SCREAMING_SNAKE_CASE__			:				Dict       						= getattr(self.model_tester ,  '''key_length''' ,  a__					)
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= getattr(self.model_tester ,  '''chunk_length''' ,  a__					)

         if chunk_length is not None and hasattr(self.model_tester ,  '''num_hashes'''					):
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= encoder_seq_length * self.model_tester.num_hashes

         for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= True
            SCREAMING_SNAKE_CASE__			:				List[str]       						= False
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= True
            SCREAMING_SNAKE_CASE__			:				Any       						= model_class(a__					)
            model.to(a__					)
            model.eval()
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__			:				str       						= model(**self._prepare_for_class(a__ ,  a__					)					)
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(a__					) ,  self.model_tester.num_attention_outputs					)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            SCREAMING_SNAKE_CASE__			:				Any       						= True
            SCREAMING_SNAKE_CASE__			:				Tuple       						= model_class(a__					)
            model.to(a__					)
            model.eval()
            with torch.no_grad():
               SCREAMING_SNAKE_CASE__			:				int       						= model(**self._prepare_for_class(a__ ,  a__					)					)
            SCREAMING_SNAKE_CASE__			:				Any       						= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(a__					) ,  self.model_tester.num_attention_outputs					)

            if chunk_length is not None:
               self.assertListEqual(
                   list(attentions[0].shape[-4:]					) ,  [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] ,  )
            else:
               self.assertListEqual(
                   list(attentions[0].shape[-3:]					) ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] ,  )


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Dict       						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
   return image







@require_torch
@require_vision
class 						lowercase     (   unittest.TestCase):






      @cached_property
      def    A__(self					)       ->   int:
         return (
             EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''					)
             if is_vision_available()
             else None
         )






      @slow
      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				str       						= EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''					).to(
             a__					)

         SCREAMING_SNAKE_CASE__			:				Tuple       						= self.default_image_processor
         SCREAMING_SNAKE_CASE__			:				str       						= prepare_img()
         SCREAMING_SNAKE_CASE__			:				str       						= image_processor(images=a__ ,  return_tensors='''pt'''					).to(a__					)

         # forward pass
         with torch.no_grad():
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(**a__					)

         # verify the logits
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= (1, 1_0_0_0)
         self.assertEqual(outputs.logits.shape ,  a__					)

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= torch.tensor([-0.05_55, 0.48_25, -0.08_52]					).to(a__					)
         self.assertTrue(torch.allclose(outputs.logits[0][:3] ,  a__ ,  atol=1E-4					)					)






      @slow
      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				Dict       						= EfficientFormerForImageClassificationWithTeacher.from_pretrained(
             '''snap-research/efficientformer-l1-300'''					).to(a__					)

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.default_image_processor
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= prepare_img()
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= image_processor(images=a__ ,  return_tensors='''pt'''					).to(a__					)

         # forward pass
         with torch.no_grad():
            SCREAMING_SNAKE_CASE__			:				Tuple       						= model(**a__					)

         # verify the logits
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= (1, 1_0_0_0)
         self.assertEqual(outputs.logits.shape ,  a__					)

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= torch.tensor([-0.13_12, 0.43_53, -1.04_99]					).to(a__					)
         self.assertTrue(torch.allclose(outputs.logits[0][:3] ,  a__ ,  atol=1E-4					)					)







'''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)

