code: 
  
  
  
  
  '''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					)					)
  
  
style_context: 
  
  
  
  
  '''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)
  
  