





'''simple docstring'''






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCamelCase      :   Optional[int]								=     logging.get_logger(__name__)

__UpperCamelCase      :   Dict								=     {
    '''shi-labs/oneformer_ade20k_swin_tiny''': (
        '''https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'''
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	__magic_name__							   =		"""oneformer"""
	__magic_name__							   =		{"""hidden_size""": """hidden_dim"""}




	def __init__(					self   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = 2_5_5   ,     UpperCAmelCase__ = 1_5_0   ,     UpperCAmelCase__ = 0.1   ,     UpperCAmelCase__ = 2.0   ,     UpperCAmelCase__ = 5.0   ,     UpperCAmelCase__ = 5.0   ,     UpperCAmelCase__ = 0.5   ,     UpperCAmelCase__ = 0.0_7   ,     UpperCAmelCase__ = 1_2_5_4_4   ,     UpperCAmelCase__ = 3.0   ,     UpperCAmelCase__ = 0.7_5   ,     UpperCAmelCase__ = 0.0_2   ,     UpperCAmelCase__ = 1.0   ,     UpperCAmelCase__ = 1e-05   ,     UpperCAmelCase__ = False   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = [4, 8, 1_6, 3_2]   ,     UpperCAmelCase__ = 7_7   ,     UpperCAmelCase__ = 2_5_6   ,     UpperCAmelCase__ = 7_7   ,     UpperCAmelCase__ = 6   ,     UpperCAmelCase__ = 4_9_4_0_8   ,     UpperCAmelCase__ = 2   ,     UpperCAmelCase__ = 1_6   ,     UpperCAmelCase__ = 2_5_6   ,     UpperCAmelCase__ = 2_5_6   ,     UpperCAmelCase__ = 2_5_6   ,     UpperCAmelCase__ = 1_0_2_4   ,     UpperCAmelCase__ = "GN"   ,     UpperCAmelCase__ = 6   ,     UpperCAmelCase__ = 1_0   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = 8   ,     UpperCAmelCase__ = 0.1   ,     UpperCAmelCase__ = 2_0_4_8   ,     UpperCAmelCase__ = False   ,     UpperCAmelCase__ = False   ,     UpperCAmelCase__ = 2   ,     UpperCAmelCase__ = 4   ,     **UpperCAmelCase__   ,     )       ->				Any:
					if backbone_config is None:
									logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''			)
									_A :				Optional[int]				=  CONFIG_MAPPING['''swin'''](
									    image_size=2_2_4   ,     in_channels=3   ,     patch_size=4   ,     embed_dim=9_6   ,     depths=[2, 2, 6, 2]   ,     num_heads=[3, 6, 1_2, 2_4]   ,     window_size=7   ,     drop_path_rate=0.3   ,     use_absolute_embeddings=UpperCAmelCase__   ,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']   ,     )
					elif isinstance(UpperCAmelCase__   ,     UpperCAmelCase__			):
									_A :				Any				=  backbone_config.get('''model_type'''			)
									_A :				Tuple				=  CONFIG_MAPPING[backbone_model_type]
									_A :				Any				=  config_class.from_dict(UpperCAmelCase__			)

					_A :				List[Any]				=  backbone_config

					_A :				Dict				=  ignore_value
					_A :				Union[str, Any]				=  num_queries
					_A :				Any				=  no_object_weight
					_A :				Any				=  class_weight
					_A :				Optional[Any]				=  mask_weight
					_A :				Optional[int]				=  dice_weight
					_A :				List[str]				=  contrastive_weight
					_A :				str				=  contrastive_temperature
					_A :				List[Any]				=  train_num_points
					_A :				Optional[int]				=  oversample_ratio
					_A :				Optional[Any]				=  importance_sample_ratio
					_A :				List[Any]				=  init_std
					_A :				Optional[int]				=  init_xavier_std
					_A :				Union[str, Any]				=  layer_norm_eps
					_A :				Any				=  is_training
					_A :				str				=  use_auxiliary_loss
					_A :				List[Any]				=  output_auxiliary_logits
					_A :				List[Any]				=  strides
					_A :				str				=  task_seq_len
					_A :				List[str]				=  text_encoder_width
					_A :				Optional[Any]				=  text_encoder_context_length
					_A :				Tuple				=  text_encoder_num_layers
					_A :				List[str]				=  text_encoder_vocab_size
					_A :				List[Any]				=  text_encoder_proj_layers
					_A :				int				=  text_encoder_n_ctx
					_A :				Dict				=  conv_dim
					_A :				Union[str, Any]				=  mask_dim
					_A :				List[str]				=  hidden_dim
					_A :				int				=  encoder_feedforward_dim
					_A :				int				=  norm
					_A :				str				=  encoder_layers
					_A :				Union[str, Any]				=  decoder_layers
					_A :				Optional[Any]				=  use_task_norm
					_A :				Any				=  num_attention_heads
					_A :				Any				=  dropout
					_A :				Tuple				=  dim_feedforward
					_A :				List[str]				=  pre_norm
					_A :				Any				=  enforce_input_proj
					_A :				Dict				=  query_dec_layers
					_A :				str				=  common_stride
					_A :				Optional[Any]				=  decoder_layers

					super().__init__(**UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				Dict[str, any]:
					_A :				List[str]				=  copy.deepcopy(self.__dict__			)
					_A :				int				=  self.backbone_config.to_dict()
					_A :				Optional[Any]				=  self.__class__.model_type
					return output







'''simple docstring'''






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





def      lowercase  (  lowerCAmelCase :						int):



				"""simple docstring"""


				def remove_articles(lowerCAmelCase :						List[Any]):
								return re.sub(R'''\b(a|an|the)\b'''   , ''' '''   , lowerCAmelCase)

				def white_space_fix(lowerCAmelCase :						Tuple):
								return " ".join(text.split())

				def remove_punc(lowerCAmelCase :						str):
								_A :				List[Any]				=  set(string.punctuation)
								return "".join(ch for ch in text if ch not in exclude)

				def lower(lowerCAmelCase :						Any):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase))))





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Dict):



				"""simple docstring"""


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





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						str):



				"""simple docstring"""


				return normalize_answer(lowerCAmelCase) == normalize_answer(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				_A :				Optional[int]				=  []
				for ground_truth in ground_truths:
								_A :				Union[str, Any]				=  metric_fn(lowerCAmelCase   , lowerCAmelCase)
								scores_for_ground_truths.append(lowerCAmelCase)
				return max(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						str):



				"""simple docstring"""


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

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

												_A :				Optional[Any]				=  metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
												if int(_exact_match) == 1:
																correct_ids.append(qa['''id'''])
												exact_match += _exact_match

												fa += metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				_A :				Dict				=  exact_match / total
				_A :				Dict				=  fa / total

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


if __name__ == "__main__":
	__UpperCamelCase      :   List[str]								=     '''1.0'''
	__UpperCamelCase      :   Union[str, Any]								=     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.''')
	__UpperCamelCase      :   Optional[int]								=     parser.parse_args()

	with open(args.data_file) as data_file:
		__UpperCamelCase      :   Optional[int]								=     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,
			)
		__UpperCamelCase      :   Optional[Any]								=     dataset_json['''data''']

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

	__UpperCamelCase							,  __UpperCamelCase      :   List[str]								=     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)
