





from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


__lowercase							:str							=	logging.get_logger(__name__)



class  _a  (					nn.Module				):






							"""simple docstring"""

							snake_case_							=    42
							snake_case_							=    jnp.floataa

							def   A_	(   self					:  List[Any]     )						->List[Any]:
													SCREAMING_SNAKE_CASE__  :					Any =       self.config.text_config
													SCREAMING_SNAKE_CASE__  :					Dict =       self.config.vision_config

													SCREAMING_SNAKE_CASE__  :					int =       self.config.projection_dim
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       text_config.hidden_size
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       vision_config.hidden_size

													SCREAMING_SNAKE_CASE__  :					str =       FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
													SCREAMING_SNAKE_CASE__  :					Tuple =       FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__				,						a     ).module_class

													SCREAMING_SNAKE_CASE__  :					List[Any] =       text_module(a				,						dtype=self.dtype     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       vision_module(a				,						dtype=self.dtype     )

													SCREAMING_SNAKE_CASE__  :					Dict =       nn.Dense(
													    self.projection_dim				,						dtype=self.dtype				,						kernel_init=jax.nn.initializers.normal(0.02     )				,						use_bias=a				,						)
													SCREAMING_SNAKE_CASE__  :					str =       nn.Dense(
													    self.projection_dim				,						dtype=self.dtype				,						kernel_init=jax.nn.initializers.normal(0.02     )				,						use_bias=a				,						)
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.param("logit_scale"				,						jax.nn.initializers.ones				,						[]     )






							def __call__(   self					:  int				,						a					:  Optional[int]=None				,						a					:  Tuple=None				,						a					:  Dict=None				,						a					:  List[Any]=None				,						a					:  Union[str, Any]=None				,						a					:  bool = True				,						a					:  Any=None				,						a					:  Optional[int]=None				,						a					:  List[str]=None				,						)						->Dict:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       return_dict if return_dict is not None else self.config.return_dict

													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.vision_model(
													    pixel_values=a				,						deterministic=a				,						output_attentions=a				,						output_hidden_states=a				,						return_dict=a				,						)

													SCREAMING_SNAKE_CASE__  :					List[str] =       self.text_model(
													    input_ids=a				,						attention_mask=a				,						token_type_ids=a				,						position_ids=a				,						deterministic=a				,						output_attentions=a				,						output_hidden_states=a				,						return_dict=a				,						)

													SCREAMING_SNAKE_CASE__  :					List[str] =       vision_outputs[1]
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.visual_projection(a     )

													SCREAMING_SNAKE_CASE__  :					List[str] =       text_outputs[1]
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.text_projection(a     )

													# normalized features
													SCREAMING_SNAKE_CASE__  :					Any =       image_embeds / jnp.linalg.norm(a				,						axis=-1				,						keepdims=a     )
													SCREAMING_SNAKE_CASE__  :					int =       text_embeds / jnp.linalg.norm(a				,						axis=-1				,						keepdims=a     )

													# cosine similarity as logits
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       jnp.exp(self.logit_scale     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       jnp.matmul(a				,						image_embeds.T     ) * logit_scale
													SCREAMING_SNAKE_CASE__  :					Tuple =       logits_per_text.T

													if not return_dict:
																			return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

													return FlaxCLIPOutput(
													    logits_per_image=a				,						logits_per_text=a				,						text_embeds=a				,						image_embeds=a				,						text_model_output=a				,						vision_model_output=a				,						)







class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    HybridCLIPConfig
							snake_case_							=    FlaxHybridCLIPModule

							def __init__(   self					:  Any				,						a					:  HybridCLIPConfig				,						a					:  Optional[Tuple] = None				,						a					:  int = 0				,						a					:  jnp.dtype = jnp.floataa				,						**a					:  str				,						)						->Union[str, Any]:
													if input_shape is None:
																			SCREAMING_SNAKE_CASE__  :					Any =       ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.module_class(config=a				,						dtype=a				,						**a     )
													super().__init__(a				,						a				,						input_shape=a				,						seed=a				,						dtype=a     )

							def   A_	(   self					:  int				,						a					:  jax.random.PRNGKey				,						a					:  Tuple				,						a					:  FrozenDict = None     )						->FrozenDict:
													# init input tensor
													SCREAMING_SNAKE_CASE__  :					Tuple =       jnp.zeros(input_shape[0]				,						dtype="i4"     )
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(a     ).shape[-1]     )				,						input_shape[0]     )
													SCREAMING_SNAKE_CASE__  :					str =       jnp.ones_like(a     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       jnp.ones_like(a     )

													SCREAMING_SNAKE_CASE__  :					Dict =       jax.random.normal(a				,						input_shape[1]     )

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Any =       jax.random.split(a     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       {"params": params_rng, "dropout": dropout_rng}

													return self.module.init(a				,						a				,						a				,						a				,						a				,						a     )["params"]

							def __call__(   self					:  Tuple				,						a					:  Dict				,						a					:  str				,						a					:  Any=None				,						a					:  Union[str, Any]=None				,						a					:  Union[str, Any]=None				,						a					:  dict = None				,						a					:  jax.random.PRNGKey = None				,						a					:  bool = False				,						a					:  Optional[bool] = None				,						a					:  Optional[bool] = None				,						a					:  Optional[bool] = None				,						)						->str:
													SCREAMING_SNAKE_CASE__  :					str =       output_attentions if output_attentions is not None else self.config.output_attentions
													SCREAMING_SNAKE_CASE__  :					List[Any] =       (
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													SCREAMING_SNAKE_CASE__  :					Any =       return_dict if return_dict is not None else self.config.return_dict

													if position_ids is None:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(a     ).shape[-1]     )				,						input_ids.shape     )

													if token_type_ids is None:
																			SCREAMING_SNAKE_CASE__  :					int =       jnp.zeros_like(a     )

													if attention_mask is None:
																			SCREAMING_SNAKE_CASE__  :					List[str] =       jnp.ones_like(a     )

													# Handle any PRNG if needed
													SCREAMING_SNAKE_CASE__  :					Tuple =       {}
													if dropout_rng is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       dropout_rng

													return self.module.apply(
													    {"params": params or self.params}				,						jnp.array(a				,						dtype="i4"     )				,						jnp.array(a				,						dtype=jnp.floataa     )				,						jnp.array(a				,						dtype="i4"     )				,						jnp.array(a				,						dtype="i4"     )				,						jnp.array(a				,						dtype="i4"     )				,						not train				,						a				,						a				,						a				,						rngs=a				,						)

							def   A_	(   self					:  List[str]				,						a					:  Optional[int]				,						a					:  Tuple=None				,						a					:  Optional[int]=None				,						a					:  Union[str, Any]=None				,						a					:  dict = None				,						a					:  jax.random.PRNGKey = None				,						a					:  List[Any]=False				,						)						->List[str]:
													if position_ids is None:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       jnp.broadcast_to(jnp.arange(jnp.atleast_ad(a     ).shape[-1]     )				,						input_ids.shape     )

													if token_type_ids is None:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       jnp.zeros_like(a     )

													if attention_mask is None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       jnp.ones_like(a     )

													# Handle any PRNG if needed
													SCREAMING_SNAKE_CASE__  :					int =       {}
													if dropout_rng is not None:
																			SCREAMING_SNAKE_CASE__  :					Any =       dropout_rng

													def _get_features(a					:  Dict				,						a					:  Optional[int]				,						a					:  Dict				,						a					:  Tuple				,						a					:  Optional[int]				,						a					:  List[Any]     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       module.text_model(
																			    input_ids=a				,						attention_mask=a				,						position_ids=a				,						token_type_ids=a				,						deterministic=a				,						)
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       text_outputs[1]
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       module.text_projection(a     )
																			return text_features

													return self.module.apply(
													    {"params": params or self.params}				,						jnp.array(a				,						dtype="i4"     )				,						jnp.array(a				,						dtype="i4"     )				,						jnp.array(a				,						dtype="i4"     )				,						jnp.array(a				,						dtype="i4"     )				,						not train				,						method=_get_features				,						rngs=a				,						)

							def   A_	(   self					:  Tuple				,						a					:  Tuple				,						a					:  dict = None				,						a					:  jax.random.PRNGKey = None				,						a					:  int=False     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					int =       {}
													if dropout_rng is not None:
																			SCREAMING_SNAKE_CASE__  :					str =       dropout_rng

													def _get_features(a					:  Dict				,						a					:  List[Any]				,						a					:  List[str]     ):
																			SCREAMING_SNAKE_CASE__  :					str =       module.vision_model(pixel_values=a				,						deterministic=a     )
																			SCREAMING_SNAKE_CASE__  :					str =       vision_outputs[1]  # pooled_output
																			SCREAMING_SNAKE_CASE__  :					str =       module.visual_projection(a     )
																			return image_features

													return self.module.apply(
													    {"params": params or self.params}				,						jnp.array(a				,						dtype=jnp.floataa     )				,						not train				,						method=_get_features				,						rngs=a				,						)






							@classmethod
							def   A_	(   cls					:  Any				,						a					:  str = None				,						a					:  str = None				,						*a					:  Optional[int]				,						**a					:  Any				,						)						->FlaxPreTrainedModel:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       {
													    argument[len("text_"     ) :]: value for argument, value in kwargs.items() if argument.startswith("text_"     )
													}

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       {
													    argument[len("vision_"     ) :]: value for argument, value in kwargs.items() if argument.startswith("vision_"     )
													}

													# remove text, vision kwargs from kwargs
													for key in kwargs_text.keys():
																			del kwargs["text_" + key]
													for key in kwargs_vision.keys():
																			del kwargs["vision_" + key]

													# Load and initialize the text and vision model
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       kwargs_text.pop("model"				,						a     )
													if text_model is None:
																			assert (
																			    text_model_name_or_path is not None
																			), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
																			from transformers import FlaxAutoModel

																			if "config" not in kwargs_text:
																									from transformers import AutoConfig

																									SCREAMING_SNAKE_CASE__  :					Tuple =       AutoConfig.from_pretrained(a     )
																									SCREAMING_SNAKE_CASE__  :					Dict =       text_config

																			SCREAMING_SNAKE_CASE__  :					Dict =       FlaxAutoModel.from_pretrained(a				,						*a				,						**a     )

													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       kwargs_vision.pop("model"				,						a     )
													if vision_model is None:
																			assert (
																			    vision_model_name_or_path is not None
																			), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
																			from transformers import FlaxAutoModel

																			if "config" not in kwargs_vision:
																									from transformers import AutoConfig

																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       AutoConfig.from_pretrained(a     )
																									SCREAMING_SNAKE_CASE__  :					List[Any] =       vision_config

																			SCREAMING_SNAKE_CASE__  :					Dict =       FlaxAutoModel.from_pretrained(a				,						*a				,						**a     )

													# instantiate config with corresponding kwargs
													SCREAMING_SNAKE_CASE__  :					Dict =       kwargs.pop("dtype"				,						jnp.floataa     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       HybridCLIPConfig.from_text_vision_configs(text_model.config				,						vision_model.config				,						**a     )

													# init model
													SCREAMING_SNAKE_CASE__  :					Any =       cls(a				,						*a				,						dtype=a				,						**a     )

													if vision_config.model_type == "clip":
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       vision_model.params["vision_model"]
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       vision_model.params["visual_projection"]["kernel"]
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       vision_model.params

													SCREAMING_SNAKE_CASE__  :					List[Any] =       text_model.params

													return model






import argparse
import json
import re
import string
import sys

import numpy as np


__lowercase							:Optional[Any]							=	0.5







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       [".", ",", ";", ":"]

						for token in remove_tokens:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.replace(_lowerCamelCase      , ""  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       prediction.replace(_lowerCamelCase      , ""  )

						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       ground_truth.lower(), prediction.lower()
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Dict =       ground_truth.replace("/"      , " "  ), prediction.replace("/"      , " "  )
						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       set(ground_truth.split(" "  )  ), set(prediction.split(" "  )  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       ground_truth.intersection(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       ground_truth.union(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       len(_lowerCamelCase  ) / len(_lowerCamelCase  )
						return jaccard







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						def remove_articles(_lowerCamelCase   :	Any  ):
												return re.sub(r"\b(a|an|the)\b"      , " "      , _lowerCamelCase  )

						def white_space_fix(_lowerCamelCase   :	Any  ):
												return " ".join(text.split()  )

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

						def lower(_lowerCamelCase   :	str  ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase  )  )  )  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Tuple  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       0, 0, 0

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       "Parties" in qa_id

						# first check if ground truth is empty
						if len(_lowerCamelCase  ) == 0:
												if len(_lowerCamelCase  ) > 0:
																		fp += len(_lowerCamelCase  )  # false positive for each one
						else:
												for ground_truth in ground_truths:
																		assert len(_lowerCamelCase  ) > 0
																		# check if there is a match
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for pred in predictions:
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					str =       True

																		if match_found:
																								tp += 1
																		else:
																								fn += 1

        # now also get any fps by looping through preds
												for pred in predictions:
																		# Check if there's a match. if so, don't count (don't want to double count based on the above)
																		# but if there's no match, then this is a false positive.
																		# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
																		# multiple predictions that are matched with the same answer.)
																		SCREAMING_SNAKE_CASE__  :					Tuple =       False
																		for ground_truth in ground_truths:
																								assert len(_lowerCamelCase  ) > 0
																								if substr_ok:
																														SCREAMING_SNAKE_CASE__  :					Tuple =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
																								else:
																														SCREAMING_SNAKE_CASE__  :					Dict =       get_jaccard(_lowerCamelCase      , _lowerCamelCase  ) >= IOU_THRESH
																								if is_match:
																														SCREAMING_SNAKE_CASE__  :					List[Any] =       True

																		if not match_found:
																								fp += 1

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tp / (tp + fp) if tp + fp > 0 else np.nan
						SCREAMING_SNAKE_CASE__  :					Dict =       tp / (tp + fn) if tp + fn > 0 else np.nan

						return precision, recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					int =       precisions[::-1]
						for i in range(1      , len(_lowerCamelCase  )  ):
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       max(precision_best[i - 1]      , precision_best[i]  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       precision_best[::-1]
						return precisions







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict      , _lowerCamelCase   :	str  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Tuple =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       np.trapz(_lowerCamelCase      , _lowerCamelCase  )
						if np.isnan(_lowerCamelCase  ):
												return 0
						return aupr







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       process_precisions(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[str] =       0
						for prec, recall in zip(_lowerCamelCase      , _lowerCamelCase  ):
												if recall >= recall_thresh:
																		SCREAMING_SNAKE_CASE__  :					Optional[int] =       prec
																		break
						return prec_at_recall







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						return normalize_answer(_lowerCamelCase  ) == normalize_answer(_lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       0
						for pred in predictions:
												for ground_truth in ground_truths:
																		SCREAMING_SNAKE_CASE__  :					Optional[Any] =       metric_fn(_lowerCamelCase      , _lowerCamelCase  )
																		if score == 1:  # break the loop when one prediction matches the ground truth
																								break
												if score == 1:
																		break
						return score







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[int]      , _lowerCamelCase   :	int  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Any =       0
						SCREAMING_SNAKE_CASE__  :					List[str] =       []
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       "Unanswered question " + qa["id"] + " will receive score 0."
																														print(_lowerCamelCase      , file=sys.stderr  )
																														continue
																								SCREAMING_SNAKE_CASE__  :					Any =       [x["text"] for x in qa["answers"]]
																								SCREAMING_SNAKE_CASE__  :					int =       predictions[qa["id"]]
																								SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       compute_precision_recall(_lowerCamelCase      , _lowerCamelCase      , qa["id"]  )

																								precisions.append(_lowerCamelCase  )
																								recalls.append(_lowerCamelCase  )

																								if precision == 0 and recall == 0:
																														fa += 0
																								else:
																														fa += 2 * (precision * recall) / (precision + recall)

																								exact_match += metric_max_over_ground_truths(_lowerCamelCase      , _lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       [x for _, x in sorted(zip(_lowerCamelCase      , _lowerCamelCase  )  )]
						recalls.sort()

						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       1_0_0.0 * fa / total
						SCREAMING_SNAKE_CASE__  :					Dict =       1_0_0.0 * exact_match / total
						SCREAMING_SNAKE_CASE__  :					str =       get_aupr(_lowerCamelCase      , _lowerCamelCase  )

						SCREAMING_SNAKE_CASE__  :					Optional[int] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.9  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       get_prec_at_recall(_lowerCamelCase      , _lowerCamelCase      , recall_thresh=0.8  )

						return {
						    "exact_match": exact_match,
						    "f1": fa,
						    "aupr": aupr,
						    "prec_at_80_recall": prec_at_aa_recall,
						    "prec_at_90_recall": prec_at_aa_recall,
						}


if __name__ == "__main__":
				__lowercase							:Tuple							=	argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__lowercase							:Tuple							=	parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__lowercase							:Union[str, Any]							=	json.load(dataset_file)
								__lowercase							:Union[str, Any]							=	dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__lowercase							:Optional[Any]							=	json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))