





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor

class      A_	:
	def __init__(   self							, _A							, _A=1_3							, _A=3_2							, _A=2							, _A=3							, _A=1_6							, _A=[1, 2, 1]							, _A=[2, 2, 4]							, _A=2							, _A=2.0							, _A=True							, _A=0.0							, _A=0.0							, _A=0.1							, _A="gelu"							, _A=False							, _A=True							, _A=0.02							, _A=1E-5							, _A=True							, _A=None							, _A=True							, _A=1_0							, _A=8							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    parent
				UpperCAmelCase       		=    batch_size
				UpperCAmelCase       		=    image_size
				UpperCAmelCase       		=    patch_size
				UpperCAmelCase       		=    num_channels
				UpperCAmelCase       		=    embed_dim
				UpperCAmelCase       		=    depths
				UpperCAmelCase       		=    num_heads
				UpperCAmelCase       		=    window_size
				UpperCAmelCase       		=    mlp_ratio
				UpperCAmelCase       		=    qkv_bias
				UpperCAmelCase       		=    hidden_dropout_prob
				UpperCAmelCase       		=    attention_probs_dropout_prob
				UpperCAmelCase       		=    drop_path_rate
				UpperCAmelCase       		=    hidden_act
				UpperCAmelCase       		=    use_absolute_embeddings
				UpperCAmelCase       		=    patch_norm
				UpperCAmelCase       		=    layer_norm_eps
				UpperCAmelCase       		=    initializer_range
				UpperCAmelCase       		=    is_training
				UpperCAmelCase       		=    scope
				UpperCAmelCase       		=    use_labels
				UpperCAmelCase       		=    type_sequence_label_size
				UpperCAmelCase       		=    encoder_stride
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase       		=    None
				if self.use_labels:
							UpperCAmelCase       		=    ids_tensor([self.batch_size]							, self.type_sequence_label_size		)

				UpperCAmelCase       		=    self.get_config()

				return config, pixel_values, labels
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return SwinConfig(
				    image_size=self.image_size							, patch_size=self.patch_size							, num_channels=self.num_channels							, embed_dim=self.embed_dim							, depths=self.depths							, num_heads=self.num_heads							, window_size=self.window_size							, mlp_ratio=self.mlp_ratio							, qkv_bias=self.qkv_bias							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, drop_path_rate=self.drop_path_rate							, hidden_act=self.hidden_act							, use_absolute_embeddings=self.use_absolute_embeddings							, path_norm=self.patch_norm							, layer_norm_eps=self.layer_norm_eps							, initializer_range=self.initializer_range							, encoder_stride=self.encoder_stride							, )
	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    TFSwinModel(config=_A		)
				UpperCAmelCase       		=    model(_A		)

				UpperCAmelCase       		=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
				UpperCAmelCase       		=    int(config.embed_dim * 2 ** (len(config.depths		) - 1)		)

				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, expected_seq_len, expected_dim)		)
	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    TFSwinForMaskedImageModeling(config=_A		)
				UpperCAmelCase       		=    model(_A		)
				self.parent.assertEqual(
				    result.logits.shape							, (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

				# test greyscale images
				UpperCAmelCase       		=    1
				UpperCAmelCase       		=    TFSwinForMaskedImageModeling(_A		)

				UpperCAmelCase       		=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				UpperCAmelCase       		=    model(_A		)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, 1, self.image_size, self.image_size)		)
	def 					_lowercase    (   self							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.type_sequence_label_size
				UpperCAmelCase       		=    TFSwinForImageClassification(_A		)
				UpperCAmelCase       		=    model(_A							, labels=_A		)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)		)

				# test greyscale images
				UpperCAmelCase       		=    1
				UpperCAmelCase       		=    TFSwinForImageClassification(_A		)
				UpperCAmelCase       		=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				UpperCAmelCase       		=    model(_A		)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.prepare_config_and_inputs()
				UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    config_and_inputs
				UpperCAmelCase       		=    {'''pixel_values''': pixel_values}
				return config, inputs_dict

@require_tf
class      A_	(a_					,							a_					,							unittest.TestCase      ):
	UpperCAmelCase__ =      (
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	UpperCAmelCase__ =      (
	    {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCAmelCase__ =      False
	UpperCAmelCase__ =      False
	UpperCAmelCase__ =      False
	UpperCAmelCase__ =      False
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    TFSwinModelTester(self		)
				UpperCAmelCase       		=    ConfigTester(self							, config_class=_A							, embed_dim=3_7		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*_A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_A		)
	@unittest.skip(reason='''Swin does not use inputs_embeds'''		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				pass
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    model_class(_A		)
							self.assertIsInstance(model.get_input_embeddings()							, tf.keras.layers.Layer		)
							UpperCAmelCase       		=    model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_A							, tf.keras.layers.Dense		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    model_class(_A		)
							UpperCAmelCase       		=    inspect.signature(model.call		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							UpperCAmelCase       		=    [*signature.parameters.keys()]

							UpperCAmelCase       		=    ['''pixel_values''']
							self.assertListEqual(arg_names[:1]							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase       		=    True

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    False
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    model_class(_A		)
							UpperCAmelCase       		=    model(**self._prepare_for_class(_A							, _A		)		)
							UpperCAmelCase       		=    outputs.attentions
							UpperCAmelCase       		=    len(self.model_tester.depths		)
							self.assertEqual(len(_A		)							, _A		)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    config.window_size**2
							UpperCAmelCase       		=    model_class(_A		)
							UpperCAmelCase       		=    model(**self._prepare_for_class(_A							, _A		)		)
							UpperCAmelCase       		=    outputs.attentions
							self.assertEqual(len(_A		)							, _A		)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]		)							, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							, )
							UpperCAmelCase       		=    len(_A		)

							# Check attention is always last and order is fine
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    True
							UpperCAmelCase       		=    model_class(_A		)
							UpperCAmelCase       		=    model(**self._prepare_for_class(_A							, _A		)		)

							if hasattr(self.model_tester							, '''num_hidden_states_types'''		):
										UpperCAmelCase       		=    self.model_tester.num_hidden_states_types
							else:
										# also another +1 for reshaped_hidden_states
										UpperCAmelCase       		=    2
							self.assertEqual(out_len + added_hidden_states							, len(_A		)		)

							UpperCAmelCase       		=    outputs.attentions

							self.assertEqual(len(_A		)							, _A		)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]		)							, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							, )
	def 					_lowercase    (   self							, _A							, _A							, _A							, _A		):






				'''simple docstring'''







				UpperCAmelCase       		=    model_class(_A		)
				UpperCAmelCase       		=    model(**self._prepare_for_class(_A							, _A		)		)
				UpperCAmelCase       		=    outputs.hidden_states

				UpperCAmelCase       		=    getattr(
				    self.model_tester							, '''expected_num_hidden_layers'''							, len(self.model_tester.depths		) + 1		)
				self.assertEqual(len(_A		)							, _A		)

				# Swin has a different seq_length
				UpperCAmelCase       		=    to_atuple(config.patch_size		)

				UpperCAmelCase       		=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]		)							, [num_patches, self.model_tester.embed_dim]							, )

				UpperCAmelCase       		=    outputs.reshaped_hidden_states
				self.assertEqual(len(_A		)							, _A		)

				UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    reshaped_hidden_states[0].shape

				UpperCAmelCase       		=    tf.reshape(reshaped_hidden_states[0]							, (batch_size, num_channels, height * width)		)
				UpperCAmelCase       		=    tf.transpose(_A							, (0, 2, 1)		)

				self.assertListEqual(
				    list(reshaped_hidden_states.shape[-2:]		)							, [num_patches, self.model_tester.embed_dim]							, )
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()

				UpperCAmelCase       		=    to_atuple(self.model_tester.image_size		)

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    True
							self.check_hidden_states_output(_A							, _A							, _A							, _A		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase       		=    True

							self.check_hidden_states_output(_A							, _A							, _A							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase		,      UpperCAmelCase       		=    self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase       		=    3

				UpperCAmelCase       		=    to_atuple(self.model_tester.image_size		)
				UpperCAmelCase       		=    to_atuple(config.patch_size		)

				UpperCAmelCase       		=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				UpperCAmelCase       		=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
							UpperCAmelCase       		=    True
							self.check_hidden_states_output(_A							, _A							, _A							, (padded_height, padded_width)		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase       		=    True
							self.check_hidden_states_output(_A							, _A							, _A							, (padded_height, padded_width)		)







	@slow
	def 					_lowercase    (   self		):






				'''simple docstring'''







				for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase       		=    TFSwinModel.from_pretrained(_A		)
							self.assertIsNotNone(_A		)

@require_vision
@require_tf
class      A_	(unittest.TestCase      ):
	@cached_property
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return (
				    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''		)
				    if is_vision_available()
				    else None
				)







	@slow
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''		)
				UpperCAmelCase       		=    self.default_image_processor

				UpperCAmelCase       		=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
				UpperCAmelCase       		=    image_processor(images=_A							, return_tensors='''tf'''		)

				# forward pass
				UpperCAmelCase       		=    model(_A		)

				# verify the logits
				UpperCAmelCase       		=    tf.TensorShape((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape							, _A		)
				UpperCAmelCase       		=    tf.constant([-0.09_48, -0.64_54, -0.09_21]		)
				self.assertTrue(np.allclose(outputs.logits[0, :3]							, _A							, atol=1E-4		)		)










import argparse
import json
import re
import string
import sys

import numpy as np


__A    :   Optional[Any]          =       0.5





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    ['''.''', ''',''', ''';''', ''':''']

			for token in remove_tokens:
						UpperCAmelCase       		=    ground_truth.replace(UpperCamelCase__      ,      ''''''			)
						UpperCAmelCase       		=    prediction.replace(UpperCamelCase__      ,      ''''''			)

			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.lower(), prediction.lower()
			UpperCAmelCase		,      UpperCAmelCase       		=    ground_truth.replace('''/'''      ,      ''' '''			), prediction.replace('''/'''      ,      ''' '''			)
			UpperCAmelCase		,      UpperCAmelCase       		=    set(ground_truth.split(''' '''			)			), set(prediction.split(''' '''			)			)

			UpperCAmelCase       		=    ground_truth.intersection(UpperCamelCase__			)
			UpperCAmelCase       		=    ground_truth.union(UpperCamelCase__			)
			UpperCAmelCase       		=    len(UpperCamelCase__			) / len(UpperCamelCase__			)
			return jaccard





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

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

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

			def remove_punc(UpperCamelCase__			):
						UpperCAmelCase       		=    set(string.punctuation			)
						return "".join(ch for ch in text if ch not in exclude			)

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

			return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__			)			)			)			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    0, 0, 0

			UpperCAmelCase       		=    '''Parties''' in qa_id

			# first check if ground truth is empty
			if len(UpperCamelCase__			) == 0:
						if len(UpperCamelCase__			) > 0:
									fp += len(UpperCamelCase__			)  # false positive for each one
			else:
						for ground_truth in ground_truths:
									assert len(UpperCamelCase__			) > 0
									# check if there is a match
									UpperCAmelCase       		=    False
									for pred in predictions:
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH
												if is_match:
															UpperCAmelCase       		=    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.)
									UpperCAmelCase       		=    False
									for ground_truth in ground_truths:
												assert len(UpperCamelCase__			) > 0
												if substr_ok:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH or ground_truth in pred
												else:
															UpperCAmelCase       		=    get_jaccard(UpperCamelCase__      ,      UpperCamelCase__			) >= IOU_THRESH
												if is_match:
															UpperCAmelCase       		=    True

									if not match_found:
												fp += 1

			UpperCAmelCase       		=    tp / (tp + fp) if tp + fp > 0 else np.nan
			UpperCAmelCase       		=    tp / (tp + fn) if tp + fn > 0 else np.nan

			return precision, recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

			UpperCAmelCase       		=    precisions[::-1]
			for i in range(1      ,      len(UpperCamelCase__			)			):
						UpperCAmelCase       		=    max(precision_best[i - 1]      ,      precision_best[i]			)
			UpperCAmelCase       		=    precision_best[::-1]
			return precisions





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    np.trapz(UpperCamelCase__      ,      UpperCamelCase__			)
			if np.isnan(UpperCamelCase__			):
						return 0
			return aupr





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    0
			for prec, recall in zip(UpperCamelCase__      ,      UpperCamelCase__			):
						if recall >= recall_thresh:
									UpperCAmelCase       		=    prec
									break
			return prec_at_recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    0
			for pred in predictions:
						for ground_truth in ground_truths:
									UpperCAmelCase       		=    metric_fn(UpperCamelCase__      ,      UpperCamelCase__			)
									if score == 1:  # break the loop when one prediction matches the ground truth
												break
						if score == 1:
									break
			return score





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    UpperCAmelCase       		=    UpperCAmelCase       		=    0
			UpperCAmelCase       		=    []
			UpperCAmelCase       		=    []
			for article in dataset:
						for paragraph in article["paragraphs"]:
									for qa in paragraph["qas"]:
												total += 1
												if qa["id"] not in predictions:
															UpperCAmelCase       		=    '''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
															print(UpperCamelCase__      ,      file=sys.stderr			)
															continue
												UpperCAmelCase       		=    [x['''text'''] for x in qa['''answers''']]
												UpperCAmelCase       		=    predictions[qa['''id''']]
												UpperCAmelCase		,      UpperCAmelCase       		=    compute_precision_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      qa['''id''']			)

												precisions.append(UpperCamelCase__			)
												recalls.append(UpperCamelCase__			)

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

												exact_match += metric_max_over_ground_truths(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    [x for _, x in sorted(zip(UpperCamelCase__      ,      UpperCamelCase__			)			)]
			recalls.sort()

			UpperCAmelCase       		=    100.0 * fa / total
			UpperCAmelCase       		=    100.0 * exact_match / total
			UpperCAmelCase       		=    get_aupr(UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    get_prec_at_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      recall_thresh=0.9			)
			UpperCAmelCase       		=    get_prec_at_recall(UpperCamelCase__      ,      UpperCamelCase__      ,      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__":
				__A    :   Dict          =       argparse.ArgumentParser(description="Evaluation for CUAD")
				parser.add_argument("dataset_file", help="Dataset file")
				parser.add_argument("prediction_file", help="Prediction File")
				__A    :   Any          =       parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								__A    :   Any          =       json.load(dataset_file)
								__A    :   Optional[int]          =       dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								__A    :   int          =       json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))



