




import inspect
import unittest

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

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_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
       from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import GLPNImageProcessor





class        lowerCamelCase__(   __lowerCamelCase):
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		self.config_class(**self.inputs_dict  )
             self.parent.assertTrue(hasattr(UpperCamelCase_       ,			"""hidden_sizes"""  )  )
             self.parent.assertTrue(hasattr(UpperCamelCase_       ,			"""num_attention_heads"""  )  )
             self.parent.assertTrue(hasattr(UpperCamelCase_       ,			"""num_encoder_blocks"""  )  )





class        lowerCamelCase__:
      def __init__(							self:					Optional[Any]       ,			UpperCamelCase_:					List[Any]       ,			UpperCamelCase_:					str=13       ,			UpperCamelCase_:					Optional[Any]=64       ,			UpperCamelCase_:					Optional[int]=3       ,			UpperCamelCase_:					Dict=4       ,			UpperCamelCase_:					Optional[Any]=[2, 2, 2, 2]       ,			UpperCamelCase_:					str=[8, 4, 2, 1]       ,			UpperCamelCase_:					List[Any]=[16, 32, 64, 1_28]       ,			UpperCamelCase_:					Dict=[1, 4, 8, 16]       ,			UpperCamelCase_:					List[str]=[1, 2, 4, 8]       ,			UpperCamelCase_:					Optional[int]=True       ,			UpperCamelCase_:					Optional[int]=True       ,			UpperCamelCase_:					str="gelu"       ,			UpperCamelCase_:					List[str]=0.1       ,			UpperCamelCase_:					int=0.1       ,			UpperCamelCase_:					Union[str, Any]=0.02       ,			UpperCamelCase_:					str=16       ,			UpperCamelCase_:					int=3       ,			UpperCamelCase_:					Union[str, Any]=None       ,			):
             __lowerCamelCase  =		parent
             __lowerCamelCase  =		batch_size
             __lowerCamelCase  =		image_size
             __lowerCamelCase  =		num_channels
             __lowerCamelCase  =		num_encoder_blocks
             __lowerCamelCase  =		sr_ratios
             __lowerCamelCase  =		depths
             __lowerCamelCase  =		hidden_sizes
             __lowerCamelCase  =		downsampling_rates
             __lowerCamelCase  =		num_attention_heads
             __lowerCamelCase  =		is_training
             __lowerCamelCase  =		use_labels
             __lowerCamelCase  =		hidden_act
             __lowerCamelCase  =		hidden_dropout_prob
             __lowerCamelCase  =		attention_probs_dropout_prob
             __lowerCamelCase  =		initializer_range
             __lowerCamelCase  =		decoder_hidden_size
             __lowerCamelCase  =		num_labels
             __lowerCamelCase  =		scope
      def      lowerCAmelCase__		(							self:					int  ):
             __lowerCamelCase  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

             __lowerCamelCase  =		None
             if self.use_labels:
                    __lowerCamelCase  =		ids_tensor([self.batch_size, self.image_size, self.image_size]       ,			self.num_labels  )

             __lowerCamelCase  =		self.get_config()
             return config, pixel_values, labels
      def      lowerCAmelCase__		(							self:					Dict  ):
             return GLPNConfig(
                 image_size=self.image_size       ,			num_channels=self.num_channels       ,			num_encoder_blocks=self.num_encoder_blocks       ,			depths=self.depths       ,			hidden_sizes=self.hidden_sizes       ,			num_attention_heads=self.num_attention_heads       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			initializer_range=self.initializer_range       ,			decoder_hidden_size=self.decoder_hidden_size       ,			)
      def      lowerCAmelCase__		(							self:					int       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					int  ):
             __lowerCamelCase  =		GLPNModel(config=UpperCamelCase_  )
             model.to(UpperCamelCase_  )
             model.eval()
             __lowerCamelCase  =		model(UpperCamelCase_  )
             __lowerCamelCase  =		__lowerCamelCase  =		self.image_size // (self.downsampling_rates[-1] * 2)
             self.parent.assertEqual(
                 result.last_hidden_state.shape       ,			(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]       ,			UpperCamelCase_:					Optional[int]       ,			UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		self.num_labels
             __lowerCamelCase  =		GLPNForDepthEstimation(UpperCamelCase_  )
             model.to(UpperCamelCase_  )
             model.eval()
             __lowerCamelCase  =		model(UpperCamelCase_  )
             self.parent.assertEqual(result.predicted_depth.shape       ,			(self.batch_size, self.image_size, self.image_size)  )
             __lowerCamelCase  =		model(UpperCamelCase_       ,			labels=UpperCamelCase_  )
             self.parent.assertEqual(result.predicted_depth.shape       ,			(self.batch_size, self.image_size, self.image_size)  )




      def      lowerCAmelCase__		(							self:					str  ):
             __lowerCamelCase  =		self.prepare_config_and_inputs()
             __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		config_and_inputs
             __lowerCamelCase  =		{"""pixel_values""": pixel_values}
             return config, inputs_dict





@require_torch
class        lowerCamelCase__(   __lowerCamelCase					,	__lowerCamelCase					,	unittest.TestCase):
      UpperCAmelCase__ :      Any       						=  (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
      UpperCAmelCase__ :      int       						=  (
          {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
      )

      UpperCAmelCase__ :      str       						=  False
      UpperCAmelCase__ :      Union[str, Any]       						=  False
      UpperCAmelCase__ :      Tuple       						=  False
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		GLPNModelTester(self  )
             __lowerCamelCase  =		GLPNConfigTester(self       ,			config_class=UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             self.config_tester.run_common_tests()
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_  )
      @unittest.skip("""GLPN does not use inputs_embeds"""  )
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             pass
      @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             pass
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs_for_common()

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

                    __lowerCamelCase  =		["""pixel_values"""]
                    self.assertListEqual(arg_names[:1]       ,			UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs_for_common()
             __lowerCamelCase  =		True

             for model_class in self.all_model_classes:
                    __lowerCamelCase  =		True
                    __lowerCamelCase  =		False
                    __lowerCamelCase  =		True
                    __lowerCamelCase  =		model_class(UpperCamelCase_  )
                    model.to(UpperCamelCase_  )
                    model.eval()
                    with torch.no_grad():
                           __lowerCamelCase  =		model(**self._prepare_for_class(UpperCamelCase_       ,			UpperCamelCase_  )  )
                    __lowerCamelCase  =		outputs.attentions

                    __lowerCamelCase  =		sum(self.model_tester.depths  )
                    self.assertEqual(len(UpperCamelCase_  )       ,			UpperCamelCase_  )

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    __lowerCamelCase  =		True
                    __lowerCamelCase  =		model_class(UpperCamelCase_  )
                    model.to(UpperCamelCase_  )
                    model.eval()
                    with torch.no_grad():
                           __lowerCamelCase  =		model(**self._prepare_for_class(UpperCamelCase_       ,			UpperCamelCase_  )  )
                    __lowerCamelCase  =		outputs.attentions

                    self.assertEqual(len(UpperCamelCase_  )       ,			UpperCamelCase_  )

                    # verify the first attentions (first block, first layer)
                    __lowerCamelCase  =		(self.model_tester.image_size // 4) ** 2
                    __lowerCamelCase  =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                    self.assertListEqual(
                        list(attentions[0].shape[-3:]  )       ,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,			)

                    # verify the last attentions (last block, last layer)
                    __lowerCamelCase  =		(self.model_tester.image_size // 32) ** 2
                    __lowerCamelCase  =		(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                    self.assertListEqual(
                        list(attentions[-1].shape[-3:]  )       ,			[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]       ,			)
                    __lowerCamelCase  =		len(UpperCamelCase_  )

                    # Check attention is always last and order is fine
                    __lowerCamelCase  =		True
                    __lowerCamelCase  =		True
                    __lowerCamelCase  =		model_class(UpperCamelCase_  )
                    model.to(UpperCamelCase_  )
                    model.eval()
                    with torch.no_grad():
                           __lowerCamelCase  =		model(**self._prepare_for_class(UpperCamelCase_       ,			UpperCamelCase_  )  )

                    self.assertEqual(out_len + 1       ,			len(UpperCamelCase_  )  )

                    __lowerCamelCase  =		outputs.attentions

                    self.assertEqual(len(UpperCamelCase_  )       ,			UpperCamelCase_  )
                    # verify the first attentions (first block, first layer)
                    __lowerCamelCase  =		(self.model_tester.image_size // 4) ** 2
                    __lowerCamelCase  =		(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                    self.assertListEqual(
                        list(self_attentions[0].shape[-3:]  )       ,			[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,			)
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             def check_hidden_states_output(UpperCamelCase_:					List[str]       ,			UpperCamelCase_:					Any       ,			UpperCamelCase_:					List[str]  ):
                    __lowerCamelCase  =		model_class(UpperCamelCase_  )
                    model.to(UpperCamelCase_  )
                    model.eval()

                    with torch.no_grad():
                           __lowerCamelCase  =		model(**self._prepare_for_class(UpperCamelCase_       ,			UpperCamelCase_  )  )

                    __lowerCamelCase  =		outputs.hidden_states

                    __lowerCamelCase  =		self.model_tester.num_encoder_blocks
                    self.assertEqual(len(UpperCamelCase_  )       ,			UpperCamelCase_  )

                    # verify the first hidden states (first block)
                    self.assertListEqual(
                        list(hidden_states[0].shape[-3:]  )       ,			[
                            self.model_tester.hidden_sizes[0],
                            self.model_tester.image_size // 4,
                            self.model_tester.image_size // 4,
                        ]       ,			)

             __lowerCamelCase,  __lowerCamelCase  =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    __lowerCamelCase  =		True
                    check_hidden_states_output(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_  )

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

                    check_hidden_states_output(UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_  )
      def      lowerCAmelCase__		(							self:					Dict  ):
             if not self.model_tester.is_training:
                    return

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

             for model_class in self.all_model_classes:
                    if model_class in get_values(UpperCamelCase_  ):
                           continue
                    # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
                    # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                    if model_class.__name__ == "GLPNForDepthEstimation":
                           __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		inputs_dict["""pixel_values"""].shape
                           __lowerCamelCase  =		torch.zeros(
                               [self.model_tester.batch_size, height, width]       ,			device=UpperCamelCase_  ).long()
                    __lowerCamelCase  =		model_class(UpperCamelCase_  )
                    model.to(UpperCamelCase_  )
                    model.train()
                    __lowerCamelCase  =		self._prepare_for_class(UpperCamelCase_       ,			UpperCamelCase_       ,			return_labels=UpperCamelCase_  )
                    __lowerCamelCase  =		model(**UpperCamelCase_  ).loss
                    loss.backward()




      @slow
      def      lowerCAmelCase__		(							self:					Tuple  ):
             for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __lowerCamelCase  =		GLPNModel.from_pretrained(UpperCamelCase_  )
                    self.assertIsNotNone(UpperCamelCase_  )




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
       return image





@require_torch
@require_vision
@slow
class        lowerCamelCase__(   unittest.TestCase):
      @slow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
             __lowerCamelCase  =		GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(UpperCamelCase_  )

             __lowerCamelCase  =		prepare_img()
             __lowerCamelCase  =		image_processor(images=UpperCamelCase_       ,			return_tensors="""pt"""  ).to(UpperCamelCase_  )

             # forward pass
             with torch.no_grad():
                    __lowerCamelCase  =		model(**UpperCamelCase_  )

             # verify the predicted depth
             __lowerCamelCase  =		torch.Size([1, 4_80, 6_40]  )
             self.assertEqual(outputs.predicted_depth.shape       ,			UpperCamelCase_  )

             __lowerCamelCase  =		torch.tensor(
                 [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]  ).to(UpperCamelCase_  )

             self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]       ,			UpperCamelCase_       ,			atol=1E-4  )  )







import argparse
import json
import re
import string
import sys

import numpy as np


UpperCAmelCase_   							=      0.5




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		[""".""", """,""", """;""", """:"""]

       for token in remove_tokens:
              __lowerCamelCase  =		ground_truth.replace(A__    ,     """"""      )
              __lowerCamelCase  =		prediction.replace(A__    ,     """"""      )

       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.lower(), prediction.lower()
       __lowerCamelCase,  __lowerCamelCase  =		ground_truth.replace("""/"""    ,     """ """      ), prediction.replace("""/"""    ,     """ """      )
       __lowerCamelCase,  __lowerCamelCase  =		set(ground_truth.split(""" """      )      ), set(prediction.split(""" """      )      )

       __lowerCamelCase  =		ground_truth.intersection(A__      )
       __lowerCamelCase  =		ground_truth.union(A__      )
       __lowerCamelCase  =		len(A__      ) / len(A__      )
       return jaccard




def    lowerCamelCase__			(		A__			:   Optional[Any]      ):





       '''simple docstring'''



       def remove_articles(A__			:   Tuple      ):
              return re.sub(R"""\b(a|an|the)\b"""    ,     """ """    ,     A__      )

       def white_space_fix(A__			:   Optional[Any]      ):
              return " ".join(text.split()      )

       def remove_punc(A__			:   Optional[Any]      ):
              __lowerCamelCase  =		set(string.punctuation      )
              return "".join(ch for ch in text if ch not in exclude      )

       def lower(A__			:   Tuple      ):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(A__      )      )      )      )




def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Optional[Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		0, 0, 0

       __lowerCamelCase  =		"""Parties""" in qa_id

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

                     if not match_found:
                            fp += 1

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

       return precision, recall




def    lowerCamelCase__			(		A__			:   int      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   str    ,     A__			:   Union[str, Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		process_precisions(A__      )
       __lowerCamelCase  =		np.trapz(A__    ,     A__      )
       if np.isnan(A__      ):
              return 0
       return aupr




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       return normalize_answer(A__      ) == normalize_answer(A__      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   int    ,     A__			:   Dict      ):





       '''simple docstring'''



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




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



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

                            precisions.append(A__      )
                            recalls.append(A__      )

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

                            exact_match += metric_max_over_ground_truths(A__    ,     A__    ,     A__      )

       __lowerCamelCase  =		[x for _, x in sorted(zip(A__    ,     A__      )      )]
       recalls.sort()

       __lowerCamelCase  =		100.0 * fa / total
       __lowerCamelCase  =		100.0 * exact_match / total
       __lowerCamelCase  =		get_aupr(A__    ,     A__      )

       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     recall_thresh=0.9      )
       __lowerCamelCase  =		get_prec_at_recall(A__    ,     A__    ,     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__":
       UpperCAmelCase_   							=      argparse.ArgumentParser(description='Evaluation for CUAD')
       parser.add_argument('dataset_file', help='Dataset file')
       parser.add_argument('prediction_file', help='Prediction File')
       UpperCAmelCase_   							=      parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              UpperCAmelCase_   							=      json.load(dataset_file)
              UpperCAmelCase_   							=      dataset_json['data']
       with open(args.prediction_file) as prediction_file:
              UpperCAmelCase_   							=      json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))

