



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.gptj.modeling_tf_gptj import (
           TFGPTJForCausalLM,
           TFGPTJForQuestionAnswering,
           TFGPTJForSequenceClassification,
           TFGPTJModel,
           shape_list,
       )





class A_      :




   def __init__(self			:Optional[Any]					,     _UpperCamelCase			:List[Any]				)->      Tuple:
         __A      =       parent
         __A      =       13
         __A      =       7
         __A      =       True
         __A      =       True
         __A      =       True
         __A      =       True
         __A      =       True
         __A      =       99
         __A      =       32
         __A      =       4
         __A      =       2
         __A      =       4
         __A      =       37
         __A      =       '''gelu'''
         __A      =       0.1
         __A      =       0.1
         __A      =       512
         __A      =       16
         __A      =       2
         __A      =       0.0_2
         __A      =       3
         __A      =       4
         __A      =       None
         __A      =       self.vocab_size - 1
         __A      =       self.vocab_size - 1
         __A      =       self.vocab_size - 1




   def _lowerCAmelCase   (self			:Dict				)->      int:
         __A      =       ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size				)

         __A      =       None
         if self.use_input_mask:
               __A      =       random_attention_mask([self.batch_size, self.seq_length]				)

         __A      =       None
         if self.use_token_type_ids:
               __A      =       ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size				)

         __A      =       None
         if self.use_mc_token_ids:
               __A      =       ids_tensor([self.batch_size, self.num_choices]					,     self.seq_length				)

         __A      =       None
         __A      =       None
         __A      =       None
         if self.use_labels:
               __A      =       ids_tensor([self.batch_size]					,     self.type_sequence_label_size				)
               __A      =       ids_tensor([self.batch_size, self.seq_length]					,     self.num_labels				)
               __A      =       ids_tensor([self.batch_size]					,     self.num_choices				)

         __A      =       GPTJConfig(
             vocab_size=self.vocab_size					,     n_embd=self.hidden_size					,     n_layer=self.num_hidden_layers					,     n_head=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					,     n_positions=self.max_position_embeddings					,     type_vocab_size=self.type_vocab_size					,     initializer_range=self.initializer_range					,     bos_token_id=self.bos_token_id					,     eos_token_id=self.eos_token_id					,     pad_token_id=self.pad_token_id					,     rotary_dim=self.rotary_dim					,     return_dict=_UpperCamelCase					,     )

         __A      =       ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,     2				)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )




   def _lowerCAmelCase   (self			:List[str]					,     _UpperCamelCase			:Dict					,     _UpperCamelCase			:List[str]					,     _UpperCamelCase			:List[str]					,     _UpperCamelCase			:Any					,     _UpperCamelCase			:Union[str, Any]					,     *_UpperCamelCase			:Dict				)->      Tuple:
         __A      =       TFGPTJModel(config=_UpperCamelCase				)
         __A      =       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         __A      =       model(_UpperCamelCase				)

         __A      =       [input_ids, None, input_mask]  # None is the input for 'past'
         __A      =       model(_UpperCamelCase				)

         __A      =       model(_UpperCamelCase				)

         self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)				)




   def _lowerCAmelCase   (self			:Union[str, Any]					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:Optional[int]					,     _UpperCamelCase			:Any					,     _UpperCamelCase			:int					,     _UpperCamelCase			:Union[str, Any]					,     *_UpperCamelCase			:List[str]				)->      str:
         __A      =       TFGPTJModel(config=_UpperCamelCase				)

         # first forward pass
         __A      =       model(_UpperCamelCase					,     token_type_ids=_UpperCamelCase					,     use_cache=_UpperCamelCase				)
         __A      =       model(_UpperCamelCase					,     token_type_ids=_UpperCamelCase				)
         __A      =       model(_UpperCamelCase					,     token_type_ids=_UpperCamelCase					,     use_cache=_UpperCamelCase				)

         self.parent.assertTrue(len(_UpperCamelCase				) == len(_UpperCamelCase				)				)
         self.parent.assertTrue(len(_UpperCamelCase				) == len(_UpperCamelCase				) + 1				)

         __A     ,		__A      =       outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         __A      =       ids_tensor((self.batch_size, 1)					,     config.vocab_size				)
         __A      =       ids_tensor([self.batch_size, 1]					,     self.type_vocab_size				)

         # append to next input_ids and token_type_ids
         __A      =       tf.concat([input_ids, next_tokens]					,     axis=-1				)
         __A      =       tf.concat([token_type_ids, next_token_types]					,     axis=-1				)

         __A      =       model(_UpperCamelCase					,     token_type_ids=_UpperCamelCase				)['''last_hidden_state''']
         __A      =       model(_UpperCamelCase					,     token_type_ids=_UpperCamelCase					,     past_key_values=_UpperCamelCase				)[
             '''last_hidden_state'''
         ]

         # select random slice
         __A      =       int(ids_tensor((1,)					,     shape_list(_UpperCamelCase				)[-1]				)				)
         __A      =       output_from_no_past[:, -1, random_slice_idx]
         __A      =       output_from_past[:, 0, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(_UpperCamelCase					,     _UpperCamelCase					,     rtol=1e-6				)




   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:int					,     _UpperCamelCase			:Optional[int]					,     _UpperCamelCase			:int					,     _UpperCamelCase			:Tuple					,     _UpperCamelCase			:Dict					,     *_UpperCamelCase			:Optional[int]				)->      str:
         __A      =       TFGPTJModel(config=_UpperCamelCase				)

         # create attention mask
         __A      =       self.seq_length // 2
         __A      =       tf.ones((self.batch_size, half_seq_length)					,     dtype=tf.intaa				)
         __A      =       tf.zeros((self.batch_size, self.seq_length - half_seq_length)					,     dtype=tf.intaa				)
         __A      =       tf.concat([attn_mask_begin, attn_mask_end]					,     axis=1				)

         # first forward pass
         __A     ,		__A      =       model(_UpperCamelCase					,     attention_mask=_UpperCamelCase				).to_tuple()

         # create hypothetical next token and extent to next_input_ids
         __A      =       ids_tensor((self.batch_size, 1)					,     config.vocab_size				)

         # change a random masked slice from input_ids
         __A      =       ids_tensor((1,)					,     _UpperCamelCase				).numpy() + 1
         __A      =       ids_tensor((self.batch_size, self.seq_length)					,     config.vocab_size				)
         __A      =       tf.range(self.seq_length				) == (self.seq_length - random_seq_idx_to_change)
         __A      =       tf.transpose(
             tf.broadcast_to(tf.expand_dims(_UpperCamelCase					,     -1				)					,     (self.seq_length, self.batch_size)				)				)
         __A      =       tf.where(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase				)

         # append to next input_ids and attn_mask
         __A      =       tf.concat([input_ids, next_tokens]					,     axis=-1				)
         __A      =       tf.concat([attn_mask, tf.ones((shape_list(_UpperCamelCase				)[0], 1)					,     dtype=tf.intaa				)]					,     axis=1				)

         # get two different outputs
         __A      =       model(_UpperCamelCase					,     attention_mask=_UpperCamelCase				)['''last_hidden_state''']
         __A      =       model(_UpperCamelCase					,     past_key_values=_UpperCamelCase					,     attention_mask=_UpperCamelCase				)[
             '''last_hidden_state'''
         ]

         # select random slice
         __A      =       int(ids_tensor((1,)					,     shape_list(_UpperCamelCase				)[-1]				)				)
         __A      =       output_from_no_past[:, -1, random_slice_idx]
         __A      =       output_from_past[:, 0, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(_UpperCamelCase					,     _UpperCamelCase					,     rtol=1e-12				)




   def _lowerCAmelCase   (self			:List[Any]					,     _UpperCamelCase			:Union[str, Any]					,     _UpperCamelCase			:int					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:str					,     _UpperCamelCase			:Optional[Any]					,     *_UpperCamelCase			:int				)->      Union[str, Any]:
         __A      =       TFGPTJModel(config=_UpperCamelCase				)

         __A      =       input_ids[:1, :]
         __A      =       input_mask[:1, :]
         __A      =       token_type_ids[:1, :]
         __A      =       1

         # first forward pass
         __A      =       model(_UpperCamelCase					,     attention_mask=_UpperCamelCase					,     token_type_ids=_UpperCamelCase					,     use_cache=_UpperCamelCase				)

         __A     ,		__A      =       outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         __A      =       ids_tensor((self.batch_size, 3)					,     config.vocab_size				)
         __A      =       ids_tensor((self.batch_size, 3)					,     2				)
         __A      =       ids_tensor((self.batch_size, 3)					,     self.type_vocab_size				)

         # append to next input_ids and token_type_ids
         __A      =       tf.concat([input_ids, next_tokens]					,     axis=-1				)
         __A      =       tf.concat([input_mask, next_attn_mask]					,     axis=-1				)
         __A      =       tf.concat([token_type_ids, next_token_types]					,     axis=-1				)

         __A      =       model(
             _UpperCamelCase					,     token_type_ids=_UpperCamelCase					,     attention_mask=_UpperCamelCase				)['''last_hidden_state''']
         __A      =       model(
             _UpperCamelCase					,     token_type_ids=_UpperCamelCase					,     attention_mask=_UpperCamelCase					,     past_key_values=_UpperCamelCase					,     )['''last_hidden_state''']
         self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]				)

         # select random slice
         __A      =       int(ids_tensor((1,)					,     shape_list(_UpperCamelCase				)[-1]				)				)
         __A      =       output_from_no_past[:, -3:, random_slice_idx]
         __A      =       output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(_UpperCamelCase					,     _UpperCamelCase					,     rtol=1e-3				)




   def _lowerCAmelCase   (self			:int					,     _UpperCamelCase			:List[str]					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:Union[str, Any]					,     _UpperCamelCase			:int					,     _UpperCamelCase			:str					,     *_UpperCamelCase			:Tuple				)->      str:
         __A      =       TFGPTJForCausalLM(config=_UpperCamelCase				)
         __A      =       {
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         __A      =       model(_UpperCamelCase				)
         self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)				)






   def _lowerCAmelCase   (self			:int				)->      str:
         __A      =       self.prepare_config_and_inputs()

         (
             (
             __A		
         )     ,		(
             __A		
         )     ,		(
             __A		
         )     ,		(
             __A		
         )     ,		(
             __A		
         )     ,		(
             __A		
         )     ,		(
             __A		
         )     ,		(
             __A		
         )     ,		(
             __A		
         )     ,				
         )      =       config_and_inputs

         __A      =       {
             '''input_ids''': input_ids,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict





@require_tf
class A_      (    _lowerCamelCase				,   _lowerCamelCase				,   _lowerCamelCase				,   unittest.TestCase     ):
   lowerCAmelCase__       =				(
       (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
       if is_tf_available()
       else ()
   )

   lowerCAmelCase__       =				(TFGPTJForCausalLM,) if is_tf_available() else ()
   lowerCAmelCase__       =				(
       {
           """feature-extraction""": TFGPTJModel,
           """question-answering""": TFGPTJForQuestionAnswering,
           """text-classification""": TFGPTJForSequenceClassification,
           """text-generation""": TFGPTJForCausalLM,
           """zero-shot""": TFGPTJForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase__       =				False
   lowerCAmelCase__       =				False
   lowerCAmelCase__       =				False
   lowerCAmelCase__       =				False




   def _lowerCAmelCase   (self			:Union[str, Any]					,     _UpperCamelCase			:Optional[int]					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:Optional[int]					,     _UpperCamelCase			:str					,     _UpperCamelCase			:str				)->      Union[str, Any]:
         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith('''Fast'''				)
         ):
               # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
               # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
               # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
               return True

         return False




   def _lowerCAmelCase   (self			:Optional[int]				)->      int:
         __A      =       TFGPTJModelTester(self				)
         __A      =       ConfigTester(self					,     config_class=_UpperCamelCase					,     n_embd=37				)




   def _lowerCAmelCase   (self			:Optional[Any]				)->      List[str]:
         self.config_tester.run_common_tests()




   def _lowerCAmelCase   (self			:Optional[int]				)->      Tuple:
         __A      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model(*_UpperCamelCase				)




   def _lowerCAmelCase   (self			:str				)->      str:
         __A      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_past(*_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Any				)->      Any:
         __A      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_attention_mask_past(*_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Dict				)->      Union[str, Any]:
         __A      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_past_large_inputs(*_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Optional[Any]				)->      Dict:
         __A      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_lm_head_model(*_UpperCamelCase				)




   @slow
   @unittest.skipIf(
       not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''				)				) > 0					,     '''skip testing on GPU for now to avoid GPU OOM.'''					,     )
   def _lowerCAmelCase   (self			:Tuple				)->      Tuple:
         __A      =       TFGPTJModel.from_pretrained('''EleutherAI/gpt-j-6B'''					,     from_pt=_UpperCamelCase				)
         self.assertIsNotNone(_UpperCamelCase				)






   @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''				)
   def _lowerCAmelCase   (self			:List[str]				)->      Optional[Any]:
         super().test_resize_token_embeddings()





@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class A_      (    unittest.TestCase     ):




   def _lowerCAmelCase   (self			:List[str]				)->      Union[str, Any]:
         __A      =       TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''					,     from_pt=_UpperCamelCase				)
         __A      =       tf.convert_to_tensor([[464, 3290]]					,     dtype=tf.intaa				)  # The dog
         # fmt: off
         # The dog is a man's best friend. It is a loyal companion, and it is a friend
         __A      =       [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 1_5185, 11, 290, 340, 318, 257, 1545]
         # fmt: on
         __A      =       model.generate(_UpperCamelCase					,     do_sample=_UpperCamelCase				)
         self.assertListEqual(output_ids[0].numpy().tolist()					,     _UpperCamelCase				)




   def _lowerCAmelCase   (self			:str				)->      Union[str, Any]:
         __A      =       AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''					,     revision='''float16'''				)
         __A      =       TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''					,     revision='''float16'''					,     from_pt=_UpperCamelCase				)

         __A      =       tokenizer('''Today is a nice day and'''					,     return_tensors='''tf'''				)
         # forces the generation to happen on CPU, to avoid GPU-related quirks
         with tf.device(''':/CPU:0'''				):
               __A      =       model.generate(**_UpperCamelCase					,     do_sample=_UpperCamelCase					,     seed=[42, 0]				)
         __A      =       tokenizer.decode(output_ids[0]					,     skip_special_tokens=_UpperCamelCase				)

         __A      =       '''Today is a nice day and I’m going to go for a walk. I’'''
         self.assertEqual(_UpperCamelCase					,     _UpperCamelCase				)




   def _lowerCAmelCase   (self			:Optional[Any]				)->      str:
         __A      =       TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''					,     revision='''float16'''					,     from_pt=_UpperCamelCase				)
         __A      =       AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''					,     revision='''float16'''				)

         __A      =       '''left'''

         # Define PAD Token = EOS Token = 50256
         __A      =       tokenizer.eos_token
         __A      =       model.config.eos_token_id

         # use different length sentences to test batching
         __A      =       [
             '''Hello, my dog is a little''',
             '''Today, I''',
         ]
         __A      =       [
             '''Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia''',
             '''Today, I’m going to be talking about a topic that’''',
         ]
         return model, tokenizer, sentences, expected_output_sentences




   def _lowerCAmelCase   (self			:int				)->      str:
         # Confirms that we get the expected results with left-padded beam search
         __A     ,		__A     ,		__A     ,		__A      =       self._get_beam_search_test_objects()

         __A      =       tokenizer(_UpperCamelCase					,     return_tensors='''tf'''					,     padding=_UpperCamelCase				)
         __A      =       model.generate(**_UpperCamelCase					,     do_sample=_UpperCamelCase					,     num_beams=2				)
         __A      =       tokenizer.batch_decode(_UpperCamelCase					,     skip_special_tokens=_UpperCamelCase				)
         self.assertListEqual(_UpperCamelCase					,     _UpperCamelCase				)




   def _lowerCAmelCase   (self			:int				)->      Union[str, Any]:
         # Confirms that left-padding is working properly
         __A     ,		__A     ,		__A     ,		__A      =       self._get_beam_search_test_objects()

         __A      =       tokenizer(_UpperCamelCase					,     return_tensors='''tf'''					,     padding=_UpperCamelCase				)
         __A      =       tokenizer(sentences[0]					,     return_tensors='''tf'''				)
         __A      =       model.generate(**_UpperCamelCase					,     do_sample=_UpperCamelCase					,     num_beams=2				)
         __A      =       (
             shape_list(inputs_non_padded['''input_ids''']				)[-1]
             - tf.reduce_sum(tf.cast(inputs['''attention_mask'''][-1]					,     tf.intaa				)				).numpy()
         )
         __A      =       tokenizer(sentences[1]					,     return_tensors='''tf'''				)
         __A      =       model.generate(
             **_UpperCamelCase					,     do_sample=_UpperCamelCase					,     num_beams=2					,     max_length=model.config.max_length - num_paddings				)
         __A      =       tokenizer.decode(output_non_padded[0]					,     skip_special_tokens=_UpperCamelCase				)
         __A      =       tokenizer.decode(output_padded[0]					,     skip_special_tokens=_UpperCamelCase				)
         self.assertListEqual(_UpperCamelCase					,     [non_padded_sentence, padded_sentence]				)






   def _lowerCAmelCase   (self			:Union[str, Any]				)->      List[str]:
         # Confirms that XLA is working properly
         __A     ,		__A     ,		__A     ,		__A      =       self._get_beam_search_test_objects()

         __A      =       tokenizer(_UpperCamelCase					,     return_tensors='''tf'''					,     padding=_UpperCamelCase				)
         __A      =       tf.function(model.generate					,     jit_compile=_UpperCamelCase				)
         __A      =       xla_generate(**_UpperCamelCase					,     do_sample=_UpperCamelCase					,     num_beams=2				)
         __A      =       tokenizer.batch_decode(_UpperCamelCase					,     skip_special_tokens=_UpperCamelCase				)
         self.assertListEqual(_UpperCamelCase					,     _UpperCamelCase				)












import argparse
import json
import re
import string
import sys

import numpy as np


snake_case__      :							Dict									=  0.5



def _a       (			lowerCamelCase:     List[str]      ,       lowerCamelCase:     List[str]				)			->		List[Any]:







      '''simple docstring'''






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

      for token in remove_tokens:
            __A      =       ground_truth.replace(lowerCamelCase      ,       ''''''				)
            __A      =       prediction.replace(lowerCamelCase      ,       ''''''				)

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

      __A      =       ground_truth.intersection(lowerCamelCase				)
      __A      =       ground_truth.union(lowerCamelCase				)
      __A      =       len(lowerCamelCase				) / len(lowerCamelCase				)
      return jaccard



def _a       (			lowerCamelCase:     List[str]				)			->		Dict:







      '''simple docstring'''






      def remove_articles(lowerCamelCase:     Tuple				):
            return re.sub(r'''\b(a|an|the)\b'''      ,       ''' '''      ,       lowerCamelCase				)

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

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

      def lower(lowerCamelCase:     int				):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase				)				)				)				)



def _a       (			lowerCamelCase:     Dict      ,       lowerCamelCase:     int      ,       lowerCamelCase:     List[str]				)			->		str:







      '''simple docstring'''






      __A     ,		__A     ,		__A      =       0, 0, 0

      __A      =       '''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
                  __A      =       False
                  for pred in predictions:
                        if substr_ok:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH or ground_truth in pred
                        else:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH
                        if is_match:
                              __A      =       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.)
                  __A      =       False
                  for ground_truth in ground_truths:
                        assert len(lowerCamelCase				) > 0
                        if substr_ok:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH or ground_truth in pred
                        else:
                              __A      =       get_jaccard(lowerCamelCase      ,       lowerCamelCase				) >= IOU_THRESH
                        if is_match:
                              __A      =       True

                  if not match_found:
                        fp += 1

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

      return precision, recall



def _a       (			lowerCamelCase:     Dict				)			->		List[str]:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     Optional[int]      ,       lowerCamelCase:     Dict				)			->		Tuple:







      '''simple docstring'''






      __A      =       process_precisions(lowerCamelCase				)
      __A      =       np.trapz(lowerCamelCase      ,       lowerCamelCase				)
      if np.isnan(lowerCamelCase				):
            return 0
      return aupr



def _a       (			lowerCamelCase:     Any      ,       lowerCamelCase:     List[str]      ,       lowerCamelCase:     Optional[int]				)			->		str:







      '''simple docstring'''






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



def _a       (			lowerCamelCase:     Tuple      ,       lowerCamelCase:     Dict				)			->		Optional[Any]:







      '''simple docstring'''






      return normalize_answer(lowerCamelCase				) == normalize_answer(lowerCamelCase				)



def _a       (			lowerCamelCase:     Any      ,       lowerCamelCase:     List[Any]      ,       lowerCamelCase:     Optional[int]				)			->		List[str]:







      '''simple docstring'''






      __A      =       0
      for pred in predictions:
            for ground_truth in ground_truths:
                  __A      =       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 _a       (			lowerCamelCase:     Union[str, Any]      ,       lowerCamelCase:     int				)			->		int:







      '''simple docstring'''






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

      __A      =       [x for _, x in sorted(zip(lowerCamelCase      ,       lowerCamelCase				)				)]
      recalls.sort()

      __A      =       100.0 * fa / total
      __A      =       100.0 * exact_match / total
      __A      =       get_aupr(lowerCamelCase      ,       lowerCamelCase				)

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





