"""simple docstring"""





import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_3		,			a=7		,			a=True		,			a=True		,			a=False		,			a=True		,			a=9_9		,			a=3_2		,			a=5		,			a=4		,			a=3_7		,			a="gelu"		,			a=0.1		,			a=0.1		,			a=5_1_2		,			a=1_6		,			a=2		,			a=0.02		,			a=3		,			a=4		,			a=None		,			)       ->					Union[str, Any]:
             lowercase__			:		Any       =	parent
             lowercase__			:		List[str]       =	batch_size
             lowercase__			:		str       =	seq_length
             lowercase__			:		Optional[Any]       =	is_training
             lowercase__			:		Tuple       =	use_input_mask
             lowercase__			:		List[Any]       =	use_token_type_ids
             lowercase__			:		Dict       =	use_labels
             lowercase__			:		List[str]       =	vocab_size
             lowercase__			:		Tuple       =	hidden_size
             lowercase__			:		Optional[Any]       =	num_hidden_layers
             lowercase__			:		str       =	num_attention_heads
             lowercase__			:		Union[str, Any]       =	intermediate_size
             lowercase__			:		Optional[int]       =	hidden_act
             lowercase__			:		Optional[Any]       =	hidden_dropout_prob
             lowercase__			:		Optional[int]       =	attention_probs_dropout_prob
             lowercase__			:		List[Any]       =	max_position_embeddings
             lowercase__			:		Optional[Any]       =	type_vocab_size
             lowercase__			:		Optional[int]       =	type_sequence_label_size
             lowercase__			:		str       =	initializer_range
             lowercase__			:		Tuple       =	num_labels
             lowercase__			:		int       =	num_choices
             lowercase__			:		Union[str, Any]       =	scope


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		str       =	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size							)

             lowercase__			:		List[str]       =	None
             if self.use_input_mask:
                    lowercase__			:		Tuple       =	random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__			:		Dict       =	None
             if self.use_token_type_ids:
                    lowercase__			:		Optional[Any]       =	ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size							)

             lowercase__			:		Tuple       =	None
             lowercase__			:		Tuple       =	None
             lowercase__			:		Tuple       =	None
             if self.use_labels:
                    lowercase__			:		str       =	ids_tensor([self.batch_size]		,			self.type_sequence_label_size							)
                    lowercase__			:		str       =	ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels							)
                    lowercase__			:		List[Any]       =	ids_tensor([self.batch_size]		,			self.num_choices							)

             lowercase__			:		List[str]       =	self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


      def 		_UpperCAmelCase       (   self							)       ->					str:
             return LlamaConfig(
                 vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=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		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			is_decoder=a		,			initializer_range=self.initializer_range		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			a		,			a		,			a							)       ->					Optional[Any]:
             lowercase__			:		int       =	LlamaModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__			:		Union[str, Any]       =	model(a		,			attention_mask=a							)
             lowercase__			:		int       =	model(a							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			)       ->					Any:
             lowercase__			:		Union[str, Any]       =	True
             lowercase__			:		Tuple       =	LlamaModel(a							)
             model.to(a							)
             model.eval()
             lowercase__			:		Any       =	model(
                 a		,			attention_mask=a		,			encoder_hidden_states=a		,			encoder_attention_mask=a		,			)
             lowercase__			:		Dict       =	model(
                 a		,			attention_mask=a		,			encoder_hidden_states=a		,			)
             lowercase__			:		int       =	model(a		,			attention_mask=a							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			)       ->					Optional[Any]:
             lowercase__			:		Dict       =	LlamaForCausalLM(config=a							)
             model.to(a							)
             model.eval()
             lowercase__			:		Optional[Any]       =	model(a		,			attention_mask=a		,			labels=a							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			a		,			)       ->					Union[str, Any]:
             lowercase__			:		Any       =	True
             lowercase__			:		List[Any]       =	True
             lowercase__			:		Union[str, Any]       =	LlamaForCausalLM(config=a							)
             model.to(a							)
             model.eval()

             # first forward pass
             lowercase__			:		Optional[Any]       =	model(
                 a		,			attention_mask=a		,			encoder_hidden_states=a		,			encoder_attention_mask=a		,			use_cache=a		,			)
             lowercase__			:		int       =	outputs.past_key_values

             # create hypothetical multiple next token and extent to next_input_ids
             lowercase__			:		Optional[Any]       =	ids_tensor((self.batch_size, 3)		,			config.vocab_size							)
             lowercase__			:		Optional[Any]       =	ids_tensor((self.batch_size, 3)		,			vocab_size=2							)

             # append to next input_ids and
             lowercase__			:		Optional[int]       =	torch.cat([input_ids, next_tokens]		,			dim=-1							)
             lowercase__			:		Tuple       =	torch.cat([input_mask, next_mask]		,			dim=-1							)

             lowercase__			:		Tuple       =	model(
                 a		,			attention_mask=a		,			encoder_hidden_states=a		,			encoder_attention_mask=a		,			output_hidden_states=a		,			)['hidden_states'][0]
             lowercase__			:		Optional[int]       =	model(
                 a		,			attention_mask=a		,			encoder_hidden_states=a		,			encoder_attention_mask=a		,			past_key_values=a		,			output_hidden_states=a		,			)['hidden_states'][0]

             # select random slice
             lowercase__			:		int       =	ids_tensor((1,)		,			output_from_past.shape[-1]							).item()
             lowercase__			:		List[Any]       =	output_from_no_past[:, -3:, random_slice_idx].detach()
             lowercase__			:		Optional[Any]       =	output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(a		,			a		,			atol=1e-3							)							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )    ,      (
                 lowercase__
             )    ,      (
                 lowercase__
             )    ,      (
                 lowercase__
             )    ,      (
                 lowercase__
             )    ,      (
                 lowercase__
             )    ,      (
                 lowercase__
             )    ,      
             )			:		Dict       =	config_and_inputs
             lowercase__			:		Any       =	{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict





@require_torch
class      UpperCAmelCase_	(    _a ,							_a ,							_a ,							unittest.TestCase):
      lowerCamelCase__		:    Any   =  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
      lowerCamelCase__		:    List[Any]   =  (LlamaForCausalLM,) if is_torch_available() else ()
      lowerCamelCase__		:    str   =  (
          {
              "feature-extraction": LlamaModel,
              "text-classification": LlamaForSequenceClassification,
              "text-generation": LlamaForCausalLM,
              "zero-shot": LlamaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCamelCase__		:    Tuple   =  False
      lowerCamelCase__		:    str   =  False


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		Tuple       =	LlamaModelTester(self							)
             lowercase__			:		List[Any]       =	ConfigTester(self		,			config_class=a		,			hidden_size=3_7							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             self.config_tester.run_common_tests()


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Any       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		str       =	self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    lowercase__			:		Any       =	type
                    self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__    ,      lowercase__			:		Any       =	self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__			:		Tuple       =	3
             lowercase__			:		str       =	input_dict['input_ids']
             lowercase__			:		str       =	input_ids.ne(1							).to(a							)
             lowercase__			:		Tuple       =	ids_tensor([self.model_tester.batch_size]		,			self.model_tester.type_sequence_label_size							)
             lowercase__			:		Optional[int]       =	LlamaForSequenceClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__			:		Tuple       =	model(a		,			attention_mask=a		,			labels=a							)
             self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels)							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__    ,      lowercase__			:		Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__			:		Tuple       =	3
             lowercase__			:		str       =	'single_label_classification'
             lowercase__			:		int       =	input_dict['input_ids']
             lowercase__			:		List[Any]       =	input_ids.ne(1							).to(a							)
             lowercase__			:		Any       =	ids_tensor([self.model_tester.batch_size]		,			self.model_tester.type_sequence_label_size							)
             lowercase__			:		Any       =	LlamaForSequenceClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__			:		Dict       =	model(a		,			attention_mask=a		,			labels=a							)
             self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels)							)


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__    ,      lowercase__			:		int       =	self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__			:		Union[str, Any]       =	3
             lowercase__			:		str       =	'multi_label_classification'
             lowercase__			:		List[str]       =	input_dict['input_ids']
             lowercase__			:		Optional[Any]       =	input_ids.ne(1							).to(a							)
             lowercase__			:		List[Any]       =	ids_tensor(
                 [self.model_tester.batch_size, config.num_labels]		,			self.model_tester.type_sequence_label_size							).to(torch.float							)
             lowercase__			:		Any       =	LlamaForSequenceClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__			:		List[Any]       =	model(a		,			attention_mask=a		,			labels=a							)
             self.assertEqual(result.logits.shape		,			(self.model_tester.batch_size, self.model_tester.num_labels)							)


      @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'							)
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             pass


      @parameterized.expand([('linear',), ('dynamic',)]							)
      def 		_UpperCAmelCase       (   self		,			a							)       ->					List[Any]:
             lowercase__    ,      lowercase__			:		List[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__			:		Optional[int]       =	ids_tensor([1, 1_0]		,			config.vocab_size							)
             lowercase__			:		Tuple       =	ids_tensor([1, int(config.max_position_embeddings * 1.5							)]		,			config.vocab_size							)

             set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
             lowercase__			:		int       =	LlamaModel(a							)
             original_model.to(a							)
             original_model.eval()
             lowercase__			:		int       =	original_model(a							).last_hidden_state
             lowercase__			:		Optional[int]       =	original_model(a							).last_hidden_state

             set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
             lowercase__			:		Union[str, Any]       =	{'type': scaling_type, 'factor': 10.0}
             lowercase__			:		Optional[int]       =	LlamaModel(a							)
             scaled_model.to(a							)
             scaled_model.eval()
             lowercase__			:		str       =	scaled_model(a							).last_hidden_state
             lowercase__			:		Union[str, Any]       =	scaled_model(a							).last_hidden_state

             # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
             # maximum sequence length, so the outputs for the short input should match.
             if scaling_type == "dynamic":
                    self.assertTrue(torch.allclose(a		,			a		,			atol=1e-5							)							)
             else:
                    self.assertFalse(torch.allclose(a		,			a		,			atol=1e-5							)							)

             # The output should be different for long inputs
             self.assertFalse(torch.allclose(a		,			a		,			atol=1e-5							)							)





@require_torch
class      UpperCAmelCase_	(    unittest.TestCase):


      @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'							)
      @slow
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Dict       =	[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
             lowercase__			:		Dict       =	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'		,			device_map='auto'							)
             lowercase__			:		List[str]       =	model(torch.tensor([input_ids]							)							)
             # Expected mean on dim = -1
             lowercase__			:		Dict       =	torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]							)
             torch.testing.assert_close(out.mean(-1							)		,			a		,			atol=1e-2		,			rtol=1e-2							)
             # slicing logits[0, 0, 0:30]
             # fmt: off
             lowercase__			:		int       =	torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]							)
             # fmt: on
             torch.testing.assert_close(out[0, 0, :3_0]		,			a		,			atol=1e-5		,			rtol=1e-5							)


      @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'							)
      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Optional[int]       =	[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
             lowercase__			:		Optional[Any]       =	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'		,			device_map='auto'							)
             lowercase__			:		Any       =	model(torch.tensor(a							)							)
             # Expected mean on dim = -1
             lowercase__			:		Optional[int]       =	torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]							)
             torch.testing.assert_close(out.mean(-1							)		,			a		,			atol=1e-2		,			rtol=1e-2							)
             # slicing logits[0, 0, 0:30]
             # fmt: off
             lowercase__			:		Tuple       =	torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]							)
             # fmt: on
             torch.testing.assert_close(out[0, 0, :3_0]		,			a		,			atol=1e-5		,			rtol=1e-5							)


      @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'							)
      @slow
      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Optional[Any]       =	[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
             lowercase__			:		Union[str, Any]       =	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'		,			device_map='auto'							)
             lowercase__			:		int       =	model(torch.tensor(a							)							)
             # Expected mean on dim = -1
             lowercase__			:		Tuple       =	torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]							)
             torch.testing.assert_close(out.mean(-1							)		,			a		,			atol=1e-2		,			rtol=1e-2							)
             # slicing logits[0, 0, 0:30]
             # fmt: off
             lowercase__			:		int       =	torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]							)
             # fmt: on
             torch.testing.assert_close(out.mean(-1							)		,			a		,			atol=1e-2		,			rtol=1e-2							)


      @unittest.skip(
          'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'							)
      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		str       =	[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
             lowercase__			:		Optional[Any]       =	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'		,			device_map='auto'							)
             lowercase__			:		Optional[int]       =	model(torch.tensor(a							)							)

             lowercase__			:		int       =	torch.tensor(
                 [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]		,			dtype=torch.floataa							)
             torch.testing.assert_close(out.mean(-1							)		,			a		,			atol=1e-2		,			rtol=1e-2							)
             # fmt: off
             lowercase__			:		Optional[Any]       =	torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]							)
             # fmt: on
             torch.testing.assert_close(out[0, 0, :3_0]		,			a		,			atol=1e-5		,			rtol=1e-5							)


      @unittest.skip('Model is curently gated'							)
      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		str       =	'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
             lowercase__			:		Optional[int]       =	'Simply put, the theory of relativity states that '
             lowercase__			:		List[Any]       =	LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'							)
             lowercase__			:		Optional[int]       =	tokenizer.encode(a		,			return_tensors='pt'							)
             lowercase__			:		int       =	LlamaForCausalLM.from_pretrained(
                 'meta-llama/Llama-2-13b-chat-hf'		,			device_map='sequential'		,			use_safetensors=a							)

             # greedy generation outputs
             lowercase__			:		str       =	model.generate(a		,			max_new_tokens=6_4		,			top_p=a		,			temperature=1		,			do_sample=a							)
             lowercase__			:		Optional[Any]       =	tokenizer.decode(generated_ids[0]		,			skip_special_tokens=a							)
             self.assertEqual(a		,			a							)





import datasets

from .evaluate import evaluate


A      :    Dict		=					"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

A      :    Any		=					"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

A      :    str		=					"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,  _KWARGS_DESCRIPTION)
class 			_lowercase (							datasets.Metric):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
                     {
                         "predictions": {
                             "id": datasets.Value("string"       ),
                             "prediction_text": datasets.features.Sequence(datasets.Value("string"       )       ),
                         },
                         "references": {
                             "id": datasets.Value("string"       ),
                             "answers": datasets.features.Sequence(
                                 {
                                     "text": datasets.Value("string"       ),
                                     "answer_start": datasets.Value("int32"       ),
                                 }       ),
                         },
                     }       )					,      codebase_urls=["https://www.atticusprojectai.org/cuad"]					,      reference_urls=["https://www.atticusprojectai.org/cuad"]					,      )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
             lowerCamelCase__		:				Any    =  [
                 {
                     "paragraphs": [
                         {
                             "qas": [
                                 {
                                     "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                     "id": ref["id"],
                                 }
                                 for ref in references
                             ]
                         }
                     ]
                 }
             ]
             lowerCamelCase__		:				Optional[int]    =  evaluate(dataset=__lowerCamelCase					,      predictions=__lowerCamelCase       )
             return score


