
import argparse
import json
import re
import string
import sys

import numpy as np


SCREAMING_SNAKE_CASE							:int											=       0.5


def 		UpperCAmelCase    (			a_	,     a_		)	->			Dict:







				"""simple docstring"""



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

				for token in remove_tokens:
								__A         =				ground_truth.replace(snake_case__	,     ""		)
								__A         =				prediction.replace(snake_case__	,     ""		)

				__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(snake_case__		)
				__A         =				ground_truth.union(snake_case__		)
				__A         =				len(snake_case__		) / len(snake_case__		)
				return jaccard


def 		UpperCAmelCase    (			a_		)	->			Optional[Any]:







				"""simple docstring"""



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

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(snake_case__		)		)		)		)


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				__A    ,							__A    ,							__A         =				0, 0, 0

				__A         =				"Parties" in qa_id

				# first check if ground truth is empty
				if len(snake_case__		) == 0:
								if len(snake_case__		) > 0:
												fp += len(snake_case__		)  # false positive for each one
				else:
								for ground_truth in ground_truths:
												assert len(snake_case__		) > 0
												# check if there is a match
												__A         =				False
												for pred in predictions:
																if substr_ok:
																				__A         =				get_jaccard(snake_case__	,     snake_case__		) >= IOU_THRESH or ground_truth in pred
																else:
																				__A         =				get_jaccard(snake_case__	,     snake_case__		) >= 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(snake_case__		) > 0
																if substr_ok:
																				__A         =				get_jaccard(snake_case__	,     snake_case__		) >= IOU_THRESH or ground_truth in pred
																else:
																				__A         =				get_jaccard(snake_case__	,     snake_case__		) >= 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 		UpperCAmelCase    (			a_		)	->			Optional[Any]:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_		)	->			Tuple:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Any:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_		)	->			Optional[int]:







				"""simple docstring"""



				return normalize_answer(snake_case__		) == normalize_answer(snake_case__		)


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			List[str]:







				"""simple docstring"""



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


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[Any]:







				"""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(snake_case__	,     file=sys.stderr		)
																				continue
																__A         =				[x["text"] for x in qa["answers"]]
																__A         =				predictions[qa["id"]]
																__A    ,							__A         =				compute_precision_recall(snake_case__	,     snake_case__	,     qa["id"]		)

																precisions.append(snake_case__		)
																recalls.append(snake_case__		)

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

																exact_match += metric_max_over_ground_truths(snake_case__	,     snake_case__	,     snake_case__		)

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

				__A         =				1_0_0.0 * fa / total
				__A         =				1_0_0.0 * exact_match / total
				__A         =				get_aupr(snake_case__	,     snake_case__		)

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


import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
					import torch

					from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
					    SpeechaTextaDecoder,
					    SpeechaTextaForCausalLM,
					)





@require_torch
class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				Tuple							,A      :				Union[str, Any]=99							,A      :				Dict=13							,A      :				int=16							,A      :				int=7							,A      :				Optional[int]=True							,A      :				str=True							,A      :				Optional[Any]=True							,A      :				Optional[int]=False							,A      :				Dict=True							,A      :				Any=2							,A      :				List[Any]=32							,A      :				Optional[Any]=4							,A      :				Tuple=4							,A      :				List[Any]=30							,A      :				Optional[Any]=0							,A      :				str=1							,A      :				List[Any]=2							,A      :				List[Any]=None							,):
					__A         =				parent
					__A         =				batch_size
					__A         =				decoder_seq_length
					# For common tests
					__A         =				self.decoder_seq_length
					__A         =				is_training
					__A         =				use_attention_mask
					__A         =				use_labels

					__A         =				vocab_size
					__A         =				d_model
					__A         =				d_model
					__A         =				decoder_layers
					__A         =				decoder_layers
					__A         =				decoder_ffn_dim
					__A         =				decoder_attention_heads
					__A         =				decoder_attention_heads
					__A         =				eos_token_id
					__A         =				bos_token_id
					__A         =				pad_token_id
					__A         =				decoder_start_token_id
					__A         =				use_cache
					__A         =				max_position_embeddings

					__A         =				None
					__A         =				decoder_seq_length
					__A         =				2
					__A         =				1






	def        UpperCamelCase_						( self      :				Tuple				):
					__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,self.vocab_size				)

					__A         =				None
					if self.use_attention_mask:
									__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,vocab_size=2				)

					__A         =				None
					if self.use_labels:
									__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,self.vocab_size				)

					__A         =				SpeechaTextaConfig(
					    vocab_size=self.vocab_size							,d_model=self.d_model							,decoder_layers=self.decoder_layers							,decoder_ffn_dim=self.decoder_ffn_dim							,decoder_attention_heads=self.decoder_attention_heads							,eos_token_id=self.eos_token_id							,bos_token_id=self.bos_token_id							,use_cache=self.use_cache							,pad_token_id=self.pad_token_id							,decoder_start_token_id=self.decoder_start_token_id							,max_position_embeddings=self.max_position_embeddings							,)

					return (
					    config,
					    input_ids,
					    attention_mask,
					    lm_labels,
					)






	def        UpperCamelCase_						( self      :				Tuple							,A      :				List[str]							,A      :				Tuple							,A      :				Optional[int]							,A      :				List[str]							,):
					__A         =				True
					__A         =				SpeechaTextaDecoder(config=A				).to(A				).eval()
					__A         =				input_ids[:2]

					input_ids[input_ids == 0] += 1
					# first forward pass
					__A         =				model(A							,use_cache=A				)
					__A         =				model(A				)
					__A         =				model(A							,use_cache=A				)

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

					__A         =				outputs["past_key_values"]

					# create hypothetical next token and extent to next_input_ids
					__A         =				ids_tensor((2, 1)							,config.vocab_size - 1				) + 1

					# append to next input_ids and
					__A         =				torch.cat([input_ids, next_tokens]							,dim=-1				)

					print(A				)

					__A         =				model(A				)["last_hidden_state"]
					__A         =				model(A							,past_key_values=A				)["last_hidden_state"]

					# select random slice
					__A         =				ids_tensor((1,)							,output_from_past.shape[-1]				).item()
					__A         =				output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
					__A         =				output_from_past[:, 0, random_slice_idx].detach()

					# test that outputs are equal for slice
					assert torch.allclose(A							,A							,atol=1E-3				)







	def        UpperCamelCase_						( self      :				Any				):
					__A         =				self.prepare_config_and_inputs()
					(
					    (
					    __A
					)    ,							(
					    __A
					)    ,							(
					    __A
					)    ,							(
					    __A
					)    ,							
					)         =				config_and_inputs

					__A         =				{
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
	snake_case_      					=  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
	snake_case_      					=  {"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
	snake_case_      					=  True
	snake_case_      					=  False






	def        UpperCamelCase_						( self      :				Any							,):
					__A         =				SpeechaTextaStandaloneDecoderModelTester(self							,is_training=A				)
					__A         =				ConfigTester(self							,config_class=A				)






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					pass






	def        UpperCamelCase_						( self      :				int				):
					pass






	def        UpperCamelCase_						( self      :				Tuple				):
					pass






	def        UpperCamelCase_						( self      :				str				):
					self.config_tester.run_common_tests()






	def        UpperCamelCase_						( self      :				Dict				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past(*A				)







	def        UpperCamelCase_						( self      :				str				):
					return
