




from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
				from .data.data_collator import DataCollator
				from .modeling_utils import PreTrainedModel
				from .tokenization_utils_base import PreTrainedTokenizerBase
				from .trainer_callback import TrainerCallback
				from .trainer_utils import EvalPrediction, PredictionOutput
				from .training_args import TrainingArguments


SCREAMING_SNAKE_CASE__    :    Optional[int]								=					logging.get_logger(__name__)



class 			a_					(    SCREAMING_SNAKE_CASE__    ):





	def __init__(       self			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = (None, None)			,    SCREAMING_SNAKE_CASE = None			,    )     ->				int:




				"""simple docstring"""



				super().__init__(
				    model=SCREAMING_SNAKE_CASE			,    args=SCREAMING_SNAKE_CASE			,    data_collator=SCREAMING_SNAKE_CASE			,    train_dataset=SCREAMING_SNAKE_CASE			,    eval_dataset=SCREAMING_SNAKE_CASE			,    tokenizer=SCREAMING_SNAKE_CASE			,    model_init=SCREAMING_SNAKE_CASE			,    compute_metrics=SCREAMING_SNAKE_CASE			,    callbacks=SCREAMING_SNAKE_CASE			,    optimizers=SCREAMING_SNAKE_CASE			,    preprocess_logits_for_metrics=SCREAMING_SNAKE_CASE			,    )

				# Override self.model.generation_config if a GenerationConfig is specified in args.
				# Priority: args.generation_config > model.generation_config > default GenerationConfig.
				if self.args.generation_config is not None:
							SCREAMING_SNAKE_CASE_			=	self.load_generation_config(self.args.generation_config			)
							SCREAMING_SNAKE_CASE_			=	gen_config





	@staticmethod
	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				GenerationConfig:




				"""simple docstring"""



				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
							return deepcopy(SCREAMING_SNAKE_CASE			)

				# str or Path
				SCREAMING_SNAKE_CASE_			=	Path(SCREAMING_SNAKE_CASE			) if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) else gen_config_arg
				SCREAMING_SNAKE_CASE_			=	None

				# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
				# This step is required in order to determine config_file_name
				if pretrained_model_name.is_file():
							SCREAMING_SNAKE_CASE_			=	pretrained_model_name.name
							SCREAMING_SNAKE_CASE_			=	pretrained_model_name.parent
				# dir path
				elif pretrained_model_name.is_dir():
							pass
				# model id or URL
				else:
							SCREAMING_SNAKE_CASE_			=	gen_config_arg

				SCREAMING_SNAKE_CASE_			=	GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				return gen_config





	def 	A_(       self			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = "eval"			,    **SCREAMING_SNAKE_CASE			,    )     ->				Dict[str, float]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	gen_kwargs.copy()
				if gen_kwargs.get('max_length'			) is None and gen_kwargs.get('max_new_tokens'			) is None:
							SCREAMING_SNAKE_CASE_			=	self.args.generation_max_length
				SCREAMING_SNAKE_CASE_			=	(
				    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'			) is not None else self.args.generation_num_beams
				)
				SCREAMING_SNAKE_CASE_			=	gen_kwargs

				return super().evaluate(SCREAMING_SNAKE_CASE			,    ignore_keys=SCREAMING_SNAKE_CASE			,    metric_key_prefix=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = "test"			,    **SCREAMING_SNAKE_CASE			,    )     ->				"PredictionOutput":




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	gen_kwargs.copy()
				if gen_kwargs.get('max_length'			) is None and gen_kwargs.get('max_new_tokens'			) is None:
							SCREAMING_SNAKE_CASE_			=	self.args.generation_max_length
				SCREAMING_SNAKE_CASE_			=	(
				    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'			) is not None else self.args.generation_num_beams
				)
				SCREAMING_SNAKE_CASE_			=	gen_kwargs

				return super().predict(SCREAMING_SNAKE_CASE			,    ignore_keys=SCREAMING_SNAKE_CASE			,    metric_key_prefix=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:




				"""simple docstring"""



				if not self.args.predict_with_generate or prediction_loss_only:
							return super().prediction_step(
							    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    prediction_loss_only=SCREAMING_SNAKE_CASE			,    ignore_keys=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	'labels' in inputs
				SCREAMING_SNAKE_CASE_			=	self._prepare_inputs(SCREAMING_SNAKE_CASE			)

				# XXX: adapt synced_gpus for fairscale as well
				# Priority (handled in generate):
				# gen_kwargs > model.generation_config > default GenerationConfig()

				if len(SCREAMING_SNAKE_CASE			) == 0 and hasattr(self			,    '_gen_kwargs'			):
							SCREAMING_SNAKE_CASE_			=	self._gen_kwargs.copy()

				if gen_kwargs.get('max_length'			) is None and gen_kwargs.get('max_new_tokens'			) is None:
							SCREAMING_SNAKE_CASE_			=	self.model.config.max_length
				SCREAMING_SNAKE_CASE_			=	(
				    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'			) is not None else self.model.config.num_beams
				)
				SCREAMING_SNAKE_CASE_			=	True if is_deepspeed_zeroa_enabled() else False
				SCREAMING_SNAKE_CASE_			=	(
				    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'			) is not None else default_synced_gpus
				)

				# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
				# (otherwise, it would continue generating from the padded `decoder_input_ids`)
				if (
				    "labels" in inputs
				    and "decoder_input_ids" in inputs
				    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
				):
							SCREAMING_SNAKE_CASE_			=	{k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
				SCREAMING_SNAKE_CASE_			=	self.model.generate(**SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)

				# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
				# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
				# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
				if self.model.generation_config._from_model_config:
							SCREAMING_SNAKE_CASE_			=	False

				# Retrieves GenerationConfig from model.generation_config
				SCREAMING_SNAKE_CASE_			=	self.model.generation_config
				# in case the batch is shorter than max length, the output should be padded
				if generated_tokens.shape[-1] < gen_config.max_length:
							SCREAMING_SNAKE_CASE_			=	self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE			,    gen_config.max_length			)
				elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
							SCREAMING_SNAKE_CASE_			=	self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE			,    gen_config.max_new_tokens + 1			)

				with torch.no_grad():
							if has_labels:
										with self.compute_loss_context_manager():
													SCREAMING_SNAKE_CASE_			=	model(**SCREAMING_SNAKE_CASE			)
										if self.label_smoother is not None:
													SCREAMING_SNAKE_CASE_			=	self.label_smoother(SCREAMING_SNAKE_CASE			,    inputs['labels']			).mean().detach()
										else:
													SCREAMING_SNAKE_CASE_			=	(outputs['loss'] if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) else outputs[0]).mean().detach()
							else:
										SCREAMING_SNAKE_CASE_			=	None

				if self.args.prediction_loss_only:
							return loss, None, None

				if has_labels:
							SCREAMING_SNAKE_CASE_			=	inputs['labels']
							if labels.shape[-1] < gen_config.max_length:
										SCREAMING_SNAKE_CASE_			=	self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE			,    gen_config.max_length			)
							elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
										SCREAMING_SNAKE_CASE_			=	self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE			,    gen_config.max_new_tokens + 1			)
				else:
							SCREAMING_SNAKE_CASE_			=	None

				return loss, generated_tokens, labels





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				List[str]:




				"""simple docstring"""



				if self.tokenizer is not None and hasattr(self.tokenizer			,    'pad_token_id'			):
							# If PAD token is not defined at least EOS token has to be defined
							SCREAMING_SNAKE_CASE_			=	(
							    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
							)
				else:
							if self.model.config.pad_token_id is not None:
										SCREAMING_SNAKE_CASE_			=	self.model.config.pad_token_id
							else:
										raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'			)

				SCREAMING_SNAKE_CASE_			=	pad_token_id * torch.ones(
				    (tensor.shape[0], max_length)			,    dtype=tensor.dtype			,    device=tensor.device			)
				SCREAMING_SNAKE_CASE_			=	tensor
				return padded_tensor






import argparse
import json
import re
import string
import sys

import numpy as np


SCREAMING_SNAKE_CASE__    :    List[Any]								=					0.5

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:


			'''simple docstring'''







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

			for token in remove_tokens:
						SCREAMING_SNAKE_CASE_			=	ground_truth.replace(SCREAMING_SNAKE_CASE					,						''   )
						SCREAMING_SNAKE_CASE_			=	prediction.replace(SCREAMING_SNAKE_CASE					,						''   )

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

			SCREAMING_SNAKE_CASE_			=	ground_truth.intersection(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	ground_truth.union(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE   ) / len(SCREAMING_SNAKE_CASE   )
			return jaccard

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       List[str]:


			'''simple docstring'''







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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE   )   )   )   )

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	0, 0, 0

			SCREAMING_SNAKE_CASE_			=	'Parties' in qa_id

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

									if not match_found:
												fp += 1

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

			return precision, recall

def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Tuple:


			'''simple docstring'''







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

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	process_precisions(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	np.trapz(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
			if np.isnan(SCREAMING_SNAKE_CASE   ):
						return 0
			return aupr

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Dict:


			'''simple docstring'''







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

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:


			'''simple docstring'''







			return normalize_answer(SCREAMING_SNAKE_CASE   ) == normalize_answer(SCREAMING_SNAKE_CASE   )

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[str]:


			'''simple docstring'''







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

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:


			'''simple docstring'''







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

												precisions.append(SCREAMING_SNAKE_CASE   )
												recalls.append(SCREAMING_SNAKE_CASE   )

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

												exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE_			=	[x for _, x in sorted(zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )   )]
			recalls.sort()

			SCREAMING_SNAKE_CASE_			=	100.0 * fa / total
			SCREAMING_SNAKE_CASE_			=	100.0 * exact_match / total
			SCREAMING_SNAKE_CASE_			=	get_aupr(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE_			=	get_prec_at_recall(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						recall_thresh=0.9   )
			SCREAMING_SNAKE_CASE_			=	get_prec_at_recall(SCREAMING_SNAKE_CASE					,						SCREAMING_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__    :    Optional[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__    :    Any								=					parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								SCREAMING_SNAKE_CASE__    :    Any								=					json.load(dataset_file)
								SCREAMING_SNAKE_CASE__    :    str								=					dataset_json["data"]
				with open(args.prediction_file) as prediction_file:
								SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))
