"""simple docstring"""



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_UpperCamelCase								=		logging.getLogger(__name__)

_UpperCamelCase								=		"pytorch_model.bin"
@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				dataclasses.field(
			    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}							,   )





@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}     )
			_UpperCamelCase  :						str					     =				dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'A csv or a json file containing the validation data.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'The name of the task to train on.'}							,   )
			_UpperCamelCase  :						Optional[List[str]]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'The list of labels for the task.'}     )





@dataclasses.dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				dataclasses.field(
			    metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default='accuracy'							,   metadata={'help': 'The evaluation metric used for the task.'}     )
			_UpperCamelCase  :						Optional[str]					     =				dataclasses.field(
			    default='no'							,   metadata={
			        'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
			    }							,   )
			_UpperCamelCase  :						Optional[int]					     =				dataclasses.field(
			    default=10							,   metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}							,   )
			_UpperCamelCase  :						Optional[float]					     =				dataclasses.field(
			    default=0.0							,   metadata={
			        'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
			    }							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}							,   )
			_UpperCamelCase  :						Optional[bool]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}							,   )
			_UpperCamelCase  :						Optional[float]					     =				dataclasses.field(
			    default=0.0							,   metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}							,   )
			_UpperCamelCase  :						Optional[int]					     =				dataclasses.field(
			    default=100							,   metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}							,   )
			_UpperCamelCase  :						Optional[int]					     =				dataclasses.field(
			    default=__A							,   metadata={'help': 'Random seed for initialization.'}							,   )





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			datasets.concatenate_datasets([infer_input, infer_output]			,  axis=1							)

							if args.do_filter_by_confidence:
														A__       :     Union[str, Any]								=			dataset.filter(lambda lowerCAmelCase							: example["probability"] > args.confidence_threshold							)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														A__       :     Dict								=			int(eval_result * len(lowerCAmelCase							)							)
														print(lowerCAmelCase							)
														A__       :     Dict								=			dataset.sort("""probability"""			,  reverse=lowerCAmelCase							)
														A__       :     Tuple								=			dataset.select(range(lowerCAmelCase							)							)

							A__       :     Optional[int]								=			dataset.remove_columns(["""label""", """probability"""]							)
							A__       :     Dict								=			dataset.rename_column("""prediction"""			,  """label"""							)
							A__       :     str								=			dataset.map(lambda lowerCAmelCase							: {"label": idalabel[example["label"]]}							)
							A__       :     Optional[Any]								=			dataset.shuffle(seed=args.seed							)

							A__       :     Any								=			os.path.join(lowerCAmelCase			,  F'''train_pseudo.{args.data_file_extension}'''							)
							if args.data_file_extension == "csv":
														dataset.to_csv(lowerCAmelCase			,  index=lowerCAmelCase							)
							else:
														dataset.to_json(lowerCAmelCase							)



def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  **lowerCAmelCase							):
							A__       :     Dict								=			Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,  datefmt="""%m/%d/%Y %H:%M:%S"""			,  level=logging.INFO			,  )
							logger.info(accelerator.state							)

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR							)

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							A__       :     Union[str, Any]								=			STModelArguments(model_name_or_path=lowerCAmelCase							)
							A__       :     Tuple								=			STDataArguments(train_file=lowerCAmelCase			,  infer_file=lowerCAmelCase							)
							A__       :     List[str]								=			STTrainingArguments(output_dir=lowerCAmelCase							)
							A__       :     Tuple								=			argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(lowerCAmelCase							).items():
																					setattr(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)

							for key, value in kwargs.items():
														if hasattr(lowerCAmelCase			,  lowerCAmelCase							):
																					setattr(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)

    # Sanity checks
							A__       :     Optional[int]								=			{}
							A__       :     Tuple								=			None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							A__       :     Tuple								=			args.train_file
							A__       :     int								=			args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														A__       :     Optional[Any]								=			args.eval_file

							for key in data_files:
														A__       :     List[str]								=			data_files[key].split("""."""							)[-1]
														assert extension in ["csv", "json"], F'''`{key}_file` should be a csv or a json file.'''
														if args.data_file_extension is None:
																					A__       :     int								=			extension
														else:
																					assert extension == args.data_file_extension, F'''`{key}_file` should be a {args.data_file_extension} file`.'''

							assert (
							    args.eval_metric in datasets.list_metrics()
							), F'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed							)

							logger.info("""Creating the initial data directory for self-training..."""							)
							A__       :     Union[str, Any]								=			F'''{args.output_dir}/self-train_iter-{{}}'''.format
							A__       :     Any								=			data_dir_format(0							)

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir			,  exist_ok=lowerCAmelCase							)
																					os.makedirs(lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
							accelerator.wait_for_everyone()

							A__       :     Union[str, Any]								=			None
							A__       :     str								=			None
							A__       :     Union[str, Any]								=			0
							A__       :     List[Any]								=			False
							# Show the progress bar
							A__       :     List[Any]								=			tqdm(range(args.max_selftrain_iterations							)			,  disable=not accelerator.is_local_main_process							)

							# Self-train
							for iteration in range(0			,  int(args.max_selftrain_iterations							)							):
														A__       :     List[str]								=			data_dir_format(lowerCAmelCase							)
														assert os.path.exists(lowerCAmelCase							)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														A__       :     int								=			os.path.join(lowerCAmelCase			,  """stage-1"""							)
														A__       :     Any								=			{
														    """accelerator""": accelerator,
														    """model_name_or_path""": args.model_name_or_path,
														    """cache_dir""": args.cache_dir,
														    """do_train""": True,
														    """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
														    """do_eval""": True if args.eval_file is not None else False,
														    """eval_file""": data_files["""eval"""],
														    """do_predict""": True,
														    """infer_file""": data_files["""infer"""],
														    """task_name""": args.task_name,
														    """label_list""": args.label_list,
														    """output_dir""": current_output_dir,
														    """eval_metric""": args.eval_metric,
														    """evaluation_strategy""": args.evaluation_strategy,
														    """early_stopping_patience""": args.early_stopping_patience,
														    """early_stopping_threshold""": args.early_stopping_threshold,
														    """seed""": args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(lowerCAmelCase			,  lowerCAmelCase							):
																												arguments_dict.update({key: value}							)

														A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  """best-checkpoint"""			,  lowerCAmelCase							)
														if os.path.exists(lowerCAmelCase							):
																					logger.info(
																					    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""			,  lowerCAmelCase			,  lowerCAmelCase			,  )
														else:
																					logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""			,  lowerCAmelCase							)
																					finetune(**lowerCAmelCase							)
																					accelerator.wait_for_everyone()
																					assert os.path.exists(lowerCAmelCase							)
																					logger.info("""Self-training job completed: iteration: %d, stage: 1."""			,  lowerCAmelCase							)

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  """best-checkpoint"""							)
																					A__       :     str								=			os.path.join(lowerCAmelCase			,  """stage-2"""							)
																					# Update arguments_dict
																					A__       :     Dict								=			model_path
																					A__       :     Optional[Any]								=			data_files["""train"""]
																					A__       :     Optional[Any]								=			current_output_dir

																					A__       :     Union[str, Any]								=			os.path.join(lowerCAmelCase			,  """best-checkpoint"""			,  lowerCAmelCase							)
																					if os.path.exists(lowerCAmelCase							):
																												logger.info(
																												    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""			,  lowerCAmelCase			,  lowerCAmelCase			,  )
																					else:
																												logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""			,  lowerCAmelCase							)
																												finetune(**lowerCAmelCase							)
																												accelerator.wait_for_everyone()
																												assert os.path.exists(lowerCAmelCase							)
																												logger.info("""Self-training job completed: iteration: %d, stage: 2."""			,  lowerCAmelCase							)

														A__       :     List[str]								=			iteration
														A__       :     List[str]								=			data_dir_format(iteration + 1							)

														A__       :     List[Any]								=			AutoConfig.from_pretrained(os.path.join(lowerCAmelCase			,  """best-checkpoint"""							)							)
														A__       :     int								=			config.idalabel
														A__       :     Tuple								=			os.path.join(lowerCAmelCase			,  """eval_results_best-checkpoint.json"""							)
														A__       :     str								=			os.path.join(lowerCAmelCase			,  """test_results_best-checkpoint.json"""							)
														assert os.path.exists(lowerCAmelCase							)

														with open(lowerCAmelCase			,  """r"""							) as f:
																					A__       :     List[str]								=			float(json.load(lowerCAmelCase							)[args.eval_metric]							)
														A__       :     List[Any]								=			os.path.join(lowerCAmelCase			,  """infer_output_best-checkpoint.csv"""							)
														assert os.path.exists(lowerCAmelCase							)
														# Loading the dataset from local csv or json files.
														A__       :     List[str]								=			load_dataset(args.data_file_extension			,  data_files={"""data""": data_files["""infer"""]}							)["""data"""]
														A__       :     List[Any]								=			load_dataset("""csv"""			,  data_files={"""data""": infer_output_file}							)["""data"""]

														if accelerator.is_main_process:
																					os.makedirs(lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
																					shutil.copy(lowerCAmelCase			,  os.path.join(lowerCAmelCase			,  F'''eval_results_iter-{iteration}.json'''							)							)
																					if os.path.exists(lowerCAmelCase							):
																												shutil.copy(lowerCAmelCase			,  os.path.join(lowerCAmelCase			,  F'''test_results_iter-{iteration}.json'''							)							)
																					create_pseudo_labeled_data(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														accelerator.wait_for_everyone()

														A__       :     int								=			os.path.join(lowerCAmelCase			,  F'''train_pseudo.{args.data_file_extension}'''							)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					A__       :     Optional[int]								=			eval_result

																					if best_iteration is None:
																												A__       :     List[str]								=			new_iteration
																												A__       :     List[str]								=			new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			A__       :     List[Any]								=			new_iteration
																																			A__       :     str								=			new_eval_result
																																			A__       :     Dict								=			0
																												else:
																																			if new_eval_result == best_eval_result:
																																										A__       :     Optional[int]								=			new_iteration
																																										A__       :     Optional[int]								=			new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			A__       :     Tuple								=			True

														progress_bar.update(1							)

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info("""Best iteration: %d"""			,  lowerCAmelCase							)
														logger.info("""Best evaluation result: %s = %f"""			,  args.eval_metric			,  lowerCAmelCase							)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCAmelCase			,  F'''eval_results_iter-{iteration}.json'''							)			,  os.path.join(lowerCAmelCase			,  """eval_results_best-iteration.json"""							)			,  )
							else:
														# Assume that the last iteration is the best
														logger.info("""Best iteration: %d"""			,  args.max_selftrain_iterations - 1							)
														logger.info("""Best evaluation result: %s = %f"""			,  args.eval_metric			,  lowerCAmelCase							)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCAmelCase			,  F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''							)			,  os.path.join(lowerCAmelCase			,  """eval_results_best-iteration.json"""							)			,  )






"""simple docstring"""



import datasets

from .evaluate import evaluate


_UpperCamelCase								=		"\\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"

_UpperCamelCase								=		"\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"

_UpperCamelCase								=		"\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 						__UpperCAmelCase    (datasets.Metric     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''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   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


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





