

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		:       Dict						    =	logging.getLogger(__name__)

_UpperCAmelCase		:       Optional[int]						    =	"pytorch_model.bin"







@dataclasses.dataclass
class 			lowercase    :
				__SCREAMING_SNAKE_CASE :			str	      =      dataclasses.field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}      )
				__SCREAMING_SNAKE_CASE :			Optional[str]	      =      dataclasses.field(
				    default=UpperCamelCase_     ,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}     ,  )







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







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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   datasets.concatenate_datasets([infer_input, infer_output]       ,							axis=1				)

							if args.do_filter_by_confidence:
														snake_case_          =   dataset.filter(lambda UpperCamelCase__				: example["probability"] > args.confidence_threshold				)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														snake_case_          =   int(eval_result * len(__lowerCAmelCase				)				)
														print(__lowerCAmelCase				)
														snake_case_          =   dataset.sort('probability'       ,							reverse=__lowerCAmelCase				)
														snake_case_          =   dataset.select(range(__lowerCAmelCase				)				)

							snake_case_          =   dataset.remove_columns(['label', 'probability']				)
							snake_case_          =   dataset.rename_column('prediction'       ,							'label'				)
							snake_case_          =   dataset.map(lambda UpperCamelCase__				: {"label": idalabel[example["label"]]}				)
							snake_case_          =   dataset.shuffle(seed=args.seed				)

							snake_case_          =   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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							**UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   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()

							snake_case_          =   STModelArguments(model_name_or_path=__lowerCAmelCase				)
							snake_case_          =   STDataArguments(train_file=__lowerCAmelCase       ,							infer_file=__lowerCAmelCase				)
							snake_case_          =   STTrainingArguments(output_dir=__lowerCAmelCase				)
							snake_case_          =   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
							snake_case_          =   {}
							snake_case_          =   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
							snake_case_          =   args.train_file
							snake_case_          =   args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														snake_case_          =   args.eval_file

							for key in data_files:
														snake_case_          =   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:
																					snake_case_          =   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...'				)
							snake_case_          =   F'''{args.output_dir}/self-train_iter-{{}}'''.format
							snake_case_          =   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()

							snake_case_          =   None
							snake_case_          =   None
							snake_case_          =   0
							snake_case_          =   False
							# Show the progress bar
							snake_case_          =   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				)				):
														snake_case_          =   data_dir_format(__lowerCAmelCase				)
														assert os.path.exists(__lowerCAmelCase				)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														snake_case_          =   os.path.join(__lowerCAmelCase       ,							'stage-1'				)
														snake_case_          =   {
														    """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}				)

														snake_case_          =   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
																					snake_case_          =   os.path.join(__lowerCAmelCase       ,							'best-checkpoint'				)
																					snake_case_          =   os.path.join(__lowerCAmelCase       ,							'stage-2'				)
																					# Update arguments_dict
																					snake_case_          =   model_path
																					snake_case_          =   data_files["""train"""]
																					snake_case_          =   current_output_dir

																					snake_case_          =   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				)

														snake_case_          =   iteration
														snake_case_          =   data_dir_format(iteration + 1				)

														snake_case_          =   AutoConfig.from_pretrained(os.path.join(__lowerCAmelCase       ,							'best-checkpoint'				)				)
														snake_case_          =   config.idalabel
														snake_case_          =   os.path.join(__lowerCAmelCase       ,							'eval_results_best-checkpoint.json'				)
														snake_case_          =   os.path.join(__lowerCAmelCase       ,							'test_results_best-checkpoint.json'				)
														assert os.path.exists(__lowerCAmelCase				)

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

														snake_case_          =   os.path.join(__lowerCAmelCase       ,							F'''train_pseudo.{args.data_file_extension}'''				)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					snake_case_          =   eval_result

																					if best_iteration is None:
																												snake_case_          =   new_iteration
																												snake_case_          =   new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			snake_case_          =   new_iteration
																																			snake_case_          =   new_eval_result
																																			snake_case_          =   0
																												else:
																																			if new_eval_result == best_eval_result:
																																										snake_case_          =   new_iteration
																																										snake_case_          =   new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			snake_case_          =   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'				)       ,							)








import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   BigBirdConfig.from_json_file(UpperCamelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)

							if is_trivia_qa:
														snake_case_          =   BigBirdForQuestionAnswering(UpperCamelCase__				)
							else:
														snake_case_          =   BigBirdForPreTraining(UpperCamelCase__				)

							# Load weights from tf checkpoint
							load_tf_weights_in_big_bird(UpperCamelCase__       ,							UpperCamelCase__       ,							is_trivia_qa=UpperCamelCase__				)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)




