
"""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


lowercase__		:Optional[int]			  =   logging.getLogger(__name__)

lowercase__		:Optional[Any]			  =   "pytorch_model.bin"







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







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







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



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						datasets.concatenate_datasets([infer_input, infer_output]			,      axis=1		)

					if args.do_filter_by_confidence:
										lowercase						     =						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
										lowercase						     =						int(eval_result * len(lowerCAmelCase__		)		)
										print(lowerCAmelCase__		)
										lowercase						     =						dataset.sort('''probability'''			,      reverse=lowerCAmelCase__		)
										lowercase						     =						dataset.select(range(lowerCAmelCase__		)		)

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

					lowercase						     =						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 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      **lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						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()

					lowercase						     =						STModelArguments(model_name_or_path=lowerCAmelCase__		)
					lowercase						     =						STDataArguments(train_file=lowerCAmelCase__			,      infer_file=lowerCAmelCase__		)
					lowercase						     =						STTrainingArguments(output_dir=lowerCAmelCase__		)
					lowercase						     =						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
					lowercase						     =						{}
					lowercase						     =						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
					lowercase						     =						args.train_file
					lowercase						     =						args.infer_file

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

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

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

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

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

															lowercase						     =						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__		)

										lowercase						     =						iteration
										lowercase						     =						data_dir_format(iteration + 1		)

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

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

										lowercase						     =						os.path.join(lowerCAmelCase__			,      f'train_pseudo.{args.data_file_extension}'		)

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

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

																				if early_stopping_patience_counter >= args.early_stopping_patience:
																									lowercase						     =						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 collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__		:Dict			  =   logging.get_logger(__name__)

lowercase__		:Optional[Any]			  =   "▁"

lowercase__		:str			  =   {"vocab_file": "prophetnet.tokenizer"}

lowercase__		:List[str]			  =   {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowercase__		:Optional[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowercase__		:List[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						collections.OrderedDict()
					with open(lowerCAmelCase__			,      '''r'''			,      encoding='''utf-8'''		) as reader:
										lowercase						     =						reader.readlines()
					for index, token in enumerate(lowerCAmelCase__		):
										lowercase						     =						token.rstrip('''\n'''		)
										lowercase						     =						index
					return vocab







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	Any	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Any	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




							def __init__(    self       ,A__       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[UNK]"       ,A__="[PAD]"       ,A__="[CLS]"       ,A__="[MASK]"       ,A__ = None       ,**A__       ,):
												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,sep_token=A__       ,unk_token=A__       ,pad_token=A__       ,cls_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))
												lowercase						     =						vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												lowercase						     =						{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

												for i in range(1_0):
																	lowercase						     =						f'[unused{i}]'
																	lowercase						     =						5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												lowercase						     =						1_2
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(A__)




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												# for backward compatibility
												if not hasattr(self       ,'''sp_model_kwargs'''):
																	lowercase						     =						{}

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

												if token_ids_a is None:
																	return ([0] * len(A__)) + [1]
												return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]

												if token_ids_a is None:
																	return len(token_ids_a + sep) * [0]
												return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]




							@property
							def 			A__				(    self):
												return len(self.sp_model) + self.fairseq_offset




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset)




							def 			A__				(    self       ,A__):
												lowercase						     =						''''''.join(A__).replace(A__       ,''' ''').strip()
												return out_string




							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)





							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




