

'''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 	lowerCAmelCase  :
       lowerCAmelCase_												=			dataclasses.field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} ,      )






@dataclasses.dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
       lowerCAmelCase_												=			dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "A csv or a json file containing the validation data."} )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "The name of the task to train on."} ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "The list of labels for the task."} )






@dataclasses.dataclass
class 	lowerCAmelCase  :
       lowerCAmelCase_												=			dataclasses.field(
           metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
       lowerCAmelCase_												=			dataclasses.field(
           default="accuracy" ,      metadata={"help": "The evaluation metric used for the task."} )
       lowerCAmelCase_												=			dataclasses.field(
           default="no" ,      metadata={
               "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
           } ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=1_0 ,      metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=0.0 ,      metadata={
               "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
           } ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=0.0 ,      metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=1_0_0 ,      metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} ,      )
       lowerCAmelCase_												=			dataclasses.field(
           default=a_ ,      metadata={"help": "Random seed for initialization."} ,      )






def 					__UpperCamelCase				(						lowercase__    :	Optional[int],       lowercase__    :	Tuple,       lowercase__    :	List[Any],       lowercase__    :	List[str],       lowercase__    :	Tuple,       lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





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

     if args.do_filter_by_confidence:
          __lowercase      		=dataset.filter(lambda lowercase__			: 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(__snake_case			)			)
          print(__snake_case			)
          __lowercase      		=dataset.sort('probability',       reverse=__snake_case			)
          __lowercase      		=dataset.select(range(__snake_case			)			)

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

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







def 					__UpperCamelCase				(						lowercase__    :	int,       lowercase__    :	List[str],       lowercase__    :	List[Any],       lowercase__    :	Union[str, Any],       **lowercase__    :	Tuple			):






     '''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=__snake_case			)
     __lowercase      		=STDataArguments(train_file=__snake_case,       infer_file=__snake_case			)
     __lowercase      		=STTrainingArguments(output_dir=__snake_case			)
     __lowercase      		=argparse.Namespace()

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

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

    # 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=__snake_case			)
               os.makedirs(__snake_case,       exist_ok=__snake_case			)
     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(__snake_case			)
          assert os.path.exists(__snake_case			)

          # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
          # iteration > 0
          __lowercase      		=os.path.join(__snake_case,       '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(__snake_case,       __snake_case			):
                    arguments_dict.update({key: value}			)

          __lowercase      		=os.path.join(__snake_case,       'best-checkpoint',       __snake_case			)
          if os.path.exists(__snake_case			):
               logger.info(
                   'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.',       __snake_case,       __snake_case,       )
          else:
               logger.info('***** Running self-training: iteration: %d, stage: 1 *****',       __snake_case			)
               finetune(**__snake_case			)
               accelerator.wait_for_everyone()
               assert os.path.exists(__snake_case			)
               logger.info('Self-training job completed: iteration: %d, stage: 1.',       __snake_case			)

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

               __lowercase      		=os.path.join(__snake_case,       'best-checkpoint',       __snake_case			)
               if os.path.exists(__snake_case			):
                    logger.info(
                        'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.',       __snake_case,       __snake_case,       )
               else:
                    logger.info('***** Running self-training: iteration: %d, stage: 2 *****',       __snake_case			)
                    finetune(**__snake_case			)
                    accelerator.wait_for_everyone()
                    assert os.path.exists(__snake_case			)
                    logger.info('Self-training job completed: iteration: %d, stage: 2.',       __snake_case			)

          __lowercase      		=iteration
          __lowercase      		=data_dir_format(iteration + 1			)

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

          with open(__snake_case,       'r'			) as f:
               __lowercase      		=float(json.load(__snake_case			)[args.eval_metric]			)
          __lowercase      		=os.path.join(__snake_case,       'infer_output_best-checkpoint.csv'			)
          assert os.path.exists(__snake_case			)
          # 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(__snake_case,       exist_ok=__snake_case			)
               shutil.copy(__snake_case,       os.path.join(__snake_case,       F'''eval_results_iter-{iteration}.json'''			)			)
               if os.path.exists(__snake_case			):
                    shutil.copy(__snake_case,       os.path.join(__snake_case,       F'''test_results_iter-{iteration}.json'''			)			)
               create_pseudo_labeled_data(__snake_case,       __snake_case,       __snake_case,       __snake_case,       __snake_case,       __snake_case			)
          accelerator.wait_for_everyone()

          __lowercase      		=os.path.join(__snake_case,       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',       __snake_case			)
          logger.info('Best evaluation result: %s = %f',       args.eval_metric,       __snake_case			)
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(__snake_case,       F'''eval_results_iter-{iteration}.json'''			),       os.path.join(__snake_case,       '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,       __snake_case			)
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               shutil.copy(
                   os.path.join(__snake_case,       F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''			),       os.path.join(__snake_case,       'eval_results_best-iteration.json'			),       )




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[Any]:
       lowercase						,       lowercase       :							Tuple									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(__snake_case	,					__snake_case	,					bias=__snake_case    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

       lowercase       :							int									=				{key.replace("decoder"	,					"model"    ): val for key, val in state_dict.items()}

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					max_position_embeddings=args.max_target_positions	,					num_layers=args.decoder_layers	,					attention_heads=args.decoder_attention_heads	,					ffn_dim=args.decoder_ffn_embed_dim	,					d_model=args.decoder_embed_dim	,					layerdrop=args.decoder_layerdrop	,					dropout=args.dropout	,					attention_dropout=args.attention_dropout	,					activation_dropout=args.activation_dropout	,					activation_function="gelu"	,					scale_embedding=not args.no_scale_embedding	,					tie_word_embeddings=args.share_decoder_input_output_embed	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   _A     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


