"""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


__A :     Union[str, Any]				       =				logging.getLogger(__name__)

__A :     Union[str, Any]				       =				"pytorch_model.bin"






@dataclasses.dataclass
class 	__lowerCAmelCase					:




    '''simple docstring'''






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






@dataclasses.dataclass
class 	__lowerCAmelCase					:




    '''simple docstring'''






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






@dataclasses.dataclass
class 	__lowerCAmelCase					:




    '''simple docstring'''






    __magic_name__   :			str       		=       dataclasses.field(
        metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""})
    __magic_name__   :			Optional[str]       		=       dataclasses.field(
        default="""accuracy"""  ,   metadata={"""help""": """The evaluation metric used for the task."""})
    __magic_name__   :			Optional[str]       		=       dataclasses.field(
        default="""no"""  ,   metadata={
            """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
        }  ,   )
    __magic_name__   :			Optional[int]       		=       dataclasses.field(
        default=10  ,   metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}  ,   )
    __magic_name__   :			Optional[float]       		=       dataclasses.field(
        default=0.0  ,   metadata={
            """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
        }  ,   )
    __magic_name__   :			Optional[bool]       		=       dataclasses.field(
        default=a__  ,   metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}  ,   )
    __magic_name__   :			Optional[bool]       		=       dataclasses.field(
        default=a__  ,   metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}  ,   )
    __magic_name__   :			Optional[bool]       		=       dataclasses.field(
        default=a__  ,   metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}  ,   )
    __magic_name__   :			Optional[float]       		=       dataclasses.field(
        default=0.0  ,   metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}  ,   )
    __magic_name__   :			Optional[int]       		=       dataclasses.field(
        default=1_00  ,   metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}  ,   )
    __magic_name__   :			Optional[int]       		=       dataclasses.field(
        default=a__  ,   metadata={"""help""": """Random seed for initialization."""}  ,   )



def 		lowercase (       UpperCamelCase     :     Union[str, Any]       ,  UpperCamelCase     :     Optional[int]       ,  UpperCamelCase     :     Optional[Any]       ,  UpperCamelCase     :     Tuple       ,  UpperCamelCase     :     Any       ,  UpperCamelCase     :     Tuple		):





      """simple docstring"""
      A__	:      int      =datasets.concatenate_datasets([infer_input, infer_output]       ,  axis=1		)

      if args.do_filter_by_confidence:
            A__	:      Any      =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
            A__	:      Any      =int(eval_result * len(UpperCamelCase		)		)
            print(UpperCamelCase		)
            A__	:      List[str]      =dataset.sort("probability"       ,  reverse=UpperCamelCase		)
            A__	:      int      =dataset.select(range(UpperCamelCase		)		)

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

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



def 		lowercase (       UpperCamelCase     :     str       ,  UpperCamelCase     :     Union[str, Any]       ,  UpperCamelCase     :     List[str]       ,  UpperCamelCase     :     str       ,  **UpperCamelCase     :     Optional[int]		):





      """simple docstring"""
      A__	:      Tuple      =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__	:      Optional[Any]      =STModelArguments(model_name_or_path=UpperCamelCase		)
      A__	:      Dict      =STDataArguments(train_file=UpperCamelCase       ,  infer_file=UpperCamelCase		)
      A__	:      Dict      =STTrainingArguments(output_dir=UpperCamelCase		)
      A__	:      Dict      =argparse.Namespace()

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

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

    # Sanity checks
      A__	:      List[Any]      ={}
      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__	:      List[str]      =args.train_file
      A__	:      List[str]      =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__	:      Tuple      =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__	:      Union[str, Any]      =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__	:      List[Any]      =F'''{args.output_dir}/self-train_iter-{{}}'''.format
      A__	:      List[Any]      =data_dir_format(0		)

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

      A__	:      Union[str, Any]      =None
      A__	:      List[str]      =None
      A__	:      Optional[int]      =0
      A__	:      Dict      =False
      # Show the progress bar
      A__	:      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(UpperCamelCase		)
            assert os.path.exists(UpperCamelCase		)

            # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
            # iteration > 0
            A__	:      str      =os.path.join(UpperCamelCase       ,  "stage-1"		)
            A__	:      List[str]      ={
                "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(UpperCamelCase       ,  UpperCamelCase		):
                        arguments_dict.update({key: value}		)

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

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

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

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

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

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

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

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

            if args.evaluation_strategy != IntervalStrategy.NO.value:
                  A__	:      Any      =eval_result

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

                        if early_stopping_patience_counter >= args.early_stopping_patience:
                              A__	:      Optional[int]      =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"       ,  UpperCamelCase		)
            logger.info("Best evaluation result: %s = %f"       ,  args.eval_metric       ,  UpperCamelCase		)
            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  shutil.copy(
                      os.path.join(UpperCamelCase       ,  F'''eval_results_iter-{iteration}.json'''		)       ,  os.path.join(UpperCamelCase       ,  "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       ,  UpperCamelCase		)
            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  shutil.copy(
                      os.path.join(UpperCamelCase       ,  F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''		)       ,  os.path.join(UpperCamelCase       ,  "eval_results_best-iteration.json"		)       ,  )









'''simple docstring'''






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCAmelCase       							=				[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def    _snake_case  (	A					,       A=None					,       A=None					,       A=None			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      True
							while ask_again:
														lowerCAmelCase__			  =      input(A			)
														try:
																					if default is not None and len(A			) == 0:
																												return default
																					return convert_value(A			) if convert_value is not None else result
														except Exception:
																					if error_message is not None:
																												print(A			)


def    _snake_case  (	A					,       A=[]					,       A=None					,       A=0			)		->       List[Any]:
							lowerCAmelCase__			  =      BulletMenu(A					,       A			)
							lowerCAmelCase__			  =      menu.run(default_choice=A			)
							return convert_value(A			) if convert_value is not None else result


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]			)


def    _snake_case  (	A			)		->       str:
							lowerCAmelCase__			  =      int(A			)
							return DynamoBackend(DYNAMO_BACKENDS[value]			).value


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]			)


def    _snake_case  (	A			)		->       List[str]:
							return {"yes": True, "no": False}[value.lower()]






class 						a__							(				argparse.RawDescriptionHelpFormatter    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      super()._format_usage(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      usage.replace('''<command> [<args>] '''   ,       ''''''  )
														return usage