





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}

class     __SCREAMING_SNAKE_CASE   (       _SCREAMING_SNAKE_CASE    ):
							snake_case       : str         =    """table-transformer"""
							snake_case       : Union[str, Any]         =    ["""past_key_values"""]
							snake_case       : Optional[int]         =    {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}






							def __init__(  self					,	__lowerCAmelCase=True					,	__lowerCAmelCase=None					,	__lowerCAmelCase=3					,	__lowerCAmelCase=100					,	__lowerCAmelCase=6					,	__lowerCAmelCase=2048					,	__lowerCAmelCase=8					,	__lowerCAmelCase=6					,	__lowerCAmelCase=2048					,	__lowerCAmelCase=8					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=True					,	__lowerCAmelCase="relu"					,	__lowerCAmelCase=256					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=1.0					,	__lowerCAmelCase=False					,	__lowerCAmelCase="sine"					,	__lowerCAmelCase="resnet50"					,	__lowerCAmelCase=True					,	__lowerCAmelCase=False					,	__lowerCAmelCase=1					,	__lowerCAmelCase=5					,	__lowerCAmelCase=2					,	__lowerCAmelCase=1					,	__lowerCAmelCase=1					,	__lowerCAmelCase=5					,	__lowerCAmelCase=2					,	__lowerCAmelCase=0.1					,	**__lowerCAmelCase					,	):
														if backbone_config is not None and use_timm_backbone:
																					raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

														if not use_timm_backbone:
																					if backbone_config is None:
																												logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																												UpperCamelCase__			       =     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
																					elif isinstance(__lowerCAmelCase					,	__lowerCAmelCase					):
																												UpperCamelCase__			       =     backbone_config.get("""model_type"""					)
																												UpperCamelCase__			       =     CONFIG_MAPPING[backbone_model_type]
																												UpperCamelCase__			       =     config_class.from_dict(__lowerCAmelCase					)
																					# set timm attributes to None
																					UpperCamelCase__			       =     None, None, None

														UpperCamelCase__			       =     use_timm_backbone
														UpperCamelCase__			       =     backbone_config
														UpperCamelCase__			       =     num_channels
														UpperCamelCase__			       =     num_queries
														UpperCamelCase__			       =     d_model
														UpperCamelCase__			       =     encoder_ffn_dim
														UpperCamelCase__			       =     encoder_layers
														UpperCamelCase__			       =     encoder_attention_heads
														UpperCamelCase__			       =     decoder_ffn_dim
														UpperCamelCase__			       =     decoder_layers
														UpperCamelCase__			       =     decoder_attention_heads
														UpperCamelCase__			       =     dropout
														UpperCamelCase__			       =     attention_dropout
														UpperCamelCase__			       =     activation_dropout
														UpperCamelCase__			       =     activation_function
														UpperCamelCase__			       =     init_std
														UpperCamelCase__			       =     init_xavier_std
														UpperCamelCase__			       =     encoder_layerdrop
														UpperCamelCase__			       =     decoder_layerdrop
														UpperCamelCase__			       =     encoder_layers
														UpperCamelCase__			       =     auxiliary_loss
														UpperCamelCase__			       =     position_embedding_type
														UpperCamelCase__			       =     backbone
														UpperCamelCase__			       =     use_pretrained_backbone
														UpperCamelCase__			       =     dilation
														# Hungarian matcher
														UpperCamelCase__			       =     class_cost
														UpperCamelCase__			       =     bbox_cost
														UpperCamelCase__			       =     giou_cost
														# Loss coefficients
														UpperCamelCase__			       =     mask_loss_coefficient
														UpperCamelCase__			       =     dice_loss_coefficient
														UpperCamelCase__			       =     bbox_loss_coefficient
														UpperCamelCase__			       =     giou_loss_coefficient
														UpperCamelCase__			       =     eos_coefficient
														super().__init__(is_encoder_decoder=__lowerCAmelCase					,	**__lowerCAmelCase					)






							@property
							def 			_lowerCamelCase						(  self					):
														return self.encoder_attention_heads







							@property
							def 			_lowerCamelCase						(  self					):
														return self.d_model






class     __SCREAMING_SNAKE_CASE   (       _SCREAMING_SNAKE_CASE    ):
							snake_case       : Optional[Any]         =    version.parse("""1.11"""    )






							@property
							def 			_lowerCamelCase						(  self					):
														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														        ("""pixel_mask""", {0: """batch"""}),
														    ]					)






							@property
							def 			_lowerCamelCase						(  self					):
														return 1E-5







							@property
							def 			_lowerCamelCase						(  self					):
														return 12











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






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






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






def       _UpperCamelCase       (a__				:List[str]				,     a__				:str				,     a__				:Any				,     a__				:Any				,     a__				:List[str]				,     a__				:Union[str, Any]    ):






							"""simple docstring"""





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

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

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

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

							UpperCamelCase__			       =     os.path.join(a__				,     f"""train_pseudo.{args.data_file_extension}"""    )
							if args.data_file_extension == "csv":
														dataset.to_csv(a__				,     index=a__    )
							else:
														dataset.to_json(a__    )





def       _UpperCamelCase       (a__				:Union[str, Any]				,     a__				:Any				,     a__				:Optional[int]				,     a__				:Union[str, Any]				,     **a__				:Union[str, Any]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     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()

							UpperCamelCase__			       =     STModelArguments(model_name_or_path=a__    )
							UpperCamelCase__			       =     STDataArguments(train_file=a__				,     infer_file=a__    )
							UpperCamelCase__			       =     STTrainingArguments(output_dir=a__    )
							UpperCamelCase__			       =     argparse.Namespace()

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

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

    # Sanity checks
							UpperCamelCase__			       =     {}
							UpperCamelCase__			       =     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
							UpperCamelCase__			       =     args.train_file
							UpperCamelCase__			       =     args.infer_file

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

							for key in data_files:
														UpperCamelCase__			       =     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:
																					UpperCamelCase__			       =     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..."""    )
							UpperCamelCase__			       =     f"""{args.output_dir}/self-train_iter-{{}}""".format
							UpperCamelCase__			       =     data_dir_format(0    )

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

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

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

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

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

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

														UpperCamelCase__			       =     iteration
														UpperCamelCase__			       =     data_dir_format(iteration + 1    )

														UpperCamelCase__			       =     AutoConfig.from_pretrained(os.path.join(a__				,     """best-checkpoint"""    )    )
														UpperCamelCase__			       =     config.idalabel
														UpperCamelCase__			       =     os.path.join(a__				,     """eval_results_best-checkpoint.json"""    )
														UpperCamelCase__			       =     os.path.join(a__				,     """test_results_best-checkpoint.json"""    )
														assert os.path.exists(a__    )

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

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

														UpperCamelCase__			       =     os.path.join(a__				,     f"""train_pseudo.{args.data_file_extension}"""    )

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

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

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			UpperCamelCase__			       =     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"""				,     a__    )
														logger.info("""Best evaluation result: %s = %f"""				,     args.eval_metric				,     a__    )
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(a__				,     f"""eval_results_iter-{iteration}.json"""    )				,     os.path.join(a__				,     """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				,     a__    )
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(a__				,     f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""    )				,     os.path.join(a__				,     """eval_results_best-iteration.json"""    )				,     )




