



'''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 __snake_case:


						'''simple docstring'''



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







@dataclasses.dataclass
class __snake_case:


						'''simple docstring'''



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







@dataclasses.dataclass
class __snake_case:


						'''simple docstring'''



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







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						List[str]							)      ->						List[str]:







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

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

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

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

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						Any							,		**_SCREAMING_SNAKE_CASE					:						Tuple							)      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     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()

	lowerCAmelCase   =     STModelArguments(model_name_or_path=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     STDataArguments(train_file=_SCREAMING_SNAKE_CASE							,		infer_file=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     STTrainingArguments(output_dir=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     argparse.Namespace()

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

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

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

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

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

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

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

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

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

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

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

		lowerCAmelCase   =     iteration
		lowerCAmelCase   =     data_dir_format(iteration + 1							)

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

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

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

		lowerCAmelCase   =     os.path.join(_SCREAMING_SNAKE_CASE							,		f'train_pseudo.{args.data_file_extension}'							)

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

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

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




'''simple docstring'''

import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				)
				from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=13     ,	A_=7     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=True     ,	A_=99     ,	A_=24     ,	A_=2     ,	A_=6     ,	A_=37     ,	A_="gelu"     ,	A_=0.1     ,	A_=0.1     ,	A_=512     ,	A_=16     ,	A_=2     ,	A_=0.0_2     ,	A_=3     ,	A_=None     ,	A_=1000     ,	)     ->     Union[str, Any]:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     seq_length
							lowerCAmelCase   =     is_training
							lowerCAmelCase   =     use_input_mask
							lowerCAmelCase   =     use_token_type_ids
							lowerCAmelCase   =     use_labels
							lowerCAmelCase   =     vocab_size
							lowerCAmelCase   =     hidden_size
							lowerCAmelCase   =     num_hidden_layers
							lowerCAmelCase   =     num_attention_heads
							lowerCAmelCase   =     intermediate_size
							lowerCAmelCase   =     hidden_act
							lowerCAmelCase   =     hidden_dropout_prob
							lowerCAmelCase   =     attention_probs_dropout_prob
							lowerCAmelCase   =     max_position_embeddings
							lowerCAmelCase   =     type_vocab_size
							lowerCAmelCase   =     type_sequence_label_size
							lowerCAmelCase   =     initializer_range
							lowerCAmelCase   =     num_labels
							lowerCAmelCase   =     scope
							lowerCAmelCase   =     range_bbox


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size				)

							lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length, 4]     ,	self.range_bbox				)
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]				):
								for j in range(bbox.shape[1]				):
									if bbox[i, j, 3] < bbox[i, j, 1]:
										lowerCAmelCase   =     bbox[i, j, 3]
										lowerCAmelCase   =     bbox[i, j, 1]
										lowerCAmelCase   =     t
									if bbox[i, j, 2] < bbox[i, j, 0]:
										lowerCAmelCase   =     bbox[i, j, 2]
										lowerCAmelCase   =     bbox[i, j, 0]
										lowerCAmelCase   =     t

							lowerCAmelCase   =     None
							if self.use_input_mask:
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	vocab_size=2				)

							lowerCAmelCase   =     None
							if self.use_token_type_ids:
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.type_vocab_size				)

							lowerCAmelCase   =     None
							lowerCAmelCase   =     None
							if self.use_labels:
								lowerCAmelCase   =     ids_tensor([self.batch_size]     ,	self.type_sequence_label_size				)
								lowerCAmelCase   =     ids_tensor([self.batch_size, self.seq_length]     ,	self.num_labels				)

							lowerCAmelCase   =     self.get_config()

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels


						def     __snake_case   ( self				)     ->     int:
							return LiltConfig(
							    vocab_size=self.vocab_size     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	max_position_embeddings=self.max_position_embeddings     ,	type_vocab_size=self.type_vocab_size     ,	initializer_range=self.initializer_range     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     int:
							lowerCAmelCase   =     LiltModel(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_				)
							lowerCAmelCase   =     model(A_     ,	bbox=A_     ,	token_type_ids=A_				)
							lowerCAmelCase   =     model(A_     ,	bbox=A_				)

							self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)				)
							self.parent.assertEqual(result.pooler_output.shape     ,	(self.batch_size, self.hidden_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     LiltForTokenClassification(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(
							    A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_     ,	labels=A_				)
							self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.seq_length, self.num_labels)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_     ,	)     ->     Optional[Any]:
							lowerCAmelCase   =     LiltForQuestionAnswering(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(
							    A_     ,	bbox=A_     ,	attention_mask=A_     ,	token_type_ids=A_     ,	start_positions=A_     ,	end_positions=A_     ,	)
							self.parent.assertEqual(result.start_logits.shape     ,	(self.batch_size, self.seq_length)				)
							self.parent.assertEqual(result.end_logits.shape     ,	(self.batch_size, self.seq_length)				)




						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       (
							    lowerCAmelCase
							),       
							)   =     config_and_inputs
							lowerCAmelCase   =     {
							    """input_ids""": input_ids,
							    """bbox""": bbox,
							    """token_type_ids""": token_type_ids,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict







@require_torch
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		(
						    (
						        LiltModel,
						        LiltForSequenceClassification,
						        LiltForTokenClassification,
						        LiltForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						UpperCAmelCase							:   Union[str, Any]									=		(
						    {
						        "feature-extraction": LiltModel,
						        "question-answering": LiltForQuestionAnswering,
						        "text-classification": LiltForSequenceClassification,
						        "token-classification": LiltForTokenClassification,
						        "zero-shot": LiltForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Dict									=		False


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_     ,	A_				)     ->     Union[str, Any]:
							return True


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     LiltModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	hidden_size=37				)


						def     __snake_case   ( self				)     ->     Any:
							self.config_tester.run_common_tests()


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								lowerCAmelCase   =     type
								self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A_				)




						@slow
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								lowerCAmelCase   =     LiltModel.from_pretrained(A_				)
								self.assertIsNotNone(A_				)







@require_torch
@slow
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""				).to(A_				)

							lowerCAmelCase   =     torch.tensor([[1, 2]]     ,	device=A_				)
							lowerCAmelCase   =     torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]     ,	device=A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(input_ids=A_     ,	bbox=A_				)

							lowerCAmelCase   =     torch.Size([1, 2, 768]				)
							lowerCAmelCase   =     torch.tensor(
							    [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]     ,	device=A_     ,	)

							self.assertTrue(outputs.last_hidden_state.shape     ,	A_				)
							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]     ,	A_     ,	atol=1e-3				)				)