


"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__SCREAMING_SNAKE_CASE							:    int		=    logging.getLogger(__name__)





@dataclass
class      __A     :





							'''simple docstring'''

							__lowercase:		str							      =    field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
							__lowercase:		Optional[str]							      =    field(
							    default=snake_case__						,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
							__lowercase:		Optional[str]							      =    field(
							    default=snake_case__						,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
							__lowercase:		Optional[str]							      =    field(
							    default=snake_case__						,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,		)
							__lowercase:		bool							      =    field(default=snake_case__						,		metadata={"""help""": """Whether tp freeze the encoder."""})
							__lowercase:		bool							      =    field(default=snake_case__						,		metadata={"""help""": """Whether  to freeze the embeddings."""})





@dataclass
class      __A     :





							'''simple docstring'''

							__lowercase:		str							      =    field(
							    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""})
							__lowercase:		Optional[str]							      =    field(
							    default="""summarization"""						,		metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}						,		)
							__lowercase:		Optional[int]							      =    field(
							    default=10_24						,		metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }						,		)
							__lowercase:		Optional[int]							      =    field(
							    default=1_28						,		metadata={
							        """help""": (
							            """The maximum total sequence length for target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }						,		)
							__lowercase:		Optional[int]							      =    field(
							    default=1_42						,		metadata={
							        """help""": (
							            """The maximum total sequence length for validation target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded. """
							            """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
							            """during ``evaluate`` and ``predict``."""
							        )
							    }						,		)
							__lowercase:		Optional[int]							      =    field(
							    default=1_42						,		metadata={
							        """help""": (
							            """The maximum total sequence length for test target text after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }						,		)
							__lowercase:		Optional[int]							      =    field(default=-1						,		metadata={"""help""": """# training examples. -1 means use all."""})
							__lowercase:		Optional[int]							      =    field(default=-1						,		metadata={"""help""": """# validation examples. -1 means use all."""})
							__lowercase:		Optional[int]							      =    field(default=-1						,		metadata={"""help""": """# test examples. -1 means use all."""})
							__lowercase:		Optional[str]							      =    field(default=snake_case__						,		metadata={"""help""": """Source language id for translation."""})
							__lowercase:		Optional[str]							      =    field(default=snake_case__						,		metadata={"""help""": """Target language id for translation."""})
							__lowercase:		Optional[int]							      =    field(default=snake_case__						,		metadata={"""help""": """# num_beams to use for evaluation."""})
							__lowercase:		bool							      =    field(
							    default=snake_case__						,		metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}						,		)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							logger.info(f"""***** {split} metrics *****"""				)
							for key in sorted(metrics.keys()				):
														logger.info(f"""  {key} = {metrics[key]}"""				)
							save_json(_SCREAMING_SNAKE_CASE				, os.path.join(_SCREAMING_SNAKE_CASE				, f"""{split}_results.json"""				)				)







def 						_a				(      )    ->				Union[str, Any]:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							snake_case_           =		HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)				)

							if len(sys.argv				) == 2 and sys.argv[1].endswith(""".json"""				):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														snake_case_       ,						snake_case_       ,						snake_case_           =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
							else:
														snake_case_       ,						snake_case_       ,						snake_case_           =		parser.parse_args_into_dataclasses()

							check_output_dir(_SCREAMING_SNAKE_CASE				)

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				, datefmt="""%m/%d/%Y %H:%M:%S"""				, level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				, )
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""				, training_args.local_rank				, training_args.device				, training_args.n_gpu				, bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED				)				, training_args.fpaa				, )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank				):
														transformers.utils.logging.set_verbosity_info()
							logger.info("""Training/evaluation parameters %s"""				, _SCREAMING_SNAKE_CASE				)

							# Set seed
							set_seed(training_args.seed				)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							snake_case_           =		AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path				, cache_dir=model_args.cache_dir				, )

							snake_case_           =		("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
							for p in extra_model_params:
														if getattr(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
																					assert hasattr(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
																					setattr(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, getattr(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				)

							snake_case_           =		AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				, cache_dir=model_args.cache_dir				, )
							snake_case_           =		AutoModelForSeqaSeqLM.from_pretrained(
							    model_args.model_name_or_path				, from_tf=""".ckpt""" in model_args.model_name_or_path				, config=_SCREAMING_SNAKE_CASE				, cache_dir=model_args.cache_dir				, )

							# use task specific params
							use_task_specific_params(_SCREAMING_SNAKE_CASE				, data_args.task				)

							# set num_beams for evaluation
							if data_args.eval_beams is None:
														snake_case_           =		model.config.num_beams

							# set decoder_start_token_id for MBart
							if model.config.decoder_start_token_id is None and isinstance(_SCREAMING_SNAKE_CASE				, (MBartTokenizer, MBartTokenizerFast)				):
														assert (
														    data_args.tgt_lang is not None and data_args.src_lang is not None
														), "mBart requires --tgt_lang and --src_lang"
														if isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
																					snake_case_           =		tokenizer.lang_code_to_id[data_args.tgt_lang]
														else:
																					snake_case_           =		tokenizer.convert_tokens_to_ids(data_args.tgt_lang				)

							if model_args.freeze_embeds:
														freeze_embeds(_SCREAMING_SNAKE_CASE				)
							if model_args.freeze_encoder:
														freeze_params(model.get_encoder()				)
														assert_all_frozen(model.get_encoder()				)

							snake_case_           =		SeqaSeqDataset

							# Get datasets
							snake_case_           =		(
							    dataset_class(
							        _SCREAMING_SNAKE_CASE				, type_path="""train"""				, data_dir=data_args.data_dir				, n_obs=data_args.n_train				, max_target_length=data_args.max_target_length				, max_source_length=data_args.max_source_length				, prefix=model.config.prefix or """"""				, )
							    if training_args.do_train
							    else None
							)
							snake_case_           =		(
							    dataset_class(
							        _SCREAMING_SNAKE_CASE				, type_path="""val"""				, data_dir=data_args.data_dir				, n_obs=data_args.n_val				, max_target_length=data_args.val_max_target_length				, max_source_length=data_args.max_source_length				, prefix=model.config.prefix or """"""				, )
							    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
							    else None
							)
							snake_case_           =		(
							    dataset_class(
							        _SCREAMING_SNAKE_CASE				, type_path="""test"""				, data_dir=data_args.data_dir				, n_obs=data_args.n_test				, max_target_length=data_args.test_max_target_length				, max_source_length=data_args.max_source_length				, prefix=model.config.prefix or """"""				, )
							    if training_args.do_predict
							    else None
							)

							# Initialize our Trainer
							snake_case_           =		(
							    build_compute_metrics_fn(data_args.task				, _SCREAMING_SNAKE_CASE				) if training_args.predict_with_generate else None
							)
							snake_case_           =		SeqaSeqTrainer(
							    model=_SCREAMING_SNAKE_CASE				, args=_SCREAMING_SNAKE_CASE				, data_args=_SCREAMING_SNAKE_CASE				, train_dataset=_SCREAMING_SNAKE_CASE				, eval_dataset=_SCREAMING_SNAKE_CASE				, data_collator=SeqaSeqDataCollator(
							        _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, model.config.decoder_start_token_id				, training_args.tpu_num_cores				)				, compute_metrics=_SCREAMING_SNAKE_CASE				, tokenizer=_SCREAMING_SNAKE_CASE				, )

							snake_case_           =		{}
							# Training
							if training_args.do_train:
														logger.info("""*** Train ***"""				)

														snake_case_           =		trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
														snake_case_           =		train_result.metrics
														snake_case_           =		data_args.n_train

														trainer.save_model()  # this also saves the tokenizer

														if trainer.is_world_process_zero():
																					handle_metrics("""train"""				, _SCREAMING_SNAKE_CASE				, training_args.output_dir				)
																					all_metrics.update(_SCREAMING_SNAKE_CASE				)

																					# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																					trainer.state.save_to_json(os.path.join(training_args.output_dir				, """trainer_state.json"""				)				)

																					# For convenience, we also re-save the tokenizer to the same directory,
																					# so that you can share your model easily on huggingface.co/models =)
																					tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""				)

														snake_case_           =		trainer.evaluate(metric_key_prefix="""val"""				)
														snake_case_           =		data_args.n_val
														snake_case_           =		round(metrics["""val_loss"""]				, 4				)

														if trainer.is_world_process_zero():
																					handle_metrics("""val"""				, _SCREAMING_SNAKE_CASE				, training_args.output_dir				)
																					all_metrics.update(_SCREAMING_SNAKE_CASE				)

							if training_args.do_predict:
														logger.info("""*** Predict ***"""				)

														snake_case_           =		trainer.predict(test_dataset=_SCREAMING_SNAKE_CASE				, metric_key_prefix="""test"""				)
														snake_case_           =		test_output.metrics
														snake_case_           =		data_args.n_test

														if trainer.is_world_process_zero():
																					snake_case_           =		round(metrics["""test_loss"""]				, 4				)
																					handle_metrics("""test"""				, _SCREAMING_SNAKE_CASE				, training_args.output_dir				)
																					all_metrics.update(_SCREAMING_SNAKE_CASE				)

																					if training_args.predict_with_generate:
																												snake_case_           =		tokenizer.batch_decode(
																												    test_output.predictions				, skip_special_tokens=_SCREAMING_SNAKE_CASE				, clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE				)
																												snake_case_           =		lmap(str.strip				, _SCREAMING_SNAKE_CASE				)
																												write_txt_file(_SCREAMING_SNAKE_CASE				, os.path.join(training_args.output_dir				, """test_generations.txt"""				)				)

							if trainer.is_world_process_zero():
														save_json(_SCREAMING_SNAKE_CASE				, os.path.join(training_args.output_dir				, """all_results.json"""				)				)

							return all_metrics







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				str:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
				main()







"""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      __A     :





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]=13		,       UpperCAmelCase_							:      Optional[int]=7		,       UpperCAmelCase_							:      List[str]=True		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      List[Any]=True		,       UpperCAmelCase_							:      Optional[int]=True		,       UpperCAmelCase_							:      List[str]=99		,       UpperCAmelCase_							:      Dict=24		,       UpperCAmelCase_							:      List[str]=2		,       UpperCAmelCase_							:      Optional[Any]=6		,       UpperCAmelCase_							:      int=37		,       UpperCAmelCase_							:      Optional[Any]="gelu"		,       UpperCAmelCase_							:      Optional[Any]=0.1		,       UpperCAmelCase_							:      List[Any]=0.1		,       UpperCAmelCase_							:      Any=512		,       UpperCAmelCase_							:      str=16		,       UpperCAmelCase_							:      List[str]=2		,       UpperCAmelCase_							:      Optional[int]=0.02		,       UpperCAmelCase_							:      Tuple=3		,       UpperCAmelCase_							:      Union[str, Any]=None		,       UpperCAmelCase_							:      Any=1_000		,       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		seq_length
														snake_case_           =		is_training
														snake_case_           =		use_input_mask
														snake_case_           =		use_token_type_ids
														snake_case_           =		use_labels
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		num_attention_heads
														snake_case_           =		intermediate_size
														snake_case_           =		hidden_act
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		max_position_embeddings
														snake_case_           =		type_vocab_size
														snake_case_           =		type_sequence_label_size
														snake_case_           =		initializer_range
														snake_case_           =		num_labels
														snake_case_           =		scope
														snake_case_           =		range_bbox



							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



														snake_case_           =		ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size       )

														snake_case_           =		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]:
																																			snake_case_           =		bbox[i, j, 3]
																																			snake_case_           =		bbox[i, j, 1]
																																			snake_case_           =		t
																												if bbox[i, j, 2] < bbox[i, j, 0]:
																																			snake_case_           =		bbox[i, j, 2]
																																			snake_case_           =		bbox[i, j, 0]
																																			snake_case_           =		t

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

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

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

														snake_case_           =		self.get_config()

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



							def 		lowerCAmelCase       (				self							:      int       )					->Optional[int]:



														"""simple docstring"""



														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 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[int]		,       )					->str:



														"""simple docstring"""



														snake_case_           =		LiltModel(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_       )
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_       )
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_       )

														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 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]		,       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.num_labels
														snake_case_           =		LiltForTokenClassification(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(
														    UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_		,       labels=UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.num_labels)       )



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      List[str]		,       )					->Dict:



														"""simple docstring"""



														snake_case_           =		LiltForQuestionAnswering(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(
														    UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_		,       start_positions=UpperCAmelCase_		,       end_positions=UpperCAmelCase_		,       )
														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 		lowerCAmelCase       (				self							:      int       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														(
														    (
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						
														)           =		config_and_inputs
														snake_case_           =		{
														    """input_ids""": input_ids,
														    """bbox""": bbox,
														    """token_type_ids""": token_type_ids,
														    """attention_mask""": input_mask,
														}
														return config, inputs_dict





@require_torch
class      __A     (snake_case__						,		snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

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



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														return True



							def 		lowerCAmelCase       (				self							:      Dict       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		LiltModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       hidden_size=37       )



							def 		lowerCAmelCase       (				self							:      str       )					->List[Any]:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 		lowerCAmelCase       (				self							:      List[str]       )					->int:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					snake_case_           =		type
																					self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_       )



							@slow
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					snake_case_           =		LiltModel.from_pretrained(UpperCAmelCase_       )
																					self.assertIsNotNone(UpperCAmelCase_       )





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





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""       ).to(UpperCAmelCase_       )

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

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(input_ids=UpperCAmelCase_		,       bbox=UpperCAmelCase_       )

														snake_case_           =		torch.Size([1, 2, 768]       )
														snake_case_           =		torch.tensor(
														    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]		,       device=UpperCAmelCase_		,       )

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



