


"""simple docstring"""
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE							:    Any		=    get_tests_dir('fixtures/test_sentencepiece_no_bos.model')





@require_sentencepiece
@require_tokenizers
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		List[str]							      =    PegasusTokenizer
							__lowercase:		List[Any]							      =    PegasusTokenizerFast
							__lowercase:		Optional[Any]							      =    True
							__lowercase:		Dict							      =    True



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



														"""simple docstring"""



														super().setUp()

														# We have a SentencePiece fixture for testing
														snake_case_           =		PegasusTokenizer(UpperCAmelCase_       )
														tokenizer.save_pretrained(self.tmpdirname       )



							@cached_property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->Dict:



														"""simple docstring"""



														return PegasusTokenizer.from_pretrained("""google/pegasus-large"""       )



							def 		lowerCAmelCase       (				self							:      int		,       **UpperCAmelCase_							:      List[str]       )					->PegasusTokenizer:



														"""simple docstring"""



														return PegasusTokenizer.from_pretrained(self.tmpdirname		,       **UpperCAmelCase_       )



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



														"""simple docstring"""



														return ("This is a test", "This is a test")



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



														"""simple docstring"""



														snake_case_           =		"""</s>"""
														snake_case_           =		1

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_       )		,       UpperCAmelCase_       )
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_       )		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Any       )					->str:



														"""simple docstring"""



														snake_case_           =		list(self.get_tokenizer().get_vocab().keys()       )

														self.assertEqual(vocab_keys[0]		,       """<pad>"""       )
														self.assertEqual(vocab_keys[1]		,       """</s>"""       )
														self.assertEqual(vocab_keys[-1]		,       """v"""       )
														self.assertEqual(len(UpperCAmelCase_       )		,       1_103       )



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



														"""simple docstring"""



														self.assertEqual(self.get_tokenizer().vocab_size		,       1_103       )



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



														"""simple docstring"""



														snake_case_           =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
														snake_case_           =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
														snake_case_           =		(
														    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
														    """ </s> <pad> <pad> <pad>"""
														)
														snake_case_           =		rust_tokenizer([raw_input_str]		,       return_tensors=UpperCAmelCase_		,       add_special_tokens=UpperCAmelCase_       ).input_ids[0]
														snake_case_           =		py_tokenizer([raw_input_str]		,       return_tensors=UpperCAmelCase_		,       add_special_tokens=UpperCAmelCase_       ).input_ids[0]
														self.assertListEqual(UpperCAmelCase_		,       UpperCAmelCase_       )



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



														"""simple docstring"""



														snake_case_           =		self._large_tokenizer
														# <mask_1> masks whole sentence while <mask_2> masks single word
														snake_case_           =		"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
														snake_case_           =		[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
														snake_case_           =		tokenizer([raw_input_str]		,       return_tensors=UpperCAmelCase_       ).input_ids[0]
														self.assertListEqual(UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



														snake_case_           =		self._large_tokenizer
														# The tracebacks for the following asserts are **better** without messages or self.assertEqual
														assert tokenizer.vocab_size == 96_103
														assert tokenizer.pad_token_id == 0
														assert tokenizer.eos_token_id == 1
														assert tokenizer.offset == 103
														assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
														assert tokenizer.unk_token == "<unk>"
														assert tokenizer.model_max_length == 1_024
														snake_case_           =		"""To ensure a smooth flow of bank resolutions."""
														snake_case_           =		[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
														snake_case_           =		tokenizer([raw_input_str]		,       return_tensors=UpperCAmelCase_       ).input_ids[0]
														self.assertListEqual(UpperCAmelCase_		,       UpperCAmelCase_       )
														assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



							@require_torch
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->str:



														"""simple docstring"""



														snake_case_           =		["""This is going to be way too long.""" * 150, """short example"""]
														snake_case_           =		["""not super long but more than 5 tokens""", """tiny"""]
														snake_case_           =		self._large_tokenizer(UpperCAmelCase_		,       padding=UpperCAmelCase_		,       truncation=UpperCAmelCase_		,       return_tensors="""pt"""       )
														snake_case_           =		self._large_tokenizer(
														    text_target=UpperCAmelCase_		,       max_length=5		,       padding=UpperCAmelCase_		,       truncation=UpperCAmelCase_		,       return_tensors="""pt"""       )

														assert batch.input_ids.shape == (2, 1_024)
														assert batch.attention_mask.shape == (2, 1_024)
														assert targets["input_ids"].shape == (2, 5)
														assert len(UpperCAmelCase_       ) == 2  # input_ids, attention_mask.



							@slow
							def 		lowerCAmelCase       (				self							:      str       )					->str:



														"""simple docstring"""



														snake_case_           =		{"""input_ids""": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
														# fmt: on

														self.tokenizer_integration_test_util(
														    expected_encoding=UpperCAmelCase_		,       model_name="""google/bigbird-pegasus-large-arxiv"""		,       revision="""ba85d0851d708441f91440d509690f1ab6353415"""		,       )





@require_sentencepiece
@require_tokenizers
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Tuple							      =    PegasusTokenizer
							__lowercase:		Tuple							      =    PegasusTokenizerFast
							__lowercase:		str							      =    True
							__lowercase:		Dict							      =    True



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



														"""simple docstring"""



														super().setUp()

														# We have a SentencePiece fixture for testing
														snake_case_           =		PegasusTokenizer(UpperCAmelCase_		,       offset=0		,       mask_token_sent=UpperCAmelCase_		,       mask_token="""[MASK]"""       )
														tokenizer.save_pretrained(self.tmpdirname       )



							@cached_property
							def 		lowerCAmelCase       (				self							:      str       )					->Dict:



														"""simple docstring"""



														return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""       )



							def 		lowerCAmelCase       (				self							:      List[str]		,       **UpperCAmelCase_							:      Any       )					->PegasusTokenizer:



														"""simple docstring"""



														return PegasusTokenizer.from_pretrained(self.tmpdirname		,       **UpperCAmelCase_       )



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



														"""simple docstring"""



														return ("This is a test", "This is a test")



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



														"""simple docstring"""



														snake_case_           =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
														snake_case_           =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
														snake_case_           =		(
														    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
														    """ <pad> <pad> <pad>"""
														)
														snake_case_           =		rust_tokenizer([raw_input_str]		,       return_tensors=UpperCAmelCase_		,       add_special_tokens=UpperCAmelCase_       ).input_ids[0]
														snake_case_           =		py_tokenizer([raw_input_str]		,       return_tensors=UpperCAmelCase_		,       add_special_tokens=UpperCAmelCase_       ).input_ids[0]
														self.assertListEqual(UpperCAmelCase_		,       UpperCAmelCase_       )



							@require_torch
							def 		lowerCAmelCase       (				self							:      Any       )					->Any:



														"""simple docstring"""



														snake_case_           =		["""This is going to be way too long.""" * 1_000, """short example"""]
														snake_case_           =		["""not super long but more than 5 tokens""", """tiny"""]
														snake_case_           =		self._large_tokenizer(UpperCAmelCase_		,       padding=UpperCAmelCase_		,       truncation=UpperCAmelCase_		,       return_tensors="""pt"""       )
														snake_case_           =		self._large_tokenizer(
														    text_target=UpperCAmelCase_		,       max_length=5		,       padding=UpperCAmelCase_		,       truncation=UpperCAmelCase_		,       return_tensors="""pt"""       )

														assert batch.input_ids.shape == (2, 4_096)
														assert batch.attention_mask.shape == (2, 4_096)
														assert targets["input_ids"].shape == (2, 5)
														assert len(UpperCAmelCase_       ) == 2  # input_ids, attention_mask.



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



														"""simple docstring"""



														snake_case_           =		(
														    """This is an example string that is used to test the original TF implementation against the HF"""
														    """ implementation"""
														)

														snake_case_           =		self._large_tokenizer(UpperCAmelCase_       ).input_ids

														self.assertListEqual(
														    UpperCAmelCase_		,       [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]		,       )







"""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       )       )



