





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 ,						__lowerCAmelCase ,						__lowerCAmelCase=1_3 ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=9_9 ,						__lowerCAmelCase=2_4 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=6 ,						__lowerCAmelCase=3_7 ,						__lowerCAmelCase="gelu" ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=5_1_2 ,						__lowerCAmelCase=1_6 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=0.02 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=None ,						__lowerCAmelCase=1_0_0_0 ,						):



				'''simple docstring'''







				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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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 	__lowerCamelCase       (					self							):



				'''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 ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	LiltModel(config=UpperCAmelCase_							)
				model.to(UpperCAmelCase_							)
				model.eval()
				lowerCamelCase__              =	model(UpperCAmelCase_ ,						bbox=UpperCAmelCase_ ,						attention_mask=UpperCAmelCase_ ,						token_type_ids=UpperCAmelCase_							)
				lowerCamelCase__              =	model(UpperCAmelCase_ ,						bbox=UpperCAmelCase_ ,						token_type_ids=UpperCAmelCase_							)
				lowerCamelCase__              =	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 ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	self.num_labels
				lowerCamelCase__              =	LiltForTokenClassification(config=UpperCAmelCase_							)
				model.to(UpperCAmelCase_							)
				model.eval()
				lowerCamelCase__              =	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 ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	LiltForQuestionAnswering(config=UpperCAmelCase_							)
				model.to(UpperCAmelCase_							)
				model.eval()
				lowerCamelCase__              =	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							):



				'''simple docstring'''







				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       __A					(					snake_case__    ,       snake_case__    ,       snake_case__    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		(
			    (
			        LiltModel,
			        LiltForSequenceClassification,
			        LiltForTokenClassification,
			        LiltForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCAmelCase_							  =		(
			    {
			        """feature-extraction""": LiltModel,
			        """question-answering""": LiltForQuestionAnswering,
			        """text-classification""": LiltForSequenceClassification,
			        """token-classification""": LiltForTokenClassification,
			        """zero-shot""": LiltForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				return True





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	LiltModelTester(self							)
				lowerCamelCase__              =	ConfigTester(self ,						config_class=UpperCAmelCase_ ,						hidden_size=3_7							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				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(*UpperCAmelCase_							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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






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

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

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

				lowerCamelCase__              =	torch.Size([1, 2, 7_6_8]							)
				lowerCamelCase__              =	torch.tensor(
				    [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] ,						device=UpperCAmelCase_ ,						)

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










import string
from math import logaa





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	document.translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							).replace('''\n''' ,''''''							)
	lowerCamelCase__              =	document_without_punctuation.split(''' '''							)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   tuple[int, int]:







	'''simple docstring'''







	lowerCamelCase__              =	corpus.lower().translate(
	    str.maketrans('''''' ,'''''' ,string.punctuation							)							)  # strip all punctuation and replace it with ''
	lowerCamelCase__              =	corpus_without_punctuation.split('''\n'''							)
	lowerCamelCase__              =	term.lower()
	return (len([doc for doc in docs if term in doc]							), len(__snake_case							))





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=False							)       ->   float:







	'''simple docstring'''







	if smoothing:
		if n == 0:
			raise ValueError('''log10(0) is undefined.'''							)
		return round(1 + logaa(n / (1 + df)							) ,3							)

	if df == 0:
		raise ZeroDivisionError('''df must be > 0'''							)
	elif n == 0:
		raise ValueError('''log10(0) is undefined.'''							)
	return round(logaa(n / df							) ,3							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   float:







	'''simple docstring'''







	return round(tf * idf ,3							)



