






import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import FlavaImageProcessor, FlavaProcessor
   from transformers.models.flava.image_processing_flava import (
       FLAVA_CODEBOOK_MEAN,
       FLAVA_CODEBOOK_STD,
       FLAVA_IMAGE_MEAN,
       FLAVA_IMAGE_STD,
   )

@require_vision
class        snake_case_				(							unittest.TestCase   ):







   """simple docstring"""



   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		int:


       """simple docstring"""



       _lowercase     			=						tempfile.mkdtemp()

       # fmt: off
       _lowercase     			=						['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
       # fmt: on
       _lowercase     			=						os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES['vocab_file']						)

       with open(self.vocab_file ,							'w' ,							encoding='utf-8'						) as fp:
           fp.write(''.join([x + '\n' for x in vocab_tokens]						)						)

       _lowercase     			=						{
           '''image_mean''': FLAVA_IMAGE_MEAN,
           '''image_std''': FLAVA_IMAGE_STD,
           '''do_normalize''': True,
           '''do_resize''': True,
           '''size''': 224,
           '''do_center_crop''': True,
           '''crop_size''': 224,
           '''input_size_patches''': 14,
           '''total_mask_patches''': 75,
           '''mask_group_max_patches''': None,
           '''mask_group_min_patches''': 16,
           '''mask_group_min_aspect_ratio''': 0.3,
           '''mask_group_max_aspect_ratio''': None,
           '''codebook_do_resize''': True,
           '''codebook_size''': 112,
           '''codebook_do_center_crop''': True,
           '''codebook_crop_size''': 112,
           '''codebook_do_map_pixels''': True,
           '''codebook_do_normalize''': True,
           '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
           '''codebook_image_std''': FLAVA_CODEBOOK_STD,
       }

       _lowercase     			=						os.path.join(self.tmpdirname ,							SCREAMING_SNAKE_CASE_						)
       with open(self.image_processor_file ,							'w' ,							encoding='utf-8'						) as fp:
           json.dump(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							**snake_case__:						int						)	->		Any:


       """simple docstring"""



       return BertTokenizer.from_pretrained(self.tmpdirname ,							**SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							**snake_case__:						List[str]						)	->		Optional[Any]:


       """simple docstring"""



       return BertTokenizerFast.from_pretrained(self.tmpdirname ,							**SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							**snake_case__:						Optional[int]						)	->		List[Any]:


       """simple docstring"""



       return FlavaImageProcessor.from_pretrained(self.tmpdirname ,							**SCREAMING_SNAKE_CASE_						)

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


       """simple docstring"""



       shutil.rmtree(self.tmpdirname						)

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


       """simple docstring"""



       _lowercase     			=						[np.random.randint(255 ,							size=(3, 30, 400) ,							dtype=np.uinta						)]

       _lowercase     			=						[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_ ,							0 ,							-1						)						) for x in image_inputs]

       return image_inputs

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Dict:


       """simple docstring"""



       _lowercase     			=						self.get_tokenizer()
       _lowercase     			=						self.get_rust_tokenizer()
       _lowercase     			=						self.get_image_processor()

       _lowercase     			=						FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_						)
       processor_slow.save_pretrained(self.tmpdirname						)
       _lowercase     			=						FlavaProcessor.from_pretrained(self.tmpdirname ,							use_fast=SCREAMING_SNAKE_CASE_						)

       _lowercase     			=						FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_						)
       processor_fast.save_pretrained(self.tmpdirname						)
       _lowercase     			=						FlavaProcessor.from_pretrained(self.tmpdirname						)

       self.assertEqual(processor_slow.tokenizer.get_vocab() ,							tokenizer_slow.get_vocab()						)
       self.assertEqual(processor_fast.tokenizer.get_vocab() ,							tokenizer_fast.get_vocab()						)
       self.assertEqual(tokenizer_slow.get_vocab() ,							tokenizer_fast.get_vocab()						)
       self.assertIsInstance(processor_slow.tokenizer ,							SCREAMING_SNAKE_CASE_						)
       self.assertIsInstance(processor_fast.tokenizer ,							SCREAMING_SNAKE_CASE_						)

       self.assertEqual(processor_slow.image_processor.to_json_string() ,							image_processor.to_json_string()						)
       self.assertEqual(processor_fast.image_processor.to_json_string() ,							image_processor.to_json_string()						)
       self.assertIsInstance(processor_slow.image_processor ,							SCREAMING_SNAKE_CASE_						)
       self.assertIsInstance(processor_fast.image_processor ,							SCREAMING_SNAKE_CASE_						)

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


       """simple docstring"""



       _lowercase     			=						FlavaProcessor(tokenizer=self.get_tokenizer() ,							image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

       _lowercase     			=						self.get_tokenizer(bos_token='(BOS)' ,							eos_token='(EOS)'						)
       _lowercase     			=						self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_ ,							padding_value=1.0						)

       _lowercase     			=						FlavaProcessor.from_pretrained(
           self.tmpdirname ,							bos_token='(BOS)' ,							eos_token='(EOS)' ,							do_normalize=SCREAMING_SNAKE_CASE_ ,							padding_value=1.0						)

       self.assertEqual(processor.tokenizer.get_vocab() ,							tokenizer_add_kwargs.get_vocab()						)
       self.assertIsInstance(processor.tokenizer ,							SCREAMING_SNAKE_CASE_						)

       self.assertEqual(processor.image_processor.to_json_string() ,							image_processor_add_kwargs.to_json_string()						)
       self.assertIsInstance(processor.image_processor ,							SCREAMING_SNAKE_CASE_						)

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


       """simple docstring"""



       _lowercase     			=						self.get_image_processor()
       _lowercase     			=						self.get_tokenizer()

       _lowercase     			=						FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_						)

       _lowercase     			=						self.prepare_image_inputs()

       _lowercase     			=						image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors='np'						)
       _lowercase     			=						processor(images=SCREAMING_SNAKE_CASE_ ,							return_tensors='np'						)

       for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum() ,							input_processor[key].sum() ,							delta=1E-2						)

       # With rest of the args
       random.seed(1_234						)
       _lowercase     			=						image_processor(
           SCREAMING_SNAKE_CASE_ ,							return_image_mask=SCREAMING_SNAKE_CASE_ ,							return_codebook_pixels=SCREAMING_SNAKE_CASE_ ,							return_tensors='np'						)
       random.seed(1_234						)
       _lowercase     			=						processor(
           images=SCREAMING_SNAKE_CASE_ ,							return_image_mask=SCREAMING_SNAKE_CASE_ ,							return_codebook_pixels=SCREAMING_SNAKE_CASE_ ,							return_tensors='np'						)

       for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum() ,							input_processor[key].sum() ,							delta=1E-2						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		str:


       """simple docstring"""



       _lowercase     			=						self.get_image_processor()
       _lowercase     			=						self.get_tokenizer()

       _lowercase     			=						FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_						)

       _lowercase     			=						'''lower newer'''

       _lowercase     			=						processor(text=SCREAMING_SNAKE_CASE_						)

       _lowercase     			=						tokenizer(SCREAMING_SNAKE_CASE_						)

       for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key] ,							encoded_processor[key]						)

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


       """simple docstring"""



       _lowercase     			=						self.get_image_processor()
       _lowercase     			=						self.get_tokenizer()

       _lowercase     			=						FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_						)

       _lowercase     			=						'''lower newer'''
       _lowercase     			=						self.prepare_image_inputs()

       _lowercase     			=						processor(text=SCREAMING_SNAKE_CASE_ ,							images=SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(list(inputs.keys()						) ,							['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

       # add extra args
       _lowercase     			=						processor(text=SCREAMING_SNAKE_CASE_ ,							images=SCREAMING_SNAKE_CASE_ ,							return_codebook_pixels=SCREAMING_SNAKE_CASE_ ,							return_image_mask=SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(
           list(inputs.keys()						) ,							[
               'input_ids',
               'token_type_ids',
               'attention_mask',
               'pixel_values',
               'codebook_pixel_values',
               'bool_masked_pos',
           ] ,							)

       # test if it raises when no input is passed
       with pytest.raises(SCREAMING_SNAKE_CASE_						):
           processor()

   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						self.get_image_processor()
       _lowercase     			=						self.get_tokenizer()

       _lowercase     			=						FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_						)

       _lowercase     			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _lowercase     			=						processor.batch_decode(SCREAMING_SNAKE_CASE_						)
       _lowercase     			=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Dict:


       """simple docstring"""



       _lowercase     			=						self.get_image_processor()
       _lowercase     			=						self.get_tokenizer()

       _lowercase     			=						FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_						)

       _lowercase     			=						'''lower newer'''
       _lowercase     			=						self.prepare_image_inputs()

       _lowercase     			=						processor(text=SCREAMING_SNAKE_CASE_ ,							images=SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(list(inputs.keys()						) ,							processor.model_input_names						)


'''simple docstring'''





import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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 (
						    MarkupLMForQuestionAnswering,
						    MarkupLMForSequenceClassification,
						    MarkupLMForTokenClassification,
						    MarkupLMModel,
						)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer

class     a :




				def __init__(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Any=13,     SCREAMING_SNAKE_CASE_						:					Any=7,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=True,     SCREAMING_SNAKE_CASE_						:					List[str]=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=True,     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					str=99,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=32,     SCREAMING_SNAKE_CASE_						:					Optional[int]=5,     SCREAMING_SNAKE_CASE_						:					str=4,     SCREAMING_SNAKE_CASE_						:					Dict=37,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					List[str]=0.1,     SCREAMING_SNAKE_CASE_						:					Tuple=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					Dict=16,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Tuple=0.02,     SCREAMING_SNAKE_CASE_						:					int=3,     SCREAMING_SNAKE_CASE_						:					Dict=None,     SCREAMING_SNAKE_CASE_						:					List[str]=20,     SCREAMING_SNAKE_CASE_						:					List[Any]=30,     SCREAMING_SNAKE_CASE_						:					Optional[int]=2,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=10,     ):
										snake_case							:  Union[str, Any]			=     parent
										snake_case							:  List[str]			=     batch_size
										snake_case							:  List[str]			=     seq_length
										snake_case							:  Optional[int]			=     is_training
										snake_case							:  Optional[Any]			=     use_input_mask
										snake_case							:  List[Any]			=     use_token_type_ids
										snake_case							:  int			=     use_labels
										snake_case							:  List[Any]			=     vocab_size
										snake_case							:  Tuple			=     hidden_size
										snake_case							:  List[str]			=     num_hidden_layers
										snake_case							:  List[Any]			=     num_attention_heads
										snake_case							:  int			=     intermediate_size
										snake_case							:  List[str]			=     hidden_act
										snake_case							:  int			=     hidden_dropout_prob
										snake_case							:  Dict			=     attention_probs_dropout_prob
										snake_case							:  Tuple			=     max_position_embeddings
										snake_case							:  List[str]			=     type_vocab_size
										snake_case							:  str			=     type_sequence_label_size
										snake_case							:  Optional[Any]			=     initializer_range
										snake_case							:  List[str]			=     num_labels
										snake_case							:  Union[str, Any]			=     scope
										snake_case							:  int			=     max_xpath_tag_unit_embeddings
										snake_case							:  Optional[Any]			=     max_xpath_subs_unit_embeddings
										snake_case							:  str			=     tag_pad_id
										snake_case							:  Dict			=     subs_pad_id
										snake_case							:  List[Any]			=     max_depth




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Tuple			=     ids_tensor([self.batch_size, self.seq_length],     self.vocab_size   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_tag_unit_embeddings   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_subs_unit_embeddings   )

										snake_case							:  Dict			=     None
										if self.use_input_mask:
																snake_case							:  List[str]			=     ids_tensor([self.batch_size, self.seq_length],     vocab_size=2   )

										snake_case							:  Any			=     None
										if self.use_token_type_ids:
																snake_case							:  Optional[int]			=     ids_tensor([self.batch_size, self.seq_length],     self.type_vocab_size   )

										snake_case							:  List[Any]			=     None
										snake_case							:  str			=     None
										if self.use_labels:
																snake_case							:  List[str]			=     ids_tensor([self.batch_size],     self.type_sequence_label_size   )
																snake_case							:  Union[str, Any]			=     ids_tensor([self.batch_size, self.seq_length],     self.num_labels   )

										snake_case							:  Union[str, Any]			=     self.get_config()

										return (
										    config,
										    input_ids,
										    xpath_tags_seq,
										    xpath_subs_seq,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										)




				def 						__snake_case				(				self						:					Tuple   ):
										return MarkupLMConfig(
										    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,     max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings,     max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings,     tag_pad_id=self.tag_pad_id,     subs_pad_id=self.subs_pad_id,     max_depth=self.max_depth,     )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Optional[Any]			=     MarkupLMModel(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										print('''Configs:''',     model.config.tag_pad_id,     model.config.subs_pad_id   )
										snake_case							:  Optional[Any]			=     model(SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     model(SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     model(SCREAMING_SNAKE_CASE_   )
										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						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  int			=     self.num_labels
										snake_case							:  Union[str, Any]			=     MarkupLMForSequenceClassification(SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  str			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     labels=SCREAMING_SNAKE_CASE_,     )
										self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels)   )




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Any			=     self.num_labels
										snake_case							:  List[str]			=     MarkupLMForTokenClassification(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Dict			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     labels=SCREAMING_SNAKE_CASE_,     )
										self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.num_labels)   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  List[Any]			=     MarkupLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Union[str, Any]			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     start_positions=SCREAMING_SNAKE_CASE_,     end_positions=SCREAMING_SNAKE_CASE_,     )
										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[Any]   ):
										snake_case							:  int			=     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       
										)							:  Any			=     config_and_inputs
										snake_case							:  str			=     {
										    '''input_ids''': input_ids,
										    '''xpath_tags_seq''': xpath_tags_seq,
										    '''xpath_subs_seq''': xpath_subs_seq,
										    '''token_type_ids''': token_type_ids,
										    '''attention_mask''': input_mask,
										}
										return config, inputs_dict




@require_torch
class     a (					__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				_snake_case		     =			(
				    {
				        '''feature-extraction''': MarkupLMModel,
				        '''question-answering''': MarkupLMForQuestionAnswering,
				        '''text-classification''': MarkupLMForSequenceClassification,
				        '''token-classification''': MarkupLMForTokenClassification,
				        '''zero-shot''': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
										# (batch of pretokenized examples).
										return True




				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Tuple			=     MarkupLMModelTester(self   )
										snake_case							:  List[Any]			=     ConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     hidden_size=37   )




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




				def 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  List[Any]			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_   )




def   A			(							):
						snake_case							:  Optional[int]			=     '''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

						return html_string





@require_torch
class     a (					unittest.TestCase     ):




				@cached_property
				def 						__snake_case				(				self						:					Tuple   ):
										# TODO use from_pretrained here
										snake_case							:  Optional[int]			=     MarkupLMFeatureExtractor()
										snake_case							:  Tuple			=     MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''   )

										return MarkupLMProcessor(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				@slow
				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Dict			=     MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''   ).to(SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     self.default_processor

										snake_case							:  str			=     processor(prepare_html_string(),     return_tensors='''pt'''   )
										snake_case							:  List[Any]			=     inputs.to(SCREAMING_SNAKE_CASE_   )

										# forward pass
										with torch.no_grad():
																snake_case							:  Any			=     model(**SCREAMING_SNAKE_CASE_   )

										# verify the last hidden states
										snake_case							:  Tuple			=     torch.Size([1, 14, 7_68]   )
										self.assertEqual(outputs.last_hidden_state.shape,     SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     torch.tensor(
										    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]   ).to(SCREAMING_SNAKE_CASE_   )

										self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3],     SCREAMING_SNAKE_CASE_,     atol=1e-4   )   )

