






import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




@require_tokenizers
class 							__A	(     unittest.TestCase					):





				"""simple docstring"""







				UpperCamelCase__					: Optional[Any]     					=EsmTokenizer


				def  __lowercase				(					self       ):




									"""simple docstring"""

									super().setUp()
									__UpperCamelCase :   str									=tempfile.mkdtemp()
									# fmt: off
									__UpperCamelCase :   Union[str, Any]									=['<cls>', '<pad>', '<eos>', '<unk>', 'L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', 'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C', 'X', 'B', 'U', 'Z', 'O', '.', '-', '<null_1>', '<mask>']  # noqa: E501
									# fmt: on
									__UpperCamelCase :   int									=os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['vocab_file']       )
									with open(self.vocab_file				,    'w'				,    encoding='utf-8'       ) as vocab_writer:
														vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )


				def  __lowercase				(					self				,    **lowerCamelCase__       ):




									"""simple docstring"""

									return [self.get_tokenizer(**_A       )]


				def  __lowercase				(					self				,    **lowerCamelCase__       ):




									"""simple docstring"""

									return self.tokenizer_class.from_pretrained(self.tmpdirname				,    **_A       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   List[str]									=self.tokenizer_class(self.vocab_file       )

									__UpperCamelCase :   Union[str, Any]									=tokenizer.tokenize('LAGVS'       )
									self.assertListEqual(_A				,    ['L', 'A', 'G', 'V', 'S']       )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_A       )				,    [4, 5, 6, 7, 8]       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Any									=self.tokenizer_class(self.vocab_file       )

									__UpperCamelCase :   List[Any]									='LAGVS'
									self.assertListEqual(tokenizer.encode(_A       )				,    [0, 4, 5, 6, 7, 8, 2]       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=self.tokenizer_class(self.vocab_file       )

									__UpperCamelCase :   Tuple									=['LAGVS', 'WCB']
									__UpperCamelCase :   str									=tokenizer(_A				,    padding=_A       )['input_ids']

									self.assertListEqual(_A				,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=self.tokenizer_class(self.vocab_file       )

									__UpperCamelCase :   str									=['LAGVS', 'WCB']
									__UpperCamelCase :   List[Any]									=tokenizer(_A				,    padding=_A       )['input_ids']

									self.assertListEqual(_A				,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]       )







				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Any									=self.get_tokenizers(fast=_A       )
									for tokenizer in tokenizers:
														with self.subTest(f'{tokenizer.__class__.__name__}'       ):
																			__UpperCamelCase :   int									='<unk>'
																			__UpperCamelCase :   Dict									='<mask>'

																			__UpperCamelCase :   Optional[Any]									=tokenizer.tokenize(_A       )
																			__UpperCamelCase :   List[Any]									=tokenizer.tokenize(_A       )

																			self.assertEqual(len(_A       )				,    1       )
																			self.assertEqual(len(_A       )				,    1       )
																			self.assertEqual(token_a[0]				,    _A       )
																			self.assertEqual(token_a[0]				,    _A       )



'''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 			UpperCamelCase__	:






 def __init__(      self	:str						,					_A	:Any						,					_A	:Union[str, Any]=13						,					_A	:List[Any]=7						,					_A	:int=True						,					_A	:Union[str, Any]=True						,					_A	:List[Any]=True						,					_A	:Dict=True						,					_A	:int=99						,					_A	:Union[str, Any]=32						,					_A	:int=5						,					_A	:List[str]=4						,					_A	:Optional[int]=37						,					_A	:List[str]="gelu"						,					_A	:Any=0.1						,					_A	:int=0.1						,					_A	:List[str]=512						,					_A	:Optional[Any]=16						,					_A	:Optional[Any]=2						,					_A	:List[str]=0.02						,					_A	:Any=3						,					_A	:Tuple=None						,					_A	:str=20						,					_A	:List[Any]=30						,					_A	:Dict=2						,					_A	:Union[str, Any]=2						,					_A	:int=10						,					)					->      Any:







       '''simple docstring'''



       __A													=      parent
       __A													=      batch_size
       __A													=      seq_length
       __A													=      is_training
       __A													=      use_input_mask
       __A													=      use_token_type_ids
       __A													=      use_labels
       __A													=      vocab_size
       __A													=      hidden_size
       __A													=      num_hidden_layers
       __A													=      num_attention_heads
       __A													=      intermediate_size
       __A													=      hidden_act
       __A													=      hidden_dropout_prob
       __A													=      attention_probs_dropout_prob
       __A													=      max_position_embeddings
       __A													=      type_vocab_size
       __A													=      type_sequence_label_size
       __A													=      initializer_range
       __A													=      num_labels
       __A													=      scope
       __A													=      max_xpath_tag_unit_embeddings
       __A													=      max_xpath_subs_unit_embeddings
       __A													=      tag_pad_id
       __A													=      subs_pad_id
       __A													=      max_depth






 def 						lowercase_    (      self	:Tuple      )					->      Optional[int]:







       '''simple docstring'''



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

       __A													=      ids_tensor(
           [self.batch_size, self.seq_length, self.max_depth]						,					self.max_xpath_tag_unit_embeddings      )

       __A													=      ids_tensor(
           [self.batch_size, self.seq_length, self.max_depth]						,					self.max_xpath_subs_unit_embeddings      )

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

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

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

       __A													=      self.get_config()

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






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







       '''simple docstring'''



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







       '''simple docstring'''



       __A													=      MarkupLMModel(config=_A      )
       model.to(_A      )
       model.eval()
       print('Configs:'						,					model.config.tag_pad_id						,					model.config.subs_pad_id      )
       __A													=      model(_A						,					attention_mask=_A						,					token_type_ids=_A      )
       __A													=      model(_A						,					token_type_ids=_A      )
       __A													=      model(_A      )
       self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)      )
       self.parent.assertEqual(result.pooler_output.shape						,					(self.batch_size, self.hidden_size)      )






 def 						lowercase_    (      self	:Any						,					_A	:Optional[Any]						,					_A	:Any						,					_A	:Optional[int]						,					_A	:Any						,					_A	:int						,					_A	:List[Any]						,					_A	:Dict						,					_A	:List[str]						,					)					->      Any:







       '''simple docstring'''



       __A													=      self.num_labels
       __A													=      MarkupLMForSequenceClassification(_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(
           _A						,					xpath_tags_seq=_A						,					xpath_subs_seq=_A						,					attention_mask=_A						,					token_type_ids=_A						,					labels=_A						,					)
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)      )






 def 						lowercase_    (      self	:Any						,					_A	:List[str]						,					_A	:str						,					_A	:str						,					_A	:List[Any]						,					_A	:Optional[Any]						,					_A	:Union[str, Any]						,					_A	:Dict						,					_A	:Union[str, Any]						,					)					->      Dict:







       '''simple docstring'''



       __A													=      self.num_labels
       __A													=      MarkupLMForTokenClassification(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(
           _A						,					xpath_tags_seq=_A						,					xpath_subs_seq=_A						,					attention_mask=_A						,					token_type_ids=_A						,					labels=_A						,					)
       self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)      )






 def 						lowercase_    (      self	:int						,					_A	:str						,					_A	:Tuple						,					_A	:Any						,					_A	:int						,					_A	:Any						,					_A	:Any						,					_A	:Union[str, Any]						,					_A	:List[Any]						,					)					->      List[str]:







       '''simple docstring'''



       __A													=      MarkupLMForQuestionAnswering(config=_A      )
       model.to(_A      )
       model.eval()
       __A													=      model(
           _A						,					xpath_tags_seq=_A						,					xpath_subs_seq=_A						,					attention_mask=_A						,					token_type_ids=_A						,					start_positions=_A						,					end_positions=_A						,					)
       self.parent.assertEqual(result.start_logits.shape						,					(self.batch_size, self.seq_length)      )
       self.parent.assertEqual(result.end_logits.shape						,					(self.batch_size, self.seq_length)      )






 def 						lowercase_    (      self	:Dict      )					->      List[str]:







       '''simple docstring'''



       __A													=      self.prepare_config_and_inputs()
       (
           (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   (
           __A
       )						,   
       )													=      config_and_inputs
       __A													=      {
           '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 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     List[Any]    					=       (
     (
         MarkupLMModel,
         MarkupLMForSequenceClassification,
         MarkupLMForTokenClassification,
         MarkupLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 UpperCAmelCase__     :     Tuple    					=       (
     {
         'feature-extraction': MarkupLMModel,
         'question-answering': MarkupLMForQuestionAnswering,
         'text-classification': MarkupLMForSequenceClassification,
         'token-classification': MarkupLMForTokenClassification,
         'zero-shot': MarkupLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:List[str]						,					_A	:Optional[Any]						,					_A	:Dict						,					_A	:Optional[Any]						,					_A	:Optional[int]      )					->      Tuple:







       '''simple docstring'''



       return True






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







       '''simple docstring'''



       __A													=      MarkupLMModelTester(self      )
       __A													=      ConfigTester(self						,					config_class=_A						,					hidden_size=37      )






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







       '''simple docstring'''



       self.config_tester.run_common_tests()






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_A      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      Any:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_A      )






 def 						lowercase_    (      self	:Tuple      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_A      )






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







       '''simple docstring'''



       __A													=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_A      )





def 							snake_case				(		)->      str:







      """simple docstring"""
      __A													=      '\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    '

      return html_string


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @cached_property
 def 						lowercase_    (      self	:Optional[int]      )					->      Dict:







       '''simple docstring'''



       __A													=      MarkupLMFeatureExtractor()
       __A													=      MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'      )

       return MarkupLMProcessor(_A						,					_A      )






 @slow
 def 						lowercase_    (      self	:Any      )					->      str:







       '''simple docstring'''



       __A													=      MarkupLMModel.from_pretrained('microsoft/markuplm-base'      ).to(_A      )

       __A													=      self.default_processor

       __A													=      processor(prepare_html_string()						,					return_tensors='pt'      )
       __A													=      inputs.to(_A      )

       # forward pass
       with torch.no_grad():
             __A													=      model(**_A      )

       # verify the last hidden states
       __A													=      torch.Size([1, 14, 768]      )
       self.assertEqual(outputs.last_hidden_state.shape						,					_A      )

       __A													=      torch.tensor(
           [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]]      ).to(_A      )

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






