




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			:
     """simple docstring"""

     def __init__(    self  :   List[Any]	,  A__  :   List[str]	,  A__  :   int=1_3	,  A__  :   Optional[Any]=7	,  A__  :   Optional[Any]=True	,  A__  :   Any=True	,  A__  :   List[Any]=True	,  A__  :   Union[str, Any]=True	,  A__  :   Any=9_9	,  A__  :   int=3_2	,  A__  :   Tuple=5	,  A__  :   Union[str, Any]=4	,  A__  :   Any=3_7	,  A__  :   int="gelu"	,  A__  :   Optional[Any]=0.1	,  A__  :   Dict=0.1	,  A__  :   Optional[int]=5_1_2	,  A__  :   Any=1_6	,  A__  :   List[Any]=2	,  A__  :   Any=0.02	,  A__  :   str=3	,  A__  :   int=None	,  A__  :   str=2_0	,  A__  :   List[Any]=3_0	,  A__  :   int=2	,  A__  :   int=2	,  A__  :   List[str]=1_0	,  )				->       Optional[Any]:
           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__        =				max_xpath_tag_unit_embeddings
           lowerCamelCase__        =				max_xpath_subs_unit_embeddings
           lowerCamelCase__        =				tag_pad_id
           lowerCamelCase__        =				subs_pad_id
           lowerCamelCase__        =				max_depth




     def  __snake_case   (    self  :   str			)				->       Any:
           lowerCamelCase__        =				ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size			)

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

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

           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,
               xpath_tags_seq,
               xpath_subs_seq,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
           )




     def  __snake_case   (    self  :   str			)				->       Union[str, Any]:
           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	,  A__  :   List[Any]	,  A__  :   Any	,  A__  :   List[str]	,  A__  :   Optional[Any]	,  A__  :   Any	,  A__  :   Optional[int]	,  A__  :   Optional[Any]	,  A__  :   List[str]	,  )				->       Any:
           lowerCamelCase__        =				MarkupLMModel(config=A__			)
           model.to(A__			)
           model.eval()
           print('Configs:'	,  model.config.tag_pad_id	,  model.config.subs_pad_id			)
           lowerCamelCase__        =				model(A__	,  attention_mask=A__	,  token_type_ids=A__			)
           lowerCamelCase__        =				model(A__	,  token_type_ids=A__			)
           lowerCamelCase__        =				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  __snake_case   (    self  :   List[str]	,  A__  :   List[str]	,  A__  :   str	,  A__  :   Optional[int]	,  A__  :   Optional[int]	,  A__  :   str	,  A__  :   Any	,  A__  :   Dict	,  A__  :   Optional[int]	,  )				->       Optional[Any]:
           lowerCamelCase__        =				self.num_labels
           lowerCamelCase__        =				MarkupLMForSequenceClassification(A__			)
           model.to(A__			)
           model.eval()
           lowerCamelCase__        =				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  __snake_case   (    self  :   str	,  A__  :   Optional[Any]	,  A__  :   int	,  A__  :   Optional[int]	,  A__  :   Optional[int]	,  A__  :   str	,  A__  :   int	,  A__  :   Optional[Any]	,  A__  :   str	,  )				->       Optional[int]:
           lowerCamelCase__        =				self.num_labels
           lowerCamelCase__        =				MarkupLMForTokenClassification(config=A__			)
           model.to(A__			)
           model.eval()
           lowerCamelCase__        =				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  __snake_case   (    self  :   Dict	,  A__  :   Tuple	,  A__  :   Tuple	,  A__  :   int	,  A__  :   Tuple	,  A__  :   List[str]	,  A__  :   Optional[Any]	,  A__  :   str	,  A__  :   int	,  )				->       str:
           lowerCamelCase__        =				MarkupLMForQuestionAnswering(config=A__			)
           model.to(A__			)
           model.eval()
           lowerCamelCase__        =				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  __snake_case   (    self  :   Dict			)				->       Optional[int]:
           lowerCamelCase__        =				self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase__
           )  ,		(
               lowerCamelCase__
           )  ,		(
               lowerCamelCase__
           )  ,		(
               lowerCamelCase__
           )  ,		(
               lowerCamelCase__
           )  ,		(
               lowerCamelCase__
           )  ,		(
               lowerCamelCase__
           )  ,		(
               lowerCamelCase__
           )  ,		
           )        =				config_and_inputs
           lowerCamelCase__        =				{
               '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			(		_lowerCAmelCase			,						_lowerCAmelCase			,						unittest.TestCase		):
     """simple docstring"""

     _snake_case:	List[Any]     =(
         (
             MarkupLMModel,
             MarkupLMForSequenceClassification,
             MarkupLMForTokenClassification,
             MarkupLMForQuestionAnswering,
         )
         if is_torch_available()
         else None
     )
     _snake_case:	List[str]     =(
         {
             "feature-extraction": MarkupLMModel,
             "question-answering": MarkupLMForQuestionAnswering,
             "text-classification": MarkupLMForSequenceClassification,
             "token-classification": MarkupLMForTokenClassification,
             "zero-shot": MarkupLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )




     def  __snake_case   (    self  :   int	,  A__  :   Dict	,  A__  :   Any	,  A__  :   int	,  A__  :   Optional[Any]	,  A__  :   int			)				->       List[str]:
           # 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  :   Tuple			)				->       Optional[Any]:
           lowerCamelCase__        =				MarkupLMModelTester(self			)
           lowerCamelCase__        =				ConfigTester(self	,  config_class=A__	,  hidden_size=3_7			)




     def  __snake_case   (    self  :   Dict			)				->       Optional[Any]:
           self.config_tester.run_common_tests()




     def  __snake_case   (    self  :   Union[str, Any]			)				->       Union[str, Any]:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__			)




     def  __snake_case   (    self  :   List[str]			)				->       Optional[Any]:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*A__			)




     def  __snake_case   (    self  :   int			)				->       Any:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*A__			)




     def  __snake_case   (    self  :   List[str]			)				->       Optional[Any]:
           lowerCamelCase__        =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*A__			)

def 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__        =				'\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 			A			(		unittest.TestCase		):
     """simple docstring"""

     @cached_property
     def  __snake_case   (    self  :   List[Any]			)				->       Tuple:
           # TODO use from_pretrained here
           lowerCamelCase__        =				MarkupLMFeatureExtractor()
           lowerCamelCase__        =				MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'			)

           return MarkupLMProcessor(A__	,  A__			)




     @slow
     def  __snake_case   (    self  :   List[str]			)				->       Union[str, Any]:
           lowerCamelCase__        =				MarkupLMModel.from_pretrained('microsoft/markuplm-base'			).to(A__			)

           lowerCamelCase__        =				self.default_processor

           lowerCamelCase__        =				processor(prepare_html_string()	,  return_tensors='pt'			)
           lowerCamelCase__        =				inputs.to(A__			)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__        =				model(**A__			)

           # verify the last hidden states
           lowerCamelCase__        =				torch.Size([1, 1_4, 7_6_8]			)
           self.assertEqual(outputs.last_hidden_state.shape	,  A__			)

           lowerCamelCase__        =				torch.tensor(
               [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]			).to(A__			)

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





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__SCREAMING_SNAKE_CASE    :   Dict       =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    :   str       ={"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE    :   Dict       ={
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE    :   int       ={
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =VOCAB_FILES_NAMES
     _snake_case:	List[str]     =PRETRAINED_VOCAB_FILES_MAP
     _snake_case:	str     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case:	str     =["input_ids", "attention_mask"]
     _snake_case:	Union[str, Any]     =LongformerTokenizer




     def __init__(    self  :   str	,  A__  :   Optional[Any]=None	,  A__  :   Dict=None	,  A__  :   Any=None	,  A__  :   Optional[Any]="replace"	,  A__  :   Tuple="<s>"	,  A__  :   Any="</s>"	,  A__  :   Dict="</s>"	,  A__  :   Any="<s>"	,  A__  :   Optional[Any]="<unk>"	,  A__  :   Tuple="<pad>"	,  A__  :   Union[str, Any]="<mask>"	,  A__  :   List[str]=False	,  A__  :   List[str]=True	,  **A__  :   Optional[int]	,  )				->       List[str]:
           super().__init__(
               A__	,  A__	,  tokenizer_file=A__	,  errors=A__	,  bos_token=A__	,  eos_token=A__	,  sep_token=A__	,  cls_token=A__	,  unk_token=A__	,  pad_token=A__	,  mask_token=A__	,  add_prefix_space=A__	,  trim_offsets=A__	,  **A__	,  )

           lowerCamelCase__        =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
           if pre_tok_state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                 lowerCamelCase__        =				getattr(A__	,  pre_tok_state.pop('type'			)			)
                 lowerCamelCase__        =				add_prefix_space
                 lowerCamelCase__        =				pre_tok_class(**A__			)

           lowerCamelCase__        =				add_prefix_space

           lowerCamelCase__        =				'post_processor'
           lowerCamelCase__        =				getattr(self.backend_tokenizer	,  A__	,  A__			)
           if tokenizer_component_instance:
                 lowerCamelCase__        =				json.loads(tokenizer_component_instance.__getstate__()			)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                       lowerCamelCase__        =				tuple(state['sep']			)
                 if "cls" in state:
                       lowerCamelCase__        =				tuple(state['cls']			)

                 lowerCamelCase__        =				False

                 if state.get('add_prefix_space'	,  A__			) != add_prefix_space:
                       lowerCamelCase__        =				add_prefix_space
                       lowerCamelCase__        =				True

                 if state.get('trim_offsets'	,  A__			) != trim_offsets:
                       lowerCamelCase__        =				trim_offsets
                       lowerCamelCase__        =				True

                 if changes_to_apply:
                       lowerCamelCase__        =				getattr(A__	,  state.pop('type'			)			)
                       lowerCamelCase__        =				component_class(**A__			)
                       setattr(self.backend_tokenizer	,  A__	,  A__			)




     @property
     def  __snake_case   (    self  :   Tuple			)				->       str:
           if self._mask_token is None:
                 if self.verbose:
                       logger.error('Using mask_token, but it is not set yet.'			)
                 return None
           return str(self._mask_token			)




     @mask_token.setter
     def  __snake_case   (    self  :   Dict	,  A__  :   Tuple			)				->       int:
           lowerCamelCase__        =				AddedToken(A__	,  lstrip=A__	,  rstrip=A__			) if isinstance(A__	,  A__			) else value
           lowerCamelCase__        =				value




     def  __snake_case   (    self  :   Any	,  *A__  :   Any	,  **A__  :   Any			)				->       BatchEncoding:
           lowerCamelCase__        =				kwargs.get('is_split_into_words'	,  A__			)
           assert self.add_prefix_space or not is_split_into_words, (
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A__	,  **A__			)




     def  __snake_case   (    self  :   Tuple	,  *A__  :   str	,  **A__  :   int			)				->       BatchEncoding:
           lowerCamelCase__        =				kwargs.get('is_split_into_words'	,  A__			)

           assert self.add_prefix_space or not is_split_into_words, (
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A__	,  **A__			)




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   str	,  A__  :   Optional[str] = None			)				->       Tuple[str]:
           lowerCamelCase__        =				self._tokenizer.model.save(A__	,  name=A__			)
           return tuple(A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   int	,  A__  :   List[str]=None			)				->       Dict:
           lowerCamelCase__        =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   List[int]	,  A__  :   Optional[List[int]] = None			)				->       List[int]:
           lowerCamelCase__        =				[self.sep_token_id]
           lowerCamelCase__        =				[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep			) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]