






"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowercase_								=	{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

lowercase_								=	{
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}



@lru_cache()
def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					(
      list(range(ord('''!'''					)						,			ord('''~'''					) + 1					)					) + list(range(ord('''¡'''					)						,			ord('''¬'''					) + 1					)					) + list(range(ord('''®'''					)						,			ord('''ÿ'''					) + 1					)					)
  )
  __A		     =					bs[:]
  __A		     =					0
  for b in range(2**8					):
    if b not in bs:
      bs.append(A_					)
      cs.append(2**8 + n					)
      n += 1
  __A		     =					[chr(A_					) for n in cs]
  return dict(zip(A_						,			A_					)					)



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					set()
  __A		     =					word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)					)
    __A		     =					char
  return pairs





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      List[Any]							=					VOCAB_FILES_NAMES
   A_		:      int							=					PRETRAINED_VOCAB_FILES_MAP
   A_		:      Any							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_		:      Optional[int]							=					["input_ids", "attention_mask"]


   def __init__(      self					:	Any,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	Tuple="replace",			_lowerCamelCase					:	Dict="<|endoftext|>",			_lowerCamelCase					:	str="<|endoftext|>",			_lowerCamelCase					:	Any="<|endoftext|>",			_lowerCamelCase					:	List[str]=None,			_lowerCamelCase					:	Union[str, Any]=False,			_lowerCamelCase					:	Tuple=False,			**_lowerCamelCase					:	str,			):

     '''simple docstring'''
     __A		     =					AddedToken(SCREAMING_SNAKE_CASE_,			lstrip=SCREAMING_SNAKE_CASE_,			rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_    ) else bos_token
     __A		     =					AddedToken(SCREAMING_SNAKE_CASE_,			lstrip=SCREAMING_SNAKE_CASE_,			rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_    ) else eos_token
     __A		     =					AddedToken(SCREAMING_SNAKE_CASE_,			lstrip=SCREAMING_SNAKE_CASE_,			rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_    ) else unk_token
     __A		     =					AddedToken(SCREAMING_SNAKE_CASE_,			lstrip=SCREAMING_SNAKE_CASE_,			rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_    ) else pad_token
     super().__init__(
         errors=SCREAMING_SNAKE_CASE_,			unk_token=SCREAMING_SNAKE_CASE_,			bos_token=SCREAMING_SNAKE_CASE_,			eos_token=SCREAMING_SNAKE_CASE_,			pad_token=SCREAMING_SNAKE_CASE_,			add_prefix_space=SCREAMING_SNAKE_CASE_,			add_bos_token=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
     __A		     =					add_bos_token

     with open(SCREAMING_SNAKE_CASE_,			encoding='''utf-8'''    ) as vocab_handle:
       __A		     =					json.load(SCREAMING_SNAKE_CASE_    )
     __A		     =					{v: k for k, v in self.encoder.items()}
     __A		     =					errors  # how to handle errors in decoding
     __A		     =					bytes_to_unicode()
     __A		     =					{v: k for k, v in self.byte_encoder.items()}
     with open(SCREAMING_SNAKE_CASE_,			encoding='''utf-8'''    ) as merges_handle:
       __A		     =					merges_handle.read().split('''\n'''    )[1:-1]
     __A		     =					[tuple(merge.split()    ) for merge in bpe_merges]
     __A		     =					dict(zip(SCREAMING_SNAKE_CASE_,			range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
     __A		     =					{}
     __A		     =					add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     __A		     =					re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''    )


   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     return len(self.encoder    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     return dict(self.encoder,			**self.added_tokens_encoder    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     if token in self.cache:
       return self.cache[token]
     __A		     =					tuple(SCREAMING_SNAKE_CASE_    )
     __A		     =					get_pairs(SCREAMING_SNAKE_CASE_    )

     if not pairs:
       return token

     while True:
       __A		     =					min(SCREAMING_SNAKE_CASE_,			key=lambda _lowerCamelCase    : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_,			float('''inf'''    )    )    )
       if bigram not in self.bpe_ranks:
         break
       __A		     =					bigram
       __A		     =					[]
       __A		     =					0
       while i < len(SCREAMING_SNAKE_CASE_    ):
         try:
           __A		     =					word.index(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_    )
         except ValueError:
           new_word.extend(word[i:]    )
           break
         else:
           new_word.extend(word[i:j]    )
           __A		     =					j

         if word[i] == first and i < len(SCREAMING_SNAKE_CASE_    ) - 1 and word[i + 1] == second:
           new_word.append(first + second    )
           i += 2
         else:
           new_word.append(word[i]    )
           i += 1
       __A		     =					tuple(SCREAMING_SNAKE_CASE_    )
       __A		     =					new_word
       if len(SCREAMING_SNAKE_CASE_    ) == 1:
         break
       else:
         __A		     =					get_pairs(SCREAMING_SNAKE_CASE_    )
     __A		     =					''' '''.join(SCREAMING_SNAKE_CASE_    )
     __A		     =					word
     return word


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	Optional[Any]=None    ):

     '''simple docstring'''
     if self.add_bos_token:
       __A		     =					[self.bos_token_id]
     else:
       __A		     =					[]

     __A		     =					bos_token_ids + token_ids_a

     if token_ids_a is None:
       return output

     return output + bos_token_ids + token_ids_a


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	List[int],			_lowerCamelCase					:	Optional[List[int]] = None,			_lowerCamelCase					:	bool = False    ):

     '''simple docstring'''
     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=SCREAMING_SNAKE_CASE_,			token_ids_a=SCREAMING_SNAKE_CASE_,			already_has_special_tokens=SCREAMING_SNAKE_CASE_    )

     if not self.add_bos_token:
       return super().get_special_tokens_mask(
           token_ids_a=SCREAMING_SNAKE_CASE_,			token_ids_a=SCREAMING_SNAKE_CASE_,			already_has_special_tokens=SCREAMING_SNAKE_CASE_    )

     if token_ids_a is None:
       return [1] + ([0] * len(SCREAMING_SNAKE_CASE_    ))
     return [1] + ([0] * len(SCREAMING_SNAKE_CASE_    )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_    ))


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					[]
     for token in re.findall(self.pat,			SCREAMING_SNAKE_CASE_    ):
       __A		     =					''''''.join(
           self.byte_encoder[b] for b in token.encode('''utf-8'''    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_    ).split(''' '''    )    )
     return bpe_tokens


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     return self.encoder.get(SCREAMING_SNAKE_CASE_,			self.encoder.get(self.unk_token    )    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     return self.decoder.get(SCREAMING_SNAKE_CASE_    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     __A		     =					''''''.join(SCREAMING_SNAKE_CASE_    )
     __A		     =					bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8''',			errors=self.errors    )
     return text


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[str] = None    ):

     '''simple docstring'''
     if not os.path.isdir(SCREAMING_SNAKE_CASE_    ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
       return
     __A		     =					os.path.join(
         SCREAMING_SNAKE_CASE_,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
     __A		     =					os.path.join(
         SCREAMING_SNAKE_CASE_,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

     with open(SCREAMING_SNAKE_CASE_,			'''w''',			encoding='''utf-8'''    ) as f:
       f.write(json.dumps(self.encoder,			indent=2,			sort_keys=SCREAMING_SNAKE_CASE_,			ensure_ascii=SCREAMING_SNAKE_CASE_    ) + '''\n'''    )

     __A		     =					0
     with open(SCREAMING_SNAKE_CASE_,			'''w''',			encoding='''utf-8'''    ) as writer:
       writer.write('''#version: 0.2\n'''    )
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),			key=lambda _lowerCamelCase    : kv[1]    ):
         if index != token_index:
           logger.warning(
               f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
               ''' Please check that the tokenizer is not corrupted!'''    )
           __A		     =					token_index
         writer.write(''' '''.join(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
         index += 1

     return vocab_file, merge_file


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Dict=False,			**_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					kwargs.pop('''add_prefix_space''',			self.add_prefix_space    )
     if is_split_into_words or add_prefix_space:
       __A		     =					''' ''' + text
     return (text, kwargs)






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	"Conversation"    ):

     '''simple docstring'''
     __A		     =					[]
     for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,			add_special_tokens=SCREAMING_SNAKE_CASE_    ) + [self.eos_token_id]    )
     if len(SCREAMING_SNAKE_CASE_    ) > self.model_max_length:
       __A		     =					input_ids[-self.model_max_length :]
     return input_ids






'''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   )   )

