


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Dict			=      LEDTokenizer
     A:		Optional[Any]			=      LEDTokenizerFast
     A:		int			=      True


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





      '''simple docstring'''






      super().setUp()
      UpperCamelCase__					:    List[Any]          =   [
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      UpperCamelCase__					:    Tuple          =   dict(zip(lowerCamelCase__							,	range(len(lowerCamelCase__			)			)			)			)
      UpperCamelCase__					:    Optional[Any]          =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      UpperCamelCase__					:    Dict          =   {'''unk_token''': '''<unk>'''}

      UpperCamelCase__					:    Any          =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES['''vocab_file''']			)
      UpperCamelCase__					:    int          =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES['''merges_file''']			)
      with open(self.vocab_file							,	'''w'''							,	encoding='''utf-8'''			) as fp:
       fp.write(json.dumps(lowerCamelCase__			) + '''\n'''			)
      with open(self.merges_file							,	'''w'''							,	encoding='''utf-8'''			) as fp:
       fp.write('''\n'''.join(lowerCamelCase__			)			)


     def      UpperCAmelCase__	(      self							:     List[Any]							,	**lowerCamelCase__							:     Any			)  ->      Tuple:





      '''simple docstring'''






      kwargs.update(self.special_tokens_map			)
      return self.tokenizer_class.from_pretrained(self.tmpdirname							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Tuple							,	**lowerCamelCase__							:     List[Any]			)  ->      List[Any]:





      '''simple docstring'''






      kwargs.update(self.special_tokens_map			)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,	**lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Tuple			)  ->      Union[str, Any]:





      '''simple docstring'''






      return "lower newer", "lower newer"


     @cached_property
     def      UpperCAmelCase__	(      self							:     str			)  ->      Union[str, Any]:





      '''simple docstring'''






      return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''			)


     @cached_property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Union[str, Any]:





      '''simple docstring'''






      return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Union[str, Any]          =   [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__					:    str          =   tokenizer(lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	return_tensors='''pt'''			)
       self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

       self.assertEqual((2, 9)							,	batch.input_ids.shape			)
       self.assertEqual((2, 9)							,	batch.attention_mask.shape			)
       UpperCamelCase__					:    Dict          =   batch.input_ids.tolist()[0]
       self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__					:    Union[str, Any]          =   tokenizer(lowerCamelCase__							,	padding=lowerCamelCase__							,	return_tensors='''pt'''			)
       self.assertIn('''input_ids'''							,	lowerCamelCase__			)
       self.assertIn('''attention_mask'''							,	lowerCamelCase__			)
       self.assertNotIn('''labels'''							,	lowerCamelCase__			)
       self.assertNotIn('''decoder_attention_mask'''							,	lowerCamelCase__			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   [
          '''Summary of the text.''',
          '''Another summary.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__					:    List[Any]          =   tokenizer(text_target=lowerCamelCase__							,	max_length=32							,	padding='''max_length'''							,	return_tensors='''pt'''			)
       self.assertEqual(32							,	targets['''input_ids'''].shape[1]			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      List[str]:





      '''simple docstring'''






      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__					:    Dict          =   tokenizer(
           ['''I am a small frog''' * 1024, '''I am a small frog''']							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
       self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)
       self.assertEqual(batch.input_ids.shape							,	(2, 5122)			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     int			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   ['''A long paragraph for summarization.''']
      UpperCamelCase__					:    str          =   [
          '''Summary of the text.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__					:    Dict          =   tokenizer(lowerCamelCase__							,	return_tensors='''pt'''			)
       UpperCamelCase__					:    int          =   tokenizer(text_target=lowerCamelCase__							,	return_tensors='''pt'''			)
       UpperCamelCase__					:    Union[str, Any]          =   inputs['''input_ids''']
       UpperCamelCase__					:    Dict          =   targets['''input_ids''']
       self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
       self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
       self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
       self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Optional[Any]:





      '''simple docstring'''






      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__					:    Union[str, Any]          =   ['''Summary of the text.''', '''Another summary.''']
       UpperCamelCase__					:    Optional[Any]          =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

       UpperCamelCase__					:    List[str]          =   tokenizer(lowerCamelCase__							,	padding=lowerCamelCase__			)
       UpperCamelCase__					:    Union[str, Any]          =   [[0] * len(lowerCamelCase__			) for x in encoded_output['''input_ids''']]
       UpperCamelCase__					:    Optional[Any]          =   tokenizer.pad(lowerCamelCase__			)
       self.assertSequenceEqual(outputs['''global_attention_mask''']							,	lowerCamelCase__			)


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





      '''simple docstring'''






      pass


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





      '''simple docstring'''






      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"			):
        UpperCamelCase__					:    Union[str, Any]          =   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__							,	**lowerCamelCase__			)
        UpperCamelCase__					:    Dict          =   self.tokenizer_class.from_pretrained(lowerCamelCase__							,	**lowerCamelCase__			)
        UpperCamelCase__					:    Optional[Any]          =   '''A, <mask> AllenNLP sentence.'''
        UpperCamelCase__					:    Optional[Any]          =   tokenizer_r.encode_plus(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__							,	return_token_type_ids=lowerCamelCase__			)
        UpperCamelCase__					:    int          =   tokenizer_p.encode_plus(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__							,	return_token_type_ids=lowerCamelCase__			)
        self.assertEqual(sum(tokens_r['''token_type_ids''']			)							,	sum(tokens_p['''token_type_ids''']			)			)
        self.assertEqual(
            sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)							,	sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)							,	)

        UpperCamelCase__					:    int          =   tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
        UpperCamelCase__					:    Optional[int]          =   tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)
        self.assertSequenceEqual(tokens_p['''input_ids''']							,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)
        self.assertSequenceEqual(tokens_r['''input_ids''']							,	[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)

        self.assertSequenceEqual(
            lowerCamelCase__							,	['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
        self.assertSequenceEqual(
            lowerCamelCase__							,	['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase       :       Optional[int]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Optional[int]						       =				{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		Optional[Any]			=      "markuplm"


     def __init__(      self							:     List[Any]							,	lowerCamelCase__							:     Optional[int]=30522							,	lowerCamelCase__							:     Dict=768							,	lowerCamelCase__							:     List[str]=12							,	lowerCamelCase__							:     List[Any]=12							,	lowerCamelCase__							:     Optional[Any]=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Union[str, Any]=0.1							,	lowerCamelCase__							:     Optional[int]=0.1							,	lowerCamelCase__							:     Tuple=512							,	lowerCamelCase__							:     List[str]=2							,	lowerCamelCase__							:     Union[str, Any]=0.02							,	lowerCamelCase__							:     Optional[Any]=1E-1_2							,	lowerCamelCase__							:     List[str]=0							,	lowerCamelCase__							:     Tuple=0							,	lowerCamelCase__							:     List[str]=2							,	lowerCamelCase__							:     List[Any]=256							,	lowerCamelCase__							:     List[str]=1024							,	lowerCamelCase__							:     Optional[int]=216							,	lowerCamelCase__							:     Any=1001							,	lowerCamelCase__							:     int=32							,	lowerCamelCase__							:     Tuple=50							,	lowerCamelCase__							:     str="absolute"							,	lowerCamelCase__							:     List[str]=True							,	lowerCamelCase__							:     List[Any]=None							,	**lowerCamelCase__							:     List[str]							,	)  ->      Union[str, Any]:





      '''simple docstring'''






      super().__init__(
          pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__							,	)
      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    List[Any]          =   hidden_size
      UpperCamelCase__					:    Any          =   num_hidden_layers
      UpperCamelCase__					:    Tuple          =   num_attention_heads
      UpperCamelCase__					:    Any          =   hidden_act
      UpperCamelCase__					:    Dict          =   intermediate_size
      UpperCamelCase__					:    Optional[Any]          =   hidden_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Tuple          =   max_position_embeddings
      UpperCamelCase__					:    List[str]          =   type_vocab_size
      UpperCamelCase__					:    Any          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Any          =   position_embedding_type
      UpperCamelCase__					:    Optional[Any]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout
      # additional properties
      UpperCamelCase__					:    int          =   max_depth
      UpperCamelCase__					:    Optional[Any]          =   max_xpath_tag_unit_embeddings
      UpperCamelCase__					:    List[Any]          =   max_xpath_subs_unit_embeddings
      UpperCamelCase__					:    List[Any]          =   tag_pad_id
      UpperCamelCase__					:    Optional[int]          =   subs_pad_id
      UpperCamelCase__					:    Optional[Any]          =   xpath_unit_hidden_size


