


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors

@require_tokenizers
class 						A__      (					lowerCAmelCase__ ,      unittest.TestCase			):







    """simple docstring"""

    __magic_name__    						=	BartTokenizer
    __magic_name__    						=	BartTokenizerFast
    __magic_name__    						=	True
    __magic_name__    						=	filter_roberta_detectors
    def 	a_    ( self					):
         super().setUp()
         snake_case					=       [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
         ]
         snake_case					=       dict(zip(lowerCAmelCase_    ,      range(len(lowerCAmelCase_					)					)					)					)
         snake_case					=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         snake_case					=       {'''unk_token''': '''<unk>'''}

         snake_case					=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']					)
         snake_case					=       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 	a_    ( self    ,      **__snake_case					):
         kwargs.update(self.special_tokens_map					)
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,      **lowerCAmelCase_					)
    def 	a_    ( self    ,      **__snake_case					):
         kwargs.update(self.special_tokens_map					)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,      **lowerCAmelCase_					)
    def 	a_    ( self    ,      __snake_case					):
         return "lower newer", "lower newer"
    @cached_property
    def 	a_    ( self					):
         return BartTokenizer.from_pretrained('''facebook/bart-large'''					)
    @cached_property
    def 	a_    ( self					):
         return BartTokenizerFast.from_pretrained('''facebook/bart-large'''					)
    @require_torch
    def 	a_    ( self					):
         snake_case					=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         snake_case					=       [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=       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					)
              snake_case					=       batch.input_ids.tolist()[0]
              self.assertListEqual(lowerCAmelCase_    ,      lowerCAmelCase_					)
              # Test that special tokens are reset
    @require_torch
    def 	a_    ( self					):
         snake_case					=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=       tokenizer(lowerCAmelCase_    ,      padding=lowerCAmelCase_    ,      return_tensors='''pt'''					)
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''    ,      lowerCAmelCase_					)
              self.assertIn('''attention_mask'''    ,      lowerCAmelCase_					)
              self.assertNotIn('''labels'''    ,      lowerCAmelCase_					)
              self.assertNotIn('''decoder_attention_mask'''    ,      lowerCAmelCase_					)
    @require_torch
    def 	a_    ( self					):
         snake_case					=       [
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=       tokenizer(text_target=lowerCAmelCase_    ,      max_length=3_2    ,      padding='''max_length'''    ,      return_tensors='''pt'''					)
              self.assertEqual(3_2    ,      targets['''input_ids'''].shape[1]					)
    @require_torch
    def 	a_    ( self					):






         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=       tokenizer(
                  ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']    ,      padding=lowerCAmelCase_    ,      truncation=lowerCAmelCase_    ,      return_tensors='''pt'''					)
              self.assertIsInstance(lowerCAmelCase_    ,      lowerCAmelCase_					)
              self.assertEqual(batch.input_ids.shape    ,      (2, 1_0_2_4)					)
    @require_torch
    def 	a_    ( self					):
         snake_case					=       ['''A long paragraph for summarization.''']
         snake_case					=       [
             '''Summary of the text.''',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              snake_case					=       tokenizer(lowerCAmelCase_    ,      return_tensors='''pt'''					)
              snake_case					=       tokenizer(text_target=lowerCAmelCase_    ,      return_tensors='''pt'''					)
              snake_case					=       inputs['''input_ids''']
              snake_case					=       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()					)
    def 	a_    ( self					):
         pass




    def 	a_    ( self					):






         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                   snake_case					=       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_    ,      **lowerCAmelCase_					)
                   snake_case					=       self.tokenizer_class.from_pretrained(lowerCAmelCase_    ,      **lowerCAmelCase_					)
                   snake_case					=       '''A, <mask> AllenNLP sentence.'''
                   snake_case					=       tokenizer_r.encode_plus(lowerCAmelCase_    ,      add_special_tokens=lowerCAmelCase_    ,      return_token_type_ids=lowerCAmelCase_					)
                   snake_case					=       tokenizer_p.encode_plus(lowerCAmelCase_    ,      add_special_tokens=lowerCAmelCase_    ,      return_token_type_ids=lowerCAmelCase_					)

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['''token_type_ids''']					)    ,      sum(tokens_p['''token_type_ids''']					)					)

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']					) / len(tokens_r['''attention_mask''']					)    ,      sum(tokens_p['''attention_mask''']					) / len(tokens_p['''attention_mask''']					)    ,      )

                   snake_case					=       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
                   snake_case					=       tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['''input_ids''']    ,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)
                   self.assertSequenceEqual(tokens_r['''input_ids''']    ,      [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




