



'''simple docstring'''





import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __A      (  A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Dict										=	HerbertTokenizer
 __lowerCamelCase   :	Dict										=	HerbertTokenizerFast
 __lowerCamelCase   :	Optional[int]										=	True



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      super().setUp()

      # Use a simpler test file without japanese/chinese characters
      with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''			,       encoding='''utf-8'''	) as f_data:
           _a     =  f_data.read().replace('''\n\n'''			,       '''\n'''	).strip()

      _a     =  [
          '''<s>''',
          '''</s>''',
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''w</w>''',
          '''r</w>''',
          '''t</w>''',
          '''lo''',
          '''low''',
          '''er</w>''',
          '''low</w>''',
          '''lowest</w>''',
          '''newer</w>''',
          '''wider</w>''',
          ''',</w>''',
          '''<unk>''',
      ]
      _a     =  dict(zip(A			,       range(len(A	)	)	)	)
      _a     =  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

      _a     =  os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['''vocab_file''']	)
      _a     =  os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['''merges_file''']	)
      with open(self.vocab_file			,       '''w'''	) as fp:
           fp.write(json.dumps(A	)	)
      with open(self.merges_file			,       '''w'''	) as fp:
           fp.write('''\n'''.join(A	)	)



 def  a__  (self			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  '''lower newer'''
      _a     =  '''lower newer'''
      return input_text, output_text



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  self.tokenizer_class(vocab_file=self.vocab_file			,       merges_file=self.merges_file	)

      _a     =  '''lower'''
      _a     =  ['''low''', '''er</w>''']
      _a     =  tokenizer.tokenize(A	)
      self.assertListEqual(A			,       A	)

      _a     =  tokens + ['''<unk>''']
      _a     =  [16, 17, 23]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A	)			,       A	)



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


      """simple docstring"""



      if not self.test_rust_tokenizer:
           return

      _a     =  self.get_tokenizer()
      _a     =  self.get_rust_tokenizer()

      _a     =  '''lower,newer'''

      _a     =  tokenizer.tokenize(A	)
      _a     =  rust_tokenizer.tokenize(A	)
      self.assertListEqual(A			,       A	)

      _a     =  tokenizer.encode(A			,       add_special_tokens=A	)
      _a     =  rust_tokenizer.encode(A			,       add_special_tokens=A	)
      self.assertListEqual(A			,       A	)

      _a     =  self.get_rust_tokenizer()
      _a     =  tokenizer.encode(A	)
      _a     =  rust_tokenizer.encode(A	)
      self.assertListEqual(A			,       A	)



 @slow
 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''	)

      _a     =  tokenizer.encode('''konstruowanie sekwencji'''			,       add_special_tokens=A	)
      _a     =  tokenizer.encode('''konstruowanie wielu sekwencji'''			,       add_special_tokens=A	)

      _a     =  tokenizer.build_inputs_with_special_tokens(A	)
      _a     =  tokenizer.build_inputs_with_special_tokens(A			,       A	)

      assert encoded_sentence == [0] + text + [2]
      assert encoded_pair == [0] + text + [2] + text_a + [2]








'''simple docstring'''





from typing import TYPE_CHECKING

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


lowercase_			=  {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  ["SegformerFeatureExtractor"]
  lowercase_			=  ["SegformerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SegformerDecodeHead",
      "SegformerForImageClassification",
      "SegformerForSemanticSegmentation",
      "SegformerLayer",
      "SegformerModel",
      "SegformerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSegformerDecodeHead",
      "TFSegformerForImageClassification",
      "TFSegformerForSemanticSegmentation",
      "TFSegformerModel",
      "TFSegformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

  lowercase_			=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



