




import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				OpenAIGPTTokenizer
    UpperCAmelCase__							  =				OpenAIGPTTokenizerFast
    UpperCAmelCase__							  =				True
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Union[str, Any]:





           '''simple docstring'''

           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           A__							=   [
               '''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>''',
               '''<unk>''',
           ]
           A__							=   dict(zip(UpperCAmelCase__ ,			range(len(UpperCAmelCase__))))
           A__							=   ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

           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(UpperCAmelCase__))
           with open(self.merges_file ,			'''w''') as fp:
                  fp.write('''\n'''.join(UpperCAmelCase__))


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





           '''simple docstring'''

           return "lower newer", "lower newer"


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->List[str]:





           '''simple docstring'''

           A__							=   OpenAIGPTTokenizer(self.vocab_file ,			self.merges_file)

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

           A__							=   tokens + ['''<unk>''']
           A__							=   [14, 15, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__) ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Union[str, Any]=15)						->str:





           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                         A__							=   self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__ ,			**UpperCAmelCase__)

                         # Simple input
                         A__							=   '''This is a simple input'''
                         A__							=   ['''This is a simple input 1''', '''This is a simple input 2''']
                         A__							=   ('''This is a simple input''', '''This is a pair''')
                         A__							=   [
                             ('''This is a simple input 1''', '''This is a simple input 2'''),
                             ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                         ]

                         # Simple input tests
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Simple input
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Simple input
                         self.assertRaises(
                             UpperCAmelCase__ ,			tokenizer_r.batch_encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''' ,			)

                         # Pair input
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Pair input
                         self.assertRaises(UpperCAmelCase__ ,			tokenizer_r.encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''')

                         # Pair input
                         self.assertRaises(
                             UpperCAmelCase__ ,			tokenizer_r.batch_encode_plus ,			UpperCAmelCase__ ,			max_length=UpperCAmelCase__ ,			padding='''max_length''' ,			)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Dict:





           '''simple docstring'''

           pass


@require_ftfy
@require_spacy
@require_tokenizers
class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    pass








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase     :		str								=       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Dict								=       [
      """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FocalNetForImageClassification""",
      """FocalNetForMaskedImageModeling""",
      """FocalNetBackbone""",
      """FocalNetModel""",
      """FocalNetPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_focalnet import (
        FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
        FocalNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Tuple								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


