




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   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						OpenAIGPTTokenizer
 UpperCamelCase						       =						OpenAIGPTTokenizerFast
 UpperCamelCase						       =						True
 UpperCamelCase						       =						False

 def     a__			(    self :  str  )       ->     Any:

       """simple docstring"""






       super().setUp()

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

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

 def     a__			(    self :  Optional[int]						,							A_ :  List[Any]  )       ->     List[str]:

       """simple docstring"""






       return "lower newer", "lower newer"

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

       """simple docstring"""






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

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

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

 def     a__			(    self :  int						,							A_ :  List[Any]=15  )       ->     Dict:

       """simple docstring"""






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

                   # Simple input
                   lowerCamelCase_							=   'This is a simple input'
                   lowerCamelCase_							=   ['This is a simple input 1', 'This is a simple input 2']
                   lowerCamelCase_							=   ('This is a simple input', 'This is a pair')
                   lowerCamelCase_							=   [
                       ('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(_snake_case						,							tokenizer_r.encode						,							_snake_case						,							max_length=_snake_case						,							padding='max_length'  )

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

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

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

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

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





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

       """simple docstring"""






       pass







@require_ftfy
@require_spacy
@require_tokenizers
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 pass









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



