


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 				SCREAMING_SNAKE_CASE__						(       UpperCamelCase_		,					unittest.TestCase    ):
      snake_case__       :  int       =		OpenAIGPTTokenizer
      snake_case__       :  Any       =		OpenAIGPTTokenizerFast
      snake_case__       :  Any       =		True
      snake_case__       :  List[Any]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[Any]:
             super().setUp()

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

             a_      :	Dict							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['vocab_file']  )
             a_      :	Optional[int]							=						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  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    List[str]:
             return "lower newer", "lower newer"
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Any:
             a_      :	str							=						OpenAIGPTTokenizer(self.vocab_file  ,							self.merges_file  )

             a_      :	Dict							=						'lower'
             a_      :	List[Any]							=						['low', 'er</w>']
             a_      :	Dict							=						tokenizer.tokenize(__a  )
             self.assertListEqual(__a  ,							__a  )

             a_      :	int							=						tokens + ['<unk>']
             a_      :	int							=						[1_4, 1_5, 2_0]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )  ,							__a  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=1_5  )	->    Optional[Any]:






             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                           a_      :	Tuple							=						self.rust_tokenizer_class.from_pretrained(__a  ,							**__a  )

                           # Simple input
                           a_      :	Optional[Any]							=						'This is a simple input'
                           a_      :	Optional[Any]							=						['This is a simple input 1', 'This is a simple input 2']
                           a_      :	Union[str, Any]							=						('This is a simple input', 'This is a pair')
                           a_      :	Dict							=						[
                               ('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(__a  ,							tokenizer_r.encode  ,							__a  ,							max_length=__a  ,							padding='max_length'  )

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

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

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

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

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

      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[int]:
             pass
@require_ftfy
@require_spacy
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       UpperCamelCase_    ):
      pass






UpperCAmelCase_   :				Optional[int]						      = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_   :				List[str]						      = [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_   :				int						      = {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}


