


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			CTRLTokenizer
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   int:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _lowercase							      =['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
      _lowercase							      =dict(zip(UpperCAmelCase		,     range(len(UpperCAmelCase       )       )       )       )
      _lowercase							      =['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
      _lowercase							      ={'''unk_token''': '''<unk>'''}

      _lowercase							      =os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']       )
      _lowercase							      =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(UpperCAmelCase       ) + '''\n'''       )
      with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''       ) as fp:
       fp.write('''\n'''.join(UpperCAmelCase       )       )





     def      __A					(self		,     **UpperCAmelCase       )     ->   str:
      kwargs.update(self.special_tokens_map       )
      return CTRLTokenizer.from_pretrained(self.tmpdirname		,     **UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase       )     ->   Union[str, Any]:
      _lowercase							      ='''adapt react readapt apt'''
      _lowercase							      ='''adapt react readapt apt'''
      return input_text, output_text





     def      __A					(self       )     ->   Dict:
      _lowercase							      =CTRLTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map       )
      _lowercase							      ='''adapt react readapt apt'''
      _lowercase							      ='''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
      _lowercase							      =tokenizer.tokenize(UpperCAmelCase       )
      self.assertListEqual(UpperCAmelCase		,     UpperCAmelCase       )

      _lowercase							      =tokens + [tokenizer.unk_token]

      _lowercase							      =[0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase       )		,     UpperCAmelCase       )








'''simple docstring'''







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_					:    int      	=			{'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[int]      	=			['GPTNeoXTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXForCausalLM',
					    'GPTNeoXForQuestionAnswering',
					    'GPTNeoXForSequenceClassification',
					    'GPTNeoXForTokenClassification',
					    'GPTNeoXLayer',
					    'GPTNeoXModel',
					    'GPTNeoXPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_					:    List[str]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
