



"""simple docstring"""







import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin






class      UpperCAmelCase__		(      __lowercase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     CTRLTokenizer
		lowerCAmelCase_       :	Any 							=     False
		lowerCAmelCase_       :	Dict 							=     False



		def 					A_		( self  :    str				)			->						Dict:






									'''simple docstring'''

									super().setUp()

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

									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'      ,							encoding='utf-8'				) as fp:
																fp.write(json.dumps(_A				) + '\n'				)
									with open(self.merges_file      ,							'w'      ,							encoding='utf-8'				) as fp:
																fp.write('\n'.join(_A				)				)



		def 					A_		( self  :    int      ,							**snake_case  :    List[Any]				)			->						Dict:






									'''simple docstring'''

									kwargs.update(self.special_tokens_map				)
									return CTRLTokenizer.from_pretrained(self.tmpdirname      ,							**_A				)



		def 					A_		( self  :    int      ,							snake_case  :    int				)			->						str:






									'''simple docstring'''

									A         =					'adapt react readapt apt'
									A         =					'adapt react readapt apt'
									return input_text, output_text



		def 					A_		( self  :    Tuple				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					CTRLTokenizer(self.vocab_file      ,							self.merges_file      ,							**self.special_tokens_map				)
									A         =					'adapt react readapt apt'
									A         =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
									A         =					tokenizer.tokenize(_A				)
									self.assertListEqual(_A      ,							_A				)

									A         =					tokens + [tokenizer.unk_token]

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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



