



"""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        __A       (	a_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[Any]   	=				CTRLTokenizer
		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					Dict   	=				False







		def 					UpperCAmelCase    (	self						:  str						)					->		Optional[Any]:






				"""simple docstring"""



				super().setUp()

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

				lowercase__   :    List[Any]				=			os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['''vocab_file''']						)
				lowercase__   :    Tuple				=			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 					UpperCAmelCase    (	self						:  Optional[int]			,**_snake_case						:  List[str]						)					->		Any:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Optional[Any]						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    Any				=			'''adapt react readapt apt'''
				lowercase__   :    Union[str, Any]				=			'''adapt react readapt apt'''
				return input_text, output_text







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






				"""simple docstring"""



				lowercase__   :    List[Any]				=			CTRLTokenizer(self.vocab_file			,self.merges_file			,**self.special_tokens_map						)
				lowercase__   :    Union[str, Any]				=			'''adapt react readapt apt'''
				lowercase__   :    List[str]				=			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
				lowercase__   :    int				=			tokenizer.tokenize(_A						)
				self.assertListEqual(_A			,_A						)

				lowercase__   :    Union[str, Any]				=			tokens + [tokenizer.unk_token]

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				:    Dict       =   {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Union[str, Any]       =   ["RoFormerTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    str       =   [
				    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RoFormerForCausalLM",
				    "RoFormerForMaskedLM",
				    "RoFormerForMultipleChoice",
				    "RoFormerForQuestionAnswering",
				    "RoFormerForSequenceClassification",
				    "RoFormerForTokenClassification",
				    "RoFormerLayer",
				    "RoFormerModel",
				    "RoFormerPreTrainedModel",
				    "load_tf_weights_in_roformer",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRoFormerForCausalLM",
				    "TFRoFormerForMaskedLM",
				    "TFRoFormerForMultipleChoice",
				    "TFRoFormerForQuestionAnswering",
				    "TFRoFormerForSequenceClassification",
				    "TFRoFormerForTokenClassification",
				    "TFRoFormerLayer",
				    "TFRoFormerModel",
				    "TFRoFormerPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Any       =   [
				    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FlaxRoFormerForMaskedLM",
				    "FlaxRoFormerForMultipleChoice",
				    "FlaxRoFormerForQuestionAnswering",
				    "FlaxRoFormerForSequenceClassification",
				    "FlaxRoFormerForTokenClassification",
				    "FlaxRoFormerModel",
				    "FlaxRoFormerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
				from .tokenization_roformer import RoFormerTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_roformer_fast import RoFormerTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roformer import (
								    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoFormerForCausalLM,
								    RoFormerForMaskedLM,
								    RoFormerForMultipleChoice,
								    RoFormerForQuestionAnswering,
								    RoFormerForSequenceClassification,
								    RoFormerForTokenClassification,
								    RoFormerLayer,
								    RoFormerModel,
								    RoFormerPreTrainedModel,
								    load_tf_weights_in_roformer,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roformer import (
								    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRoFormerForCausalLM,
								    TFRoFormerForMaskedLM,
								    TFRoFormerForMultipleChoice,
								    TFRoFormerForQuestionAnswering,
								    TFRoFormerForSequenceClassification,
								    TFRoFormerForTokenClassification,
								    TFRoFormerLayer,
								    TFRoFormerModel,
								    TFRoFormerPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roformer import (
								    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlaxRoFormerForMaskedLM,
								    FlaxRoFormerForMultipleChoice,
								    FlaxRoFormerForQuestionAnswering,
								    FlaxRoFormerForSequenceClassification,
								    FlaxRoFormerForTokenClassification,
								    FlaxRoFormerModel,
								    FlaxRoFormerPreTrainedModel,
								)


else:
				import sys

				__A				:    int       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



