
"""simple docstring"""


import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     str   =     TransfoXLTokenizer
						_lowerCamelCase:     Union[str, Any]   =     False
						_lowerCamelCase:     int   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Tuple:
							super().setUp()

							A     = [
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							with open(self.vocab_file	,'w'	,encoding='utf-8'   ) as vocab_writer:
								vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,**A_  :   int   )  -> Tuple:
							A     = True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Optional[int]   )  -> int:
							A     = '<unk> UNwanted , running'
							A     = '<unk> unwanted, running'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Dict:
							A     = TransfoXLTokenizer(vocab_file=self.vocab_file	,lower_case=A_   )

							A     = tokenizer.tokenize('<unk> UNwanted , running'   )
							self.assertListEqual(A_	,['<unk>', 'unwanted', ',', 'running']   )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,[0, 4, 8, 7]   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Union[str, Any]:
							A     = TransfoXLTokenizer(lower_case=A_   )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '   )	,['hello', '!', 'how', 'are', 'you', '?']   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> int:
							A     = TransfoXLTokenizer(lower_case=A_   )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '   )	,['HeLLo', '!', 'how', 'Are', 'yoU', '?']   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A     = TransfoXLTokenizer(lower_case=A_   )
							A     = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							A     = [
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(A_   )	,A_   )

							self.assertEqual(tokenizer.convert_tokens_to_string(A_   )	,A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Dict:
							A     = self.get_tokenizer()
							A     = len(A_   )

							tokenizer.add_tokens(['new1', 'new2']   )
							tokenizer.move_added_token('new1'	,1   )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(A_   )	,original_len + 2   )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'   )	,[1]   )
							self.assertEqual(tokenizer.decode([1]   )	,'new1'   )

"""simple docstring"""


from typing import TYPE_CHECKING

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


_lowercase				 =					{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					['''ElectraTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ElectraForCausalLM''',
		    '''ElectraForMaskedLM''',
		    '''ElectraForMultipleChoice''',
		    '''ElectraForPreTraining''',
		    '''ElectraForQuestionAnswering''',
		    '''ElectraForSequenceClassification''',
		    '''ElectraForTokenClassification''',
		    '''ElectraModel''',
		    '''ElectraPreTrainedModel''',
		    '''load_tf_weights_in_electra''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFElectraForMaskedLM''',
		    '''TFElectraForMultipleChoice''',
		    '''TFElectraForPreTraining''',
		    '''TFElectraForQuestionAnswering''',
		    '''TFElectraForSequenceClassification''',
		    '''TFElectraForTokenClassification''',
		    '''TFElectraModel''',
		    '''TFElectraPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''FlaxElectraForCausalLM''',
		    '''FlaxElectraForMaskedLM''',
		    '''FlaxElectraForMultipleChoice''',
		    '''FlaxElectraForPreTraining''',
		    '''FlaxElectraForQuestionAnswering''',
		    '''FlaxElectraForSequenceClassification''',
		    '''FlaxElectraForTokenClassification''',
		    '''FlaxElectraModel''',
		    '''FlaxElectraPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
		from .tokenization_electra import ElectraTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_electra_fast import ElectraTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_electra import (
				    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				    ElectraPreTrainedModel,
				    load_tf_weights_in_electra,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_electra import (
				    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFElectraForMaskedLM,
				    TFElectraForMultipleChoice,
				    TFElectraForPreTraining,
				    TFElectraForQuestionAnswering,
				    TFElectraForSequenceClassification,
				    TFElectraForTokenClassification,
				    TFElectraModel,
				    TFElectraPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				    FlaxElectraPreTrainedModel,
				)

else:
		import sys

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