


import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					str					   =    XLMTokenizer
					UpperCAmelCase__      :					Optional[Any]					   =    False
					def 				lowerCamelCase__(   self     :Dict   )      ->					str:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												a__    =  [
												    '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__    =  dict(zip(__snake_case    ,range(len(__snake_case   )   )   )   )
												a__    =  ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

												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'   ) as fp:
																			fp.write(json.dumps(__snake_case   )   )
												with open(self.merges_file    ,'w'   ) as fp:
																			fp.write('\n'.join(__snake_case   )   )
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Optional[Any]   )      ->					List[str]:
												a__    =  'lower newer'
												a__    =  'lower newer'
												return input_text, output_text
					def 				lowerCamelCase__(   self     :Tuple   )      ->					int:
												a__    =  XLMTokenizer(self.vocab_file    ,self.merges_file   )

												a__    =  'lower'
												a__    =  ['low', 'er</w>']
												a__    =  tokenizer.tokenize(__snake_case   )
												self.assertListEqual(__snake_case    ,__snake_case   )

												a__    =  tokens + ['<unk>']
												a__    =  [14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case   )    ,__snake_case   )



					@slow
					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												a__    =  XLMTokenizer.from_pretrained('xlm-mlm-en-2048'   )

												a__    =  tokenizer.encode('sequence builders'    ,add_special_tokens=__snake_case   )
												a__    =  tokenizer.encode('multi-sequence build'    ,add_special_tokens=__snake_case   )

												a__    =  tokenizer.build_inputs_with_special_tokens(__snake_case   )
												a__    =  tokenizer.build_inputs_with_special_tokens(__snake_case    ,__snake_case   )

												assert encoded_sentence == [0] + text + [1]
												assert encoded_pair == [0] + text + [1] + text_a + [1]








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


snake_case							: Any      =   {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Dict      =   [
						    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileViTForImageClassification''',
						    '''MobileViTForSemanticSegmentation''',
						    '''MobileViTModel''',
						    '''MobileViTPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMobileViTForImageClassification''',
						    '''TFMobileViTForSemanticSegmentation''',
						    '''TFMobileViTModel''',
						    '''TFMobileViTPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




