



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 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     XLMTokenizer
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self			):
         super().setUp()

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

         _lowerCAmelCase 			=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''vocab_file''']			)
         _lowerCAmelCase 			=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''merges_file''']			)
         with open(self.vocab_file       ,     '''w'''			) as fp:
               fp.write(json.dumps(_lowerCAmelCase			)			)
         with open(self.merges_file       ,     '''w'''			) as fp:
               fp.write('''\n'''.join(_lowerCAmelCase			)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  '''lower newer'''
         _lowerCAmelCase 			=  '''lower newer'''
         return input_text, output_text





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  XLMTokenizer(self.vocab_file       ,     self.merges_file			)

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

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





   @slow
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''			)

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

         _lowerCAmelCase 			=  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase			)
         _lowerCAmelCase 			=  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase       ,     _lowerCAmelCase			)

         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,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)