'''simple docstring'''





import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 	__A				(			__UpperCamelCase		,     unittest.TestCase     ):
    a__	:						Dict	       =  FunnelTokenizer
    a__	:						List[Any]	       =  FunnelTokenizerFast
    a__	:						List[Any]	       =  True
    a__	:						Optional[int]	       =  True



    def  _lowercase						(self   :   Any			):
         super().setUp()

         UpperCAmelCase_	   =       [
             '<unk>',
             '<cls>',
             '<sep>',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         UpperCAmelCase_	   =       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  _lowercase						(self   :   List[Any]       ,      **__a   :   Any			):
         return FunnelTokenizer.from_pretrained(self.tmpdirname       ,      **__a			)



    def  _lowercase						(self   :   Tuple       ,      **__a   :   Tuple			):
         return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,      **__a			)



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   int			):
         UpperCAmelCase_	   =       'UNwant\u00E9d,running'
         UpperCAmelCase_	   =       'unwanted, running'
         return input_text, output_text



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       self.tokenizer_class(self.vocab_file			)

         UpperCAmelCase_	   =       tokenizer.tokenize("UNwant\u00E9d,running"			)
         self.assertListEqual(__a       ,      ["un", "##want", "##ed", ",", "runn", "##ing"]			)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__a			)       ,      [7, 4, 5, 10, 8, 9]			)






    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       self.get_tokenizers(do_lower_case=__a			)
         for tokenizer in tokenizers:
              UpperCAmelCase_	   =       tokenizer("UNwant\u00E9d,running"			)
              UpperCAmelCase_	   =       len(inputs["input_ids"]			) - 1
              self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len			)

              UpperCAmelCase_	   =       tokenizer("UNwant\u00E9d,running"       ,      "UNwant\u00E9d,running"			)
              self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len + [1] * sentence_len			)





'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:					int  ={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					List[str]  =['MaskFormerFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =['MaskFormerImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Dict  =[
        'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MaskFormerForInstanceSegmentation',
        'MaskFormerModel',
        'MaskFormerPreTrainedModel',
    ]
    SCREAMING_SNAKE_CASE_:					List[str]  =[
        'MaskFormerSwinBackbone',
        'MaskFormerSwinModel',
        'MaskFormerSwinPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					Dict  =_LazyModule(__name__, globals()['__file__'], _import_structure)




