


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     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      FunnelTokenizer
     _lowerCamelCase   =      FunnelTokenizerFast
     _lowerCamelCase   =      True
     _lowerCamelCase   =      True


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

        __a					=  [
            '<unk>',
            '<cls>',
            '<sep>',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        __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 	snake_case_  (	self  ,       **__A    ):
        return FunnelTokenizer.from_pretrained(self.tmpdirname  ,       **__UpperCAmelCase    )


     def 	snake_case_  (	self  ,       **__A    ):
        return FunnelTokenizerFast.from_pretrained(self.tmpdirname  ,       **__UpperCAmelCase    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  'UNwant\u00E9d,running'
        __a					=  'unwanted, running'
        return input_text, output_text


     def 	snake_case_  (	self    ):
        __a					=  self.tokenizer_class(self.vocab_file    )

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


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

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










"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A											=						logging.get_logger(__name__)

__A											=						{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   """deberta-v2"""






       def __init__(       self       ,					__UpperCAmelCase=1_2_8_1_0_0       ,					__UpperCAmelCase=1_5_3_6       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=6_1_4_4       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=1E-7       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=-1       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase="gelu"       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				hidden_size
         lowerCAmelCase__     :Optional[int]											=				num_hidden_layers
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads
         lowerCAmelCase__     :int											=				intermediate_size
         lowerCAmelCase__     :Dict											=				hidden_act
         lowerCAmelCase__     :Tuple											=				hidden_dropout_prob
         lowerCAmelCase__     :Optional[int]											=				attention_probs_dropout_prob
         lowerCAmelCase__     :Optional[Any]											=				max_position_embeddings
         lowerCAmelCase__     :Optional[Any]											=				type_vocab_size
         lowerCAmelCase__     :List[Any]											=				initializer_range
         lowerCAmelCase__     :Dict											=				relative_attention
         lowerCAmelCase__     :Union[str, Any]											=				max_relative_positions
         lowerCAmelCase__     :List[str]											=				pad_token_id
         lowerCAmelCase__     :Optional[int]											=				position_biased_input

         # Backwards compatibility
         if type(__UpperCAmelCase ) == str:
           lowerCAmelCase__     :Dict											=				[x.strip() for x in pos_att_type.lower().split('|' )]

         lowerCAmelCase__     :str											=				pos_att_type
         lowerCAmelCase__     :Dict											=				vocab_size
         lowerCAmelCase__     :Tuple											=				layer_norm_eps

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('pooler_hidden_size'       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				pooler_dropout
         lowerCAmelCase__     :int											=				pooler_hidden_act






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self.task == "multiple-choice":
           lowerCAmelCase__     :str											=				{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           lowerCAmelCase__     :Tuple											=				{0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
           return OrderedDict(
               [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
         else:
           return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 1_2






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				super().generate_dummy_inputs(preprocessor=__UpperCAmelCase       ,					framework=__UpperCAmelCase )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
         return dummy_inputs
