




'''simple docstring'''



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 		lowercase_   (			A					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						XLMTokenizer
   lowerCamelCase_      =						False



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      super().setUp()

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

      _SCREAMING_SNAKE_CASE	=				os.path.join(self.tmpdirname  ,				VOCAB_FILES_NAMES["vocab_file"]   )
      _SCREAMING_SNAKE_CASE	=				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 :     str  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"lower newer"
      _SCREAMING_SNAKE_CASE	=				"lower newer"
      return input_text, output_text



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				XLMTokenizer(self.vocab_file  ,				self.merges_file   )

      _SCREAMING_SNAKE_CASE	=				"lower"
      _SCREAMING_SNAKE_CASE	=				["low", "er</w>"]
      _SCREAMING_SNAKE_CASE	=				tokenizer.tokenize(__lowerCamelCase   )
      self.assertListEqual(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				tokens + ["<unk>"]
      _SCREAMING_SNAKE_CASE	=				[1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase   )  ,				__lowerCamelCase   )



   @slow
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				XLMTokenizer.from_pretrained("xlm-mlm-en-2048"   )

      _SCREAMING_SNAKE_CASE	=				tokenizer.encode("sequence builders"  ,				add_special_tokens=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer.encode("multi-sequence build"  ,				add_special_tokens=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer.build_inputs_with_special_tokens(__lowerCamelCase  ,				__lowerCamelCase   )

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







'''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


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    '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 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]=1_2_8_1_0_0  ,				__lowerCamelCase :     Optional[int]=1_5_3_6  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Tuple=6_1_4_4  ,				__lowerCamelCase :     List[str]="gelu"  ,				__lowerCamelCase :     int=0.1  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     Union[str, Any]=5_1_2  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     int=1e-7  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     Any=-1  ,				__lowerCamelCase :     Tuple=0  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Optional[int]=0  ,				__lowerCamelCase :     Any="gelu"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				relative_attention
      _SCREAMING_SNAKE_CASE	=				max_relative_positions
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				position_biased_input

      # Backwards compatibility
      if type(__lowerCamelCase   ) == str:
         _SCREAMING_SNAKE_CASE	=				[x.strip() for x in pos_att_type.lower().split("|"   )]

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{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 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return 1_2



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     Optional["TensorType"] = None  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().generate_dummy_inputs(preprocessor=__lowerCamelCase  ,				framework=__lowerCamelCase   )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs

