


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
UpperCAmelCase__            =							"\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"

# TODO: Add description of the dataset here
# You can copy an official description
UpperCAmelCase__            =							"\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n"

# TODO: Add a link to an official homepage for the dataset here
UpperCAmelCase__            =							""

# TODO: Add the licence for the dataset here if you can find it
UpperCAmelCase__            =							""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
UpperCAmelCase__            =							{
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}







class 				lowerCamelCase__       (	datasets.GeneratorBasedBuilder):
     SCREAMING_SNAKE_CASE__				      =      datasets.Version('''1.1.0''')

     # This is an example of a dataset with multiple configurations.
     # If you don't want/need to define several sub-sets in your dataset,
     # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

     # If you need to make complex sub-parts in the datasets with configurable options
     # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
     # BUILDER_CONFIG_CLASS = MyBuilderConfig

     # You will be able to load one or the other configurations in the following list with
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     SCREAMING_SNAKE_CASE__				      =      [
         datasets.BuilderConfig(name='''first_domain'''       ,							version=lowerCAmelCase_       ,							description='''This part of my dataset covers a first domain'''),
         datasets.BuilderConfig(name='''second_domain'''       ,							version=lowerCAmelCase_       ,							description='''This part of my dataset covers a second domain'''),
     ]

     SCREAMING_SNAKE_CASE__				      =      '''first_domain'''  # It's not mandatory to have a default configuration. Just use one if it make sense.





     def 					__A (self	)					->       List[Any]:
      # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
      if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
       _lowercase											=datasets.Features(
           {
               '''sentence''': datasets.Value('''string'''	),
               '''option1''': datasets.Value('''string'''	),
               '''answer''': datasets.Value('''string'''	)
               # These are the features of your dataset like images, labels ...
           }	)
      else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
       _lowercase											=datasets.Features(
           {
               '''sentence''': datasets.Value('''string'''	),
               '''option2''': datasets.Value('''string'''	),
               '''second_domain_answer''': datasets.Value('''string'''	)
               # These are the features of your dataset like images, labels ...
           }	)
      return datasets.DatasetInfo(
          # This is the description that will appear on the datasets page.
          description=_DESCRIPTION		,					features=_snake_case		,					homepage=_HOMEPAGE		,					license=_LICENSE		,					citation=_CITATION		,					)





     def 					__A (self		,					UpperCAmelCase	)					->       List[Any]:
      # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
      # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

      # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
      # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
      # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
      _lowercase											=_URLS[self.config.name]
      _lowercase											=dl_manager.download_and_extract(_snake_case	)
      return [
          datasets.SplitGenerator(
              name=datasets.Split.TRAIN		,					gen_kwargs={
                  '''filepath''': os.path.join(_snake_case		,					'''train.jsonl'''	),
                  '''split''': '''train''',
              }		,					),
          datasets.SplitGenerator(
              name=datasets.Split.VALIDATION		,					gen_kwargs={
                  '''filepath''': os.path.join(_snake_case		,					'''dev.jsonl'''	),
                  '''split''': '''dev''',
              }		,					),
          datasets.SplitGenerator(
              name=datasets.Split.TEST		,					gen_kwargs={
                  '''filepath''': os.path.join(_snake_case		,					'''test.jsonl'''	),
                  '''split''': '''test'''
              }		,					),
      ]





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase	)					->       str:
      # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
      # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
      with open(_snake_case		,					encoding='''utf-8'''	) as f:
       for key, row in enumerate(_snake_case	):
        _lowercase											=json.loads(_snake_case	)
        if self.config.name == "first_domain":
         # Yields examples as (key, example) tuples
         yield key, {
             "sentence": data["sentence"],
             "option1": data["option1"],
             "answer": "" if split == "test" else data["answer"],
         }
        else:
         yield key, {
             "sentence": data["sentence"],
             "option2": data["option2"],
             "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
         }





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


UpperCAmelCase__            =							logging.get_logger(__name__)

UpperCAmelCase__            =							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}







class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''deberta'''





     def __init__(self		,					UpperCAmelCase=5_0_2_6_5		,					UpperCAmelCase=7_6_8		,					UpperCAmelCase=1_2		,					UpperCAmelCase=1_2		,					UpperCAmelCase=3_0_7_2		,					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		,					)					->       Dict:
      super().__init__(**UpperCAmelCase	)

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

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

      _lowercase											=pos_att_type
      _lowercase											=vocab_size
      _lowercase											=layer_norm_eps

      _lowercase											=kwargs.get('''pooler_hidden_size'''		,					UpperCAmelCase	)
      _lowercase											=pooler_dropout
      _lowercase											=pooler_hidden_act







class 				lowerCamelCase__       (	lowerCAmelCase):





     @property
     def 					__A (self	)					->       Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       _lowercase											={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       _lowercase											={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 					__A (self	)					->       int:
      return 1_2





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = 3		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = None		,					)					->       Mapping[str, Any]:
      _lowercase											=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

