


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
_SCREAMING_SNAKE_CASE :					Optional[Any]			=	"""\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

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

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

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

# 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)
_SCREAMING_SNAKE_CASE :					List[Any]			=	{
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}

class  UpperCamelCase__ ( datasets.GeneratorBasedBuilder			):
      a__   :     Tuple								=						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')
      a__   :     List[Any]								=						[
          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'			),
      ]

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






      def 	__lowercase(     self					:   Union[str, Any]   )  -> Union[str, 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
                UpperCamelCase__							:  List[str]								=     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"
                UpperCamelCase__							:  Tuple								=     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=__lowerCamelCase,		homepage=_HOMEPAGE,		license=_LICENSE,		citation=_CITATION,		)






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   int   )  -> Optional[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
           UpperCamelCase__							:  str								=     _URLS[self.config.name]
           UpperCamelCase__							:  Any								=     dl_manager.download_and_extract(__lowerCamelCase   )
           return [
               datasets.SplitGenerator(
                   name=datasets.Split.TRAIN,		gen_kwargs={
                       '''filepath''': os.path.join(__lowerCamelCase,		'''train.jsonl'''   ),
                       '''split''': '''train''',
                   },		),
               datasets.SplitGenerator(
                   name=datasets.Split.VALIDATION,		gen_kwargs={
                       '''filepath''': os.path.join(__lowerCamelCase,		'''dev.jsonl'''   ),
                       '''split''': '''dev''',
                   },		),
               datasets.SplitGenerator(
                   name=datasets.Split.TEST,		gen_kwargs={
                       '''filepath''': os.path.join(__lowerCamelCase,		'''test.jsonl'''   ),
                       '''split''': '''test'''
                   },		),
           ]






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   int   )  -> Dict:
           # 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(__lowerCamelCase,		encoding='''utf-8'''   ) as f:
                for key, row in enumerate(__lowerCamelCase   ):
                     UpperCamelCase__							:  Tuple								=     json.loads(__lowerCamelCase   )
                     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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""LayoutLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[str]			=	[
       """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LayoutLMForMaskedLM""",
       """LayoutLMForSequenceClassification""",
       """LayoutLMForTokenClassification""",
       """LayoutLMForQuestionAnswering""",
       """LayoutLMModel""",
       """LayoutLMPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFLayoutLMForMaskedLM""",
       """TFLayoutLMForSequenceClassification""",
       """TFLayoutLMForTokenClassification""",
       """TFLayoutLMForQuestionAnswering""",
       """TFLayoutLMMainLayer""",
       """TFLayoutLMModel""",
       """TFLayoutLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

   _SCREAMING_SNAKE_CASE :					Any			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




