


import pytest


A   :					Tuple	=      "__dummy_dataset1__"

A   :					str	=      "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"

@pytest.fixture
def    lowercase_	(	):

      """simple docstring"""




      return DATASET_LOADING_SCRIPT_NAME

@pytest.fixture
def    lowercase_	(	):

      """simple docstring"""




      return DATASET_LOADING_SCRIPT_CODE


@pytest.fixture
def    lowercase_	(	_A    :					Optional[Any]					,      _A    :					List[str]					,      _A    :					Dict		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						dataset_loading_script_name
      lowerCamelCase__	:       Optional[Any]					=						tmp_path / "datasets" / script_name
      script_dir.mkdir(parents=_A		)
      lowerCamelCase__	:       int					=						script_dir / F"{script_name}.py"
      with open(_A					,      "w"		) as f:
            f.write(_A		)
      return str(_A		)






from collections import OrderedDict
from typing import Mapping

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


A   :					Union[str, Any]	=      logging.get_logger(__name__)

A   :					Dict	=      {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "ibert"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    List[str]=30522      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    List[Any]=12      ,   __lowerCamelCase							:    str=12      ,   __lowerCamelCase							:    List[str]=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    Any=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Union[str, Any]=0.0_2      ,   __lowerCamelCase							:    Any=1E-1_2      ,   __lowerCamelCase							:    int=1      ,   __lowerCamelCase							:    Optional[Any]=0      ,   __lowerCamelCase							:    int=2      ,   __lowerCamelCase							:    int="absolute"      ,   __lowerCamelCase							:    Tuple=False      ,   __lowerCamelCase							:    Dict="none"      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase )

             lowerCamelCase__	:       Any					=						vocab_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_size
             lowerCamelCase__	:       Optional[int]					=						num_hidden_layers
             lowerCamelCase__	:       int					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						hidden_act
             lowerCamelCase__	:       List[str]					=						intermediate_size
             lowerCamelCase__	:       Optional[int]					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       Any					=						type_vocab_size
             lowerCamelCase__	:       Optional[int]					=						initializer_range
             lowerCamelCase__	:       Tuple					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       List[str]					=						quant_mode
             lowerCamelCase__	:       int					=						force_dequant







class     _lowercase				(							lowercase__):


       """simple docstring"""




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


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Any					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


