


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


SCREAMING_SNAKE_CASE :   str							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   Tuple							      =				{
    "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     ):
       lowercase_				:			Optional[int]									=    """deberta-v2"""
       def __init__(      self,   lowerCamelCase=12_81_00,   lowerCamelCase=15_36,   lowerCamelCase=24,   lowerCamelCase=24,   lowerCamelCase=61_44,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=5_12,   lowerCamelCase=0,   lowerCamelCase=0.0_2,   lowerCamelCase=1E-7,   lowerCamelCase=False,   lowerCamelCase=-1,   lowerCamelCase=0,   lowerCamelCase=True,   lowerCamelCase=None,   lowerCamelCase=0,   lowerCamelCase="gelu",   **lowerCamelCase,   ) ->     int:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

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

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

           _lowercase :							Any   	=	pos_att_type
           _lowercase :							Dict   	=	vocab_size
           _lowercase :							List[str]   	=	layer_norm_eps

           _lowercase :							Dict   	=	kwargs.get('pooler_hidden_size',   lowerCamelCase)
           _lowercase :							Dict   	=	pooler_dropout
           _lowercase :							List[str]   	=	pooler_hidden_act






class _lowerCamelCase(		_a     ):
       @property
       def     UpperCamelCase  (      self) ->     Mapping[str, Mapping[int, str]]:




           """simple docstring"""

           if self.task == "multiple-choice":
               _lowercase :							List[str]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
               _lowercase :							List[str]   	=	{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     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return 12




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = -1,   lowerCamelCase = -1,   lowerCamelCase = -1,   lowerCamelCase = False,   lowerCamelCase = None,   lowerCamelCase = 3,   lowerCamelCase = 40,   lowerCamelCase = 40,   lowerCamelCase = None,   ) ->     Mapping[str, Any]:




           """simple docstring"""

           _lowercase :							str   	=	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






import pytest


SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"__dummy_dataset1__"

SCREAMING_SNAKE_CASE :   int							      =				"\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        UpperCamelCase_(   )					->		Dict:
    return DATASET_LOADING_SCRIPT_NAME



@pytest.fixture
def        UpperCamelCase_(   )					->		List[Any]:
    return DATASET_LOADING_SCRIPT_CODE



@pytest.fixture
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							List[str]   	=	dataset_loading_script_name
    _lowercase :							Union[str, Any]   	=	tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	script_dir / F'''{script_name}.py'''
    with open(lowerCamelCase_							,				'w'				) as f:
        f.write(lowerCamelCase_				)
    return str(lowerCamelCase_				)


