





import pytest

import datasets


# Import fixture modules as plugins
__lowerCamelCase       :     Any   =     ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
 for item in items:
  if any(marker in item.keywords for marker in ["integration", "unit"]     ):
   continue
  item.add_marker(pytest.mark.unit     )



def 		A_				(     _lowerCAmelCase     )						-> Optional[int]:
 config.addinivalue_line("markers"							,   "torchaudio_latest: mark test to run with torchaudio>=0.12"     )



@pytest.fixture(autouse=_lowerCAmelCase     )
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Optional[int]:
 # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
 UpperCamelCase	:					List[Any]       	=   tmp_path_factory.getbasetemp() / "cache"
 UpperCamelCase	:					Optional[int]       	=   test_hf_cache_home / "datasets"
 UpperCamelCase	:					str       	=   test_hf_cache_home / "metrics"
 UpperCamelCase	:					Union[str, Any]       	=   test_hf_cache_home / "modules"
 monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE"							,   str(_lowerCAmelCase     )     )
 monkeypatch.setattr("datasets.config.HF_METRICS_CACHE"							,   str(_lowerCAmelCase     )     )
 monkeypatch.setattr("datasets.config.HF_MODULES_CACHE"							,   str(_lowerCAmelCase     )     )
 UpperCamelCase	:					Dict       	=   test_hf_datasets_cache / "downloads"
 monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"							,   str(_lowerCAmelCase     )     )
 UpperCamelCase	:					Tuple       	=   test_hf_datasets_cache / "downloads" / "extracted"
 monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"							,   str(_lowerCAmelCase     )     )



@pytest.fixture(autouse=_lowerCAmelCase							,   scope="session"     )
def 		A_				(     )						-> Union[str, Any]:
 datasets.disable_progress_bar()



@pytest.fixture(autouse=_lowerCAmelCase     )
def 		A_				(     _lowerCAmelCase     )						-> Union[str, Any]:
 # don't take tests into account when counting downloads
 monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"							,   _lowerCAmelCase     )



@pytest.fixture
def 		A_				(     _lowerCAmelCase     )						-> Optional[int]:
 # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
 # To be removed once SQLAlchemy 2.0 supported
 monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"							,   _lowerCAmelCase     )







from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase       :     List[Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Union[str, Any]						    =   'roberta'





      def __init__( self       ,							A_=5_0265       ,							A_=768       ,							A_=12       ,							A_=12       ,							A_=3072       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=2       ,							A_=0.02       ,							A_=1e-12       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_="absolute"       ,							A_=True       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




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

       UpperCamelCase	:					Optional[int]       	=   vocab_size
       UpperCamelCase	:					Dict       	=   hidden_size
       UpperCamelCase	:					str       	=   num_hidden_layers
       UpperCamelCase	:					Any       	=   num_attention_heads
       UpperCamelCase	:					List[str]       	=   hidden_act
       UpperCamelCase	:					Optional[Any]       	=   intermediate_size
       UpperCamelCase	:					Tuple       	=   hidden_dropout_prob
       UpperCamelCase	:					Tuple       	=   attention_probs_dropout_prob
       UpperCamelCase	:					Tuple       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   type_vocab_size
       UpperCamelCase	:					int       	=   initializer_range
       UpperCamelCase	:					str       	=   layer_norm_eps
       UpperCamelCase	:					Dict       	=   position_embedding_type
       UpperCamelCase	:					Any       	=   use_cache
       UpperCamelCase	:					Union[str, Any]       	=   classifier_dropout



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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