






from collections import OrderedDict
from typing import Mapping

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


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		{
    """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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[int]								=						'''ibert'''


     def __init__(					self							:						List[str]       ,			UpperCAmelCase_							:						List[Any]=3_0522       ,			UpperCAmelCase_							:						Tuple=768       ,			UpperCAmelCase_							:						str=12       ,			UpperCAmelCase_							:						Dict=12       ,			UpperCAmelCase_							:						int=3072       ,			UpperCAmelCase_							:						Dict="gelu"       ,			UpperCAmelCase_							:						Tuple=0.1       ,			UpperCAmelCase_							:						str=0.1       ,			UpperCAmelCase_							:						List[Any]=512       ,			UpperCAmelCase_							:						int=2       ,			UpperCAmelCase_							:						Union[str, Any]=0.02       ,			UpperCAmelCase_							:						List[Any]=1E-12       ,			UpperCAmelCase_							:						Any=1       ,			UpperCAmelCase_							:						Any=0       ,			UpperCAmelCase_							:						Union[str, Any]=2       ,			UpperCAmelCase_							:						List[str]="absolute"       ,			UpperCAmelCase_							:						Optional[int]=False       ,			UpperCAmelCase_							:						List[str]="none"       ,			**UpperCAmelCase_							:						Dict       ,			):
       super().__init__(pad_token_id=UpperCAmelCase_       ,			bos_token_id=UpperCAmelCase_       ,			eos_token_id=UpperCAmelCase_       ,			**UpperCAmelCase_   )

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





class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     @property
     def 					_A							(					self							:						List[str]   ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE							:   List[str]						    =     {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]   )











from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  int						, __UpperCamelCase      :  Callable						, __UpperCamelCase      :  Optional[Features] = None						, __UpperCamelCase      :  str = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  Optional[dict] = None						, __UpperCamelCase      :  Optional[int] = None						, **__UpperCamelCase      :  str						, )		->     List[str]:
											super().__init__(
											    features=__UpperCamelCase						, cache_dir=__UpperCamelCase						, keep_in_memory=__UpperCamelCase						, streaming=__UpperCamelCase						, num_proc=__UpperCamelCase						, **__UpperCamelCase						, )
											A      						=    Generator(
											    cache_dir=__UpperCamelCase						, features=__UpperCamelCase						, generator=__UpperCamelCase						, gen_kwargs=__UpperCamelCase						, **__UpperCamelCase						, )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											# Build iterable dataset
											if self.streaming:
																		A      						=    self.builder.as_streaming_dataset(split='train'       )
											# Build regular (map-style) dataset
											else:
																		A      						=    None
																		A      						=    None
																		A      						=    None
																		A      						=    None

																		self.builder.download_and_prepare(
																		    download_config=__UpperCamelCase						, download_mode=__UpperCamelCase						, verification_mode=__UpperCamelCase						, base_path=__UpperCamelCase						, num_proc=self.num_proc						, )
																		A      						=    self.builder.as_dataset(
																		    split='train'						, verification_mode=__UpperCamelCase						, in_memory=self.keep_in_memory       )
											return dataset