






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''
     def wrapper(*__lowerCamelCase ,							**__lowerCamelCase       ):
          UpperCAmelCase__      :  str				      =			timeit.default_timer()
          UpperCAmelCase__      :  Union[str, Any]				      =			func(*__lowerCamelCase ,							**__lowerCamelCase       )
          UpperCAmelCase__      :  int				      =			timeit.default_timer() - starttime
          return delta

     UpperCAmelCase__      :  Dict				      =			func.__name__

     return wrapper





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase=100 ,							__lowerCamelCase=None       ) ->			Any:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			[]
     UpperCAmelCase__      :  Dict				      =			seq_shapes or {}
     for i in range(__lowerCamelCase       ):
          UpperCAmelCase__      :  Optional[int]				      =			{}
          for col_id, (k, v) in enumerate(features.items()       ):
               if isinstance(__lowerCamelCase ,							_ArrayXD       ):
                    UpperCAmelCase__      :  Tuple				      =			np.random.rand(*v.shape       ).astype(v.dtype       )
               elif isinstance(__lowerCamelCase ,							datasets.Value       ):
                    if v.dtype == "string":
                         UpperCAmelCase__      :  Optional[int]				      =			"""The small grey turtle was surprisingly fast when challenged."""
                    else:
                         UpperCAmelCase__      :  Union[str, Any]				      =			np.random.randint(10 ,							size=1       ).astype(v.dtype       ).item()
               elif isinstance(__lowerCamelCase ,							datasets.Sequence       ):
                    while isinstance(__lowerCamelCase ,							datasets.Sequence       ):
                         UpperCAmelCase__      :  str				      =			v.feature
                    UpperCAmelCase__      :  str				      =			seq_shapes[k]
                    UpperCAmelCase__      :  Any				      =			np.random.rand(*__lowerCamelCase       ).astype(v.dtype       )
               UpperCAmelCase__      :  Any				      =			data

          dummy_data.append((i, example)       )

     return dummy_data







def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=100 ,							__lowerCamelCase=None       ) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[Any]				      =			generate_examples(__lowerCamelCase ,							num_examples=__lowerCamelCase ,							seq_shapes=__lowerCamelCase       )

     with ArrowWriter(features=__lowerCamelCase ,							path=__lowerCamelCase       ) as writer:
          for key, record in dummy_data:
               UpperCAmelCase__      :  List[Any]				      =			features.encode_example(__lowerCamelCase       )
               writer.write(__lowerCamelCase       )

          UpperCAmelCase__      ,		UpperCAmelCase__      :  str				      =			writer.finalize()

     if not num_final_examples == num_examples:
          raise ValueError(
              F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."       )

     UpperCAmelCase__      :  Any				      =			datasets.Dataset.from_file(filename=__lowerCamelCase ,							info=datasets.DatasetInfo(features=__lowerCamelCase       )       )

     return dataset








"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Tuple				   =						"ibert"




      def __init__(				self ,							lowercase_=3_0_5_2_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=1E-12 ,							lowercase_=1 ,							lowercase_=0 ,							lowercase_=2 ,							lowercase_="absolute" ,							lowercase_=False ,							lowercase_="none" ,							**lowercase_ ,							)    ->       Optional[int]:
          super().__init__(pad_token_id=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							**lowercase_				)

          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       layer_norm_eps
          UpperCAmelCase          =       position_embedding_type
          UpperCAmelCase          =       quant_mode
          UpperCAmelCase          =       force_dequant

class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      @property
      def      a_   (				self				)    ->       Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
              UpperCAmelCase          =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
              UpperCAmelCase          =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]				)



