


from typing import List

import numpy as np

def    UpperCamelCase__	(	_A    :					dict		):

      """simple docstring"""




      lowerCamelCase__	:       Any					=						{key: len(_A		) for key, value in gen_kwargs.items() if isinstance(_A					,      _A		)}
      if len(set(lists_lengths.values()		)		) > 1:
            raise RuntimeError(
                (
                    "Sharding is ambiguous for this dataset: "
                    + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
                    + "\n".join(F"\t- key {key} has length {length}" for key, length in lists_lengths.items()		)
                    + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
                    + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
                )		)
      lowerCamelCase__	:       Optional[Any]					=						max(lists_lengths.values()					,      default=0		)
      return max(1					,      _A		)

def    UpperCamelCase__	(	_A    :					int					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						[]
      for group_idx in range(_A		):
            lowerCamelCase__	:       str					=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            lowerCamelCase__	:       List[str]					=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            lowerCamelCase__	:       Tuple					=						range(_A					,      start + num_shards_to_add		)
            shards_indices_per_group.append(_A		)
      return shards_indices_per_group

def    UpperCamelCase__	(	_A    :					dict					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						_number_of_shards_in_gen_kwargs(_A		)
      if num_shards == 1:
            return [dict(_A		)]
      else:
            lowerCamelCase__	:       Any					=						_distribute_shards(num_shards=_A					,      max_num_jobs=_A		)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(_A					,      _A		)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(_A		)		)
            ]

def    UpperCamelCase__	(	_A    :					List[dict]		):

      """simple docstring"""




      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key]					,      _A		)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }


def    UpperCamelCase__	(	_A    :					np.random.Generator					,      _A    :					dict		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						{len(_A		) for value in gen_kwargs.values() if isinstance(_A					,      _A		)}
      lowerCamelCase__	:       Any					=						{}
      for size in list_sizes:
            lowerCamelCase__	:       str					=						list(range(_A		)		)
            rng.shuffle(indices_per_size[size]		)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      lowerCamelCase__	:       Tuple					=						dict(_A		)
      for key, value in shuffled_kwargs.items():
            if isinstance(_A					,      _A		):
                  lowerCamelCase__	:       Tuple					=						[value[i] for i in indices_per_size[len(_A		)]]
      return shuffled_kwargs






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),
                 ] )


