


from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class       __magic_name__	(     __lowerCAmelCase):


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      int:





      '''simple docstring'''






      return [
          {"col_1": 3, "col_2": "a"},
          {"col_1": 2, "col_2": "b"},
          {"col_1": 1, "col_2": "c"},
          {"col_1": 0, "col_2": "d"},
      ]


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
      return Dataset.from_dict(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self._create_example_records()
      UpperCamelCase__					:    List[str]          =   Dataset.from_list(lowerCamelCase__			)
      self.assertListEqual(dset.column_names							,	['''col_1''', '''col_2''']			)
      for i, r in enumerate(lowerCamelCase__			):
       self.assertDictEqual(lowerCamelCase__							,	example_records[i]			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   self._create_example_records()
      UpperCamelCase__					:    Dict          =   Dataset.from_list(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[int]          =   Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}			)
      self.assertEqual(dset.info							,	dset_from_dict.info			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      int:  # checks what happens with missing columns





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   [{'''col_1''': 1}, {'''col_2''': '''x'''}]
      UpperCamelCase__					:    str          =   Dataset.from_list(lowerCamelCase__			)
      self.assertDictEqual(dset[0]							,	{'''col_1''': 1}			)
      self.assertDictEqual(dset[1]							,	{'''col_1''': None}			)  # NB: first record is used for columns


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Union[str, Any]:  # checks if the type can be inferred from the second record





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   [{'''col_1''': []}, {'''col_1''': [1, 2]}]
      UpperCamelCase__					:    str          =   Dataset.from_list(lowerCamelCase__			)
      self.assertEqual(dset.info.features['''col_1''']							,	Sequence(Value('''int64'''			)			)			)


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   Dataset.from_list([]			)
      self.assertEqual(len(lowerCamelCase__			)							,	0			)
      self.assertListEqual(dset.column_names							,	[]			)






from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






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

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



class       __magic_name__	(     __lowerCAmelCase):


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






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


