



from unittest import TestCase

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




class  _UpperCAmelCase (    lowerCAmelCase__):

 def 		_snake_case      (  self	: str       ):
     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 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Optional[int]       =			{'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
     return Dataset.from_dict(lowercase_       )

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  str       =			self._create_example_records()
     snake_case_				:  List[Any]       =			Dataset.from_list(lowercase_       )
     self.assertListEqual(dset.column_names						,  ['''col_1''', '''col_2''']       )
     for i, r in enumerate(lowercase_       ):
         self.assertDictEqual(lowercase_						,  example_records[i]       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Tuple       =			self._create_example_records()
     snake_case_				:  List[Any]       =			Dataset.from_list(lowercase_       )
     snake_case_				:  Union[str, Any]       =			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 		_snake_case      (  self	: Dict       ):  # checks what happens with missing columns
     snake_case_				:  List[Any]       =			[{'''col_1''': 1}, {'''col_2''': '''x'''}]
     snake_case_				:  List[Any]       =			Dataset.from_list(lowercase_       )
     self.assertDictEqual(dset[0]						,  {'''col_1''': 1}       )
     self.assertDictEqual(dset[1]						,  {'''col_1''': None}       )  # NB: first record is used for columns

 def 		_snake_case      (  self	: int       ):  # checks if the type can be inferred from the second record
     snake_case_				:  Union[str, Any]       =			[{'''col_1''': []}, {'''col_1''': [1, 2]}]
     snake_case_				:  Tuple       =			Dataset.from_list(lowercase_       )
     self.assertEqual(dset.info.features['''col_1''']						,  Sequence(Value('''int64'''       )       )       )






 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Dict       =			Dataset.from_list([]       )
     self.assertEqual(len(lowercase_       )						,  0       )
     self.assertListEqual(dset.column_names						,  []       )










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''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  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

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

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





class  _UpperCAmelCase (    lowerCAmelCase__):

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





