
'''simple docstring'''

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									=    {
    """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
    """distilbert-base-uncased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
    """distilbert-base-cased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
    """distilbert-base-multilingual-cased""": (
        """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
    ),
    """distilbert-base-uncased-finetuned-sst-2-english""": (
        """https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
    ),
}




class    lowerCAmelCase       (     __A  ):
 A_				:				Union[str, Any]	=			"""distilbert"""
 A_				:				Any	=			{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }


 def __init__(    self  : Optional[int]			,							a__  : str=3_0522			,							a__  : Any=512			,							a__  : int=False			,							a__  : List[str]=6			,							a__  : Optional[int]=12			,							a__  : Dict=768			,							a__  : str=4 * 768			,							a__  : int=0.1			,							a__  : Optional[int]=0.1			,							a__  : List[Any]="gelu"			,							a__  : List[Any]=0.02			,							a__  : List[Any]=0.1			,							a__  : Optional[int]=0.2			,							a__  : List[Any]=0			,							**a__  : Dict			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	vocab_size
  lowerCAmelCase__     :   Union[str, Any]     		=	max_position_embeddings
  lowerCAmelCase__     :   Any     		=	sinusoidal_pos_embds
  lowerCAmelCase__     :   Dict     		=	n_layers
  lowerCAmelCase__     :   Optional[int]     		=	n_heads
  lowerCAmelCase__     :   Optional[Any]     		=	dim
  lowerCAmelCase__     :   Any     		=	hidden_dim
  lowerCAmelCase__     :   Optional[int]     		=	dropout
  lowerCAmelCase__     :   str     		=	attention_dropout
  lowerCAmelCase__     :   Any     		=	activation
  lowerCAmelCase__     :   Optional[Any]     		=	initializer_range
  lowerCAmelCase__     :   Optional[int]     		=	qa_dropout
  lowerCAmelCase__     :   Optional[Any]     		=	seq_classif_dropout
  super().__init__(**a__			,							pad_token_id=a__    )




class    lowerCAmelCase       (     __A  ):


 @property
 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

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













'''simple docstring'''



from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def 					A__   (			self      :		Optional[int]		)	->	List[str]:


       '''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 					A__   (			self      :		Any		)	->	List[str]:


       '''simple docstring'''

       lowercase       : List[str]						={'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
       return Dataset.from_dict(UpperCAmelCase		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Tuple						=self._create_example_records()
       lowercase       : Tuple						=Dataset.from_list(UpperCAmelCase		)
       self.assertListEqual(dset.column_names  ,   ['''col_1''', '''col_2''']		)
       for i, r in enumerate(UpperCAmelCase		):
        self.assertDictEqual(UpperCAmelCase  ,   example_records[i]		)






      def 					A__   (			self      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=self._create_example_records()
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       lowercase       : Tuple						=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 					A__   (			self      :		str		)	->	List[Any]:  # checks what happens with missing columns


       '''simple docstring'''

       lowercase       : Tuple						=[{'''col_1''': 1}, {'''col_2''': '''x'''}]
       lowercase       : Optional[Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertDictEqual(dset[0]  ,   {'''col_1''': 1}		)
       self.assertDictEqual(dset[1]  ,   {'''col_1''': None}		)  # NB: first record is used for columns






      def 					A__   (			self      :		List[Any]		)	->	int:  # checks if the type can be inferred from the second record


       '''simple docstring'''

       lowercase       : List[Any]						=[{'''col_1''': []}, {'''col_1''': [1, 2]}]
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertEqual(dset.info.features['''col_1''']  ,   Sequence(Value('''int64'''		)		)		)






      def 					A__   (			self      :		int		)	->	str:


       '''simple docstring'''

       lowercase       : List[str]						=Dataset.from_list([]		)
       self.assertEqual(len(UpperCAmelCase		)  ,   0		)
       self.assertListEqual(dset.column_names  ,   []		)



