"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__    :    Optional[Any]			=      logging.get_logger(__name__)

lowerCAmelCase__    :    List[str]			=      {
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class 							snake_case			(					_lowerCamelCase							):




					"""simple docstring"""

					snake_case__							       =					"rembert"







					def __init__(							self  :      List[str]       ,lowerCamelCase__  :      List[str]=250_300       ,lowerCamelCase__  :      Optional[Any]=1_152       ,lowerCamelCase__  :      int=32       ,lowerCamelCase__  :      str=18       ,lowerCamelCase__  :      int=256       ,lowerCamelCase__  :      List[Any]=1_664       ,lowerCamelCase__  :      Tuple=4_608       ,lowerCamelCase__  :      int="gelu"       ,lowerCamelCase__  :      Tuple=0.0       ,lowerCamelCase__  :      Optional[Any]=0.0       ,lowerCamelCase__  :      List[Any]=0.1       ,lowerCamelCase__  :      Dict=512       ,lowerCamelCase__  :      Optional[int]=2       ,lowerCamelCase__  :      Dict=0.0_2       ,lowerCamelCase__  :      Any=1e-12       ,lowerCamelCase__  :      Union[str, Any]=True       ,lowerCamelCase__  :      Dict=0       ,lowerCamelCase__  :      str=312       ,lowerCamelCase__  :      Union[str, Any]=313       ,**lowerCamelCase__  :      Any       ,):
									super().__init__(pad_token_id=a_       ,bos_token_id=a_       ,eos_token_id=a_       ,**a_     )

									UpperCAmelCase__			 =				vocab_size
									UpperCAmelCase__			 =				input_embedding_size
									UpperCAmelCase__			 =				output_embedding_size
									UpperCAmelCase__			 =				max_position_embeddings
									UpperCAmelCase__			 =				hidden_size
									UpperCAmelCase__			 =				num_hidden_layers
									UpperCAmelCase__			 =				num_attention_heads
									UpperCAmelCase__			 =				intermediate_size
									UpperCAmelCase__			 =				hidden_act
									UpperCAmelCase__			 =				hidden_dropout_prob
									UpperCAmelCase__			 =				attention_probs_dropout_prob
									UpperCAmelCase__			 =				classifier_dropout_prob
									UpperCAmelCase__			 =				initializer_range
									UpperCAmelCase__			 =				type_vocab_size
									UpperCAmelCase__			 =				layer_norm_eps
									UpperCAmelCase__			 =				use_cache
									UpperCAmelCase__			 =				False






class 							snake_case			(					_lowerCamelCase							):




					"""simple docstring"""

					@property
					def 						__lowerCAmelCase		(							self  :      Dict     ):
									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),
									        ('token_type_ids', dynamic_axis),
									    ]     )







					@property
					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									return 1e-4








'''simple docstring'''






from unittest.mock import patch

import datasets
from datasets import Dataset




def        snake_case_   (	)->						str:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					Dataset.from_dict({"""col_1""": [3, 2, 0, 1]}						)

       with patch("""tqdm.auto.tqdm"""						) as mock_tqdm:
              datasets.disable_progress_bar()
              dset.map(lambda lowerCAmelCase_						: {"col_2": x["col_1"] + 1}						)
              mock_tqdm.assert_not_called()

              mock_tqdm.reset_mock()

              datasets.enable_progress_bar()
              dset.map(lambda lowerCAmelCase_						: {"col_2": x["col_1"] + 1}						)
              mock_tqdm.assert_called()



