






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


A__	:    Tuple			 = logging.get_logger(__name__)

A__	:    Union[str, Any]			 = {
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:List[str]             =			"xmod"


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=3_0522       ,	snake_case__					:  Tuple=768       ,	snake_case__					:  List[str]=12       ,	snake_case__					:  List[str]=12       ,	snake_case__					:  List[Any]=3072       ,	snake_case__					:  Any="gelu"       ,	snake_case__					:  int=0.1       ,	snake_case__					:  List[str]=0.1       ,	snake_case__					:  Union[str, Any]=512       ,	snake_case__					:  Optional[Any]=2       ,	snake_case__					:  List[Any]=0.02       ,	snake_case__					:  Tuple=1E-12       ,	snake_case__					:  Optional[int]=1       ,	snake_case__					:  List[Any]=0       ,	snake_case__					:  Tuple=2       ,	snake_case__					:  List[Any]="absolute"       ,	snake_case__					:  List[str]=True       ,	snake_case__					:  List[str]=None       ,	snake_case__					:  Optional[int]=False       ,	snake_case__					:  Optional[int]=2       ,	snake_case__					:  Tuple=False       ,	snake_case__					:  str=True       ,	snake_case__					:  Optional[Any]=True       ,	snake_case__					:  str=("en_XX",)       ,	snake_case__					:  str=None       ,	**snake_case__					:  str       ,	):
										super().__init__(pad_token_id=snake_case__       ,	bos_token_id=snake_case__       ,	eos_token_id=snake_case__       ,	**snake_case__ )

										lowerCamelCase_	:							Optional[Any]									=vocab_size
										lowerCamelCase_	:							str									=hidden_size
										lowerCamelCase_	:							Dict									=num_hidden_layers
										lowerCamelCase_	:							int									=num_attention_heads
										lowerCamelCase_	:							str									=hidden_act
										lowerCamelCase_	:							List[Any]									=intermediate_size
										lowerCamelCase_	:							Any									=hidden_dropout_prob
										lowerCamelCase_	:							Optional[int]									=attention_probs_dropout_prob
										lowerCamelCase_	:							Dict									=max_position_embeddings
										lowerCamelCase_	:							Optional[int]									=type_vocab_size
										lowerCamelCase_	:							List[str]									=initializer_range
										lowerCamelCase_	:							List[Any]									=layer_norm_eps
										lowerCamelCase_	:							Optional[Any]									=position_embedding_type
										lowerCamelCase_	:							Tuple									=use_cache
										lowerCamelCase_	:							Optional[Any]									=classifier_dropout
										lowerCamelCase_	:							Union[str, Any]									=pre_norm
										lowerCamelCase_	:							Union[str, Any]									=adapter_reduction_factor
										lowerCamelCase_	:							Tuple									=adapter_layer_norm
										lowerCamelCase_	:							Tuple									=adapter_reuse_layer_norm
										lowerCamelCase_	:							Dict									=ln_before_adapter
										lowerCamelCase_	:							Optional[int]									=list(snake_case__ )
										lowerCamelCase_	:							Any									=default_language







class        lowercase__      (							snake_case__       ):


			@property
			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										if self.task == "multiple-choice":
																	lowerCamelCase_	:							List[Any]									={0: "batch", 1: "choice", 2: "sequence"}
										else:
																	lowerCamelCase_	:							Optional[int]									={0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										    ] )











"""simple docstring"""



import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows

def 			_snake_case    (					lowerCamelCase__     :   Any  )    ->     Union[str, Any]:  # picklable for multiprocessing
							return i + 1

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 			_snake_case    (					)    ->     List[Any]:
							with parallel_backend("spark"  ):
														assert ParallelBackendConfig.backend_name == "spark"

							lowerCamelCase_	:							Optional[Any]									=[1, 2, 3]
							with pytest.raises(lowerCamelCase__  ):
														with parallel_backend("unsupported backend"  ):
																					map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=2  )

							with pytest.raises(lowerCamelCase__  ):
														with parallel_backend("unsupported backend"  ):
																					map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=-1  )

@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"    ,						[2, -1]  )
def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     Optional[Any]:
							lowerCamelCase_	:							str									=[1, 2]
							lowerCamelCase_	:							List[str]									={"a": 1, "b": 2}
							lowerCamelCase_	:							List[str]									={"a": [1, 2], "b": [3, 4]}
							lowerCamelCase_	:							Optional[int]									={"a": {"1": 1}, "b": 2}
							lowerCamelCase_	:							int									={"a": 1, "b": 2, "c": 3, "d": 4}
							lowerCamelCase_	:							Optional[int]									=[2, 3]
							lowerCamelCase_	:							List[Any]									={"a": 2, "b": 3}
							lowerCamelCase_	:							int									={"a": [2, 3], "b": [4, 5]}
							lowerCamelCase_	:							str									={"a": {"1": 2}, "b": 3}
							lowerCamelCase_	:							Dict									={"a": 2, "b": 3, "c": 4, "d": 5}

							with parallel_backend("spark"  ):
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa
														assert map_nested(lowerCamelCase__    ,						lowerCamelCase__    ,						num_proc=lowerCamelCase__  ) == expected_map_nested_sa



