

'''simple docstring'''


from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}



class 					lowercase						(  _SCREAMING_SNAKE_CASE ):
							_lowerCamelCase		:      Optional[int]=   "efficientformer"


							def __init__(			self    ,			_snake_case = [3, 2, 6, 4]    ,			_snake_case = [48, 96, 224, 448]    ,			_snake_case = [True, True, True, True]    ,			_snake_case = 448    ,			_snake_case = 32    ,			_snake_case = 4    ,			_snake_case = 7    ,			_snake_case = 5    ,			_snake_case = 8    ,			_snake_case = 4    ,			_snake_case = 0.0    ,			_snake_case = 16    ,			_snake_case = 3    ,			_snake_case = 3    ,			_snake_case = 3    ,			_snake_case = 2    ,			_snake_case = 1    ,			_snake_case = 0.0    ,			_snake_case = 1    ,			_snake_case = True    ,			_snake_case = True    ,			_snake_case = 1e-5    ,			_snake_case = "gelu"    ,			_snake_case = 0.02    ,			_snake_case = 1e-12    ,			_snake_case = 224    ,			_snake_case = 1e-05    ,			**_snake_case    ,			)							->				None:
								super().__init__(**A_)

								UpperCAmelCase_		:      List[Any]       	=					hidden_act
								UpperCAmelCase_		:      int       	=					hidden_dropout_prob
								UpperCAmelCase_		:      List[str]       	=					hidden_sizes
								UpperCAmelCase_		:      str       	=					num_hidden_layers
								UpperCAmelCase_		:      Any       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					initializer_range
								UpperCAmelCase_		:      int       	=					layer_norm_eps
								UpperCAmelCase_		:      Any       	=					patch_size
								UpperCAmelCase_		:      Dict       	=					num_channels
								UpperCAmelCase_		:      int       	=					depths
								UpperCAmelCase_		:      Optional[Any]       	=					mlp_expansion_ratio
								UpperCAmelCase_		:      Union[str, Any]       	=					downsamples
								UpperCAmelCase_		:      List[Any]       	=					dim
								UpperCAmelCase_		:      Optional[int]       	=					key_dim
								UpperCAmelCase_		:      str       	=					attention_ratio
								UpperCAmelCase_		:      Dict       	=					resolution
								UpperCAmelCase_		:      Any       	=					pool_size
								UpperCAmelCase_		:      Any       	=					downsample_patch_size
								UpperCAmelCase_		:      List[str]       	=					downsample_stride
								UpperCAmelCase_		:      str       	=					downsample_pad
								UpperCAmelCase_		:      str       	=					drop_path_rate
								UpperCAmelCase_		:      Tuple       	=					num_metaad_blocks
								UpperCAmelCase_		:      List[Any]       	=					distillation
								UpperCAmelCase_		:      List[Any]       	=					use_layer_scale
								UpperCAmelCase_		:      str       	=					layer_scale_init_value
								UpperCAmelCase_		:      Optional[Any]       	=					image_size
								UpperCAmelCase_		:      Any       	=					batch_norm_eps









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "xlm-roberta-xl"


							def __init__(			self    ,			_snake_case=25_0880    ,			_snake_case=2560    ,			_snake_case=36    ,			_snake_case=32    ,			_snake_case=1_0240    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=514    ,			_snake_case=1    ,			_snake_case=0.02    ,			_snake_case=1e-05    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=2    ,			_snake_case="absolute"    ,			_snake_case=True    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Optional[Any]:
								super().__init__(pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      List[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Tuple       	=					hidden_dropout_prob
								UpperCAmelCase_		:      List[str]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      str       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps
								UpperCAmelCase_		:      Dict       	=					position_embedding_type
								UpperCAmelCase_		:      int       	=					use_cache
								UpperCAmelCase_		:      List[Any]       	=					classifier_dropout



class 					lowercase						(  a_ ):


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






