

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						=					{
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}

class  a     (		UpperCAmelCase ):
						_lowercase			  =	"flaubert"
						_lowercase			  =	{
						    "hidden_size": "emb_dim",
						    "num_attention_heads": "n_heads",
						    "num_hidden_layers": "n_layers",
						    "n_words": "vocab_size",  # For backward compatibility
						}


						def __init__(				self  ,    A_=False  ,    A_=0.0  ,    A_=30145  ,    A_=2048  ,    A_=12  ,    A_=16  ,    A_=0.1  ,    A_=0.1  ,    A_=True  ,    A_=False  ,    A_=False  ,    A_=False  ,    A_=1  ,    A_=True  ,    A_=512  ,    A_=2048**-0.5  ,    A_=1e-12  ,    A_=0.02  ,    A_=0  ,    A_=1  ,    A_=2  ,    A_=3  ,    A_=5  ,    A_=True  ,    A_="first"  ,    A_=True  ,    A_=None  ,    A_=True  ,    A_=0.1  ,    A_=5  ,    A_=5  ,    A_=0  ,    A_=0  ,    A_=2  ,    A_=0  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			pre_norm
										_UpperCAmelCase				:						Union[str, Any]       						=			layerdrop
										_UpperCAmelCase				:						List[str]       						=			vocab_size
										_UpperCAmelCase				:						Any       						=			emb_dim
										_UpperCAmelCase				:						List[str]       						=			n_layers
										_UpperCAmelCase				:						Tuple       						=			n_heads
										_UpperCAmelCase				:						Union[str, Any]       						=			dropout
										_UpperCAmelCase				:						Any       						=			attention_dropout
										_UpperCAmelCase				:						List[str]       						=			gelu_activation
										_UpperCAmelCase				:						Dict       						=			sinusoidal_embeddings
										_UpperCAmelCase				:						Any       						=			causal
										_UpperCAmelCase				:						str       						=			asm
										_UpperCAmelCase				:						List[Any]       						=			n_langs
										_UpperCAmelCase				:						List[str]       						=			use_lang_emb
										_UpperCAmelCase				:						Tuple       						=			layer_norm_eps
										_UpperCAmelCase				:						Dict       						=			bos_index
										_UpperCAmelCase				:						str       						=			eos_index
										_UpperCAmelCase				:						List[str]       						=			pad_index
										_UpperCAmelCase				:						List[str]       						=			unk_index
										_UpperCAmelCase				:						Tuple       						=			mask_index
										_UpperCAmelCase				:						Any       						=			is_encoder
										_UpperCAmelCase				:						Dict       						=			max_position_embeddings
										_UpperCAmelCase				:						List[str]       						=			embed_init_std
										_UpperCAmelCase				:						Optional[int]       						=			init_std
										_UpperCAmelCase				:						Optional[int]       						=			summary_type
										_UpperCAmelCase				:						Union[str, Any]       						=			summary_use_proj
										_UpperCAmelCase				:						int       						=			summary_activation
										_UpperCAmelCase				:						Dict       						=			summary_proj_to_labels
										_UpperCAmelCase				:						List[Any]       						=			summary_first_dropout
										_UpperCAmelCase				:						Optional[int]       						=			start_n_top
										_UpperCAmelCase				:						List[Any]       						=			end_n_top
										_UpperCAmelCase				:						Any       						=			mask_token_id
										_UpperCAmelCase				:						Any       						=			lang_id

										if "n_words" in kwargs:
														_UpperCAmelCase				:						Union[str, Any]       						=			kwargs["n_words"]

										super().__init__(pad_token_id=A_  ,    bos_token_id=A_  ,    **A_		)







class  a     (		UpperCAmelCase ):


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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








from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase					:     Optional[int]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[int]					  =  {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				lowerCAmelCase__			=   "squeezebert"



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple=3_0522						,		_UpperCAmelCase					:						str=768						,		_UpperCAmelCase					:						int=12						,		_UpperCAmelCase					:						Tuple=12						,		_UpperCAmelCase					:						Tuple=3072						,		_UpperCAmelCase					:						Union[str, Any]="gelu"						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Tuple=2						,		_UpperCAmelCase					:						Optional[Any]=0.02						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=768						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Union[str, Any]=4						,		_UpperCAmelCase					:						List[str]=4						,		**_UpperCAmelCase					:						int						,		)     ->			Any:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase						,		**_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					hidden_size
								_lowerCAmelCase			:			Tuple        =					num_hidden_layers
								_lowerCAmelCase			:			int        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					type_vocab_size
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			int        =					layer_norm_eps
								_lowerCAmelCase			:			int        =					embedding_size
								_lowerCAmelCase			:			Optional[int]        =					q_groups
								_lowerCAmelCase			:			List[Any]        =					k_groups
								_lowerCAmelCase			:			List[Any]        =					v_groups
								_lowerCAmelCase			:			Dict        =					post_attention_groups
								_lowerCAmelCase			:			Optional[Any]        =					intermediate_groups
								_lowerCAmelCase			:			str        =					output_groups






class 				__snake_case     (_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								if self.task == "multiple-choice":
												_lowerCAmelCase			:			Optional[int]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
												_lowerCAmelCase			:			int        =					{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]					)

