



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     =	{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
						a__ =	"mobilebert"







						def __init__(						self   :      int						,						__lowerCamelCase   :      Optional[int]=3_05_22						,						__lowerCamelCase   :      List[Any]=5_12						,						__lowerCamelCase   :      Optional[Any]=24						,						__lowerCamelCase   :      str=4						,						__lowerCamelCase   :      str=5_12						,						__lowerCamelCase   :      List[str]="relu"						,						__lowerCamelCase   :      Optional[int]=0.0						,						__lowerCamelCase   :      Any=0.1						,						__lowerCamelCase   :      Optional[int]=5_12						,						__lowerCamelCase   :      Optional[int]=2						,						__lowerCamelCase   :      Union[str, Any]=0.02						,						__lowerCamelCase   :      Tuple=1e-12						,						__lowerCamelCase   :      int=0						,						__lowerCamelCase   :      List[Any]=1_28						,						__lowerCamelCase   :      Optional[Any]=True						,						__lowerCamelCase   :      str=True						,						__lowerCamelCase   :      List[Any]=1_28						,						__lowerCamelCase   :      List[Any]=False						,						__lowerCamelCase   :      Any=True						,						__lowerCamelCase   :      Union[str, Any]=4						,						__lowerCamelCase   :      List[Any]="no_norm"						,						__lowerCamelCase   :      Optional[Any]=True						,						__lowerCamelCase   :      str=None						,						**__lowerCamelCase   :      int						,						)   ->					Tuple:
									super().__init__(pad_token_id=__lowerCamelCase						,						**__lowerCamelCase			)

									A				:       Any														=    vocab_size
									A				:       Optional[int]														=    hidden_size
									A				:       Any														=    num_hidden_layers
									A				:       Dict														=    num_attention_heads
									A				:       List[Any]														=    hidden_act
									A				:       Dict														=    intermediate_size
									A				:       Any														=    hidden_dropout_prob
									A				:       Union[str, Any]														=    attention_probs_dropout_prob
									A				:       Tuple														=    max_position_embeddings
									A				:       List[Any]														=    type_vocab_size
									A				:       int														=    initializer_range
									A				:       Dict														=    layer_norm_eps
									A				:       Union[str, Any]														=    embedding_size
									A				:       List[Any]														=    trigram_input
									A				:       List[str]														=    use_bottleneck
									A				:       Optional[Any]														=    intra_bottleneck_size
									A				:       Optional[Any]														=    use_bottleneck_attention
									A				:       Any														=    key_query_shared_bottleneck
									A				:       Tuple														=    num_feedforward_networks
									A				:       Optional[int]														=    normalization_type
									A				:       Any														=    classifier_activation

									if self.use_bottleneck:
												A				:       List[Any]														=    intra_bottleneck_size
									else:
												A				:       Tuple														=    hidden_size

									A				:       List[Any]														=    classifier_dropout







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







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




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						*__lowerCamelCase   :      Tuple						,						**__lowerCamelCase   :      Dict			)   ->					None:
									warnings.warn(
									    "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
									    " Please use DetrImageProcessor instead."						,						__lowerCamelCase						,						)
									super().__init__(*__lowerCamelCase						,						**__lowerCamelCase			)