

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







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		a__       							=	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		a__       							=	'''mobilebert'''


		def __init__(					self						,							__snake_case=3_05_22						,							__snake_case=5_12						,							__snake_case=24						,							__snake_case=4						,							__snake_case=5_12						,							__snake_case="relu"						,							__snake_case=0.0						,							__snake_case=0.1						,							__snake_case=5_12						,							__snake_case=2						,							__snake_case=0.0_2						,							__snake_case=1e-12						,							__snake_case=0						,							__snake_case=1_28						,							__snake_case=True						,							__snake_case=True						,							__snake_case=1_28						,							__snake_case=False						,							__snake_case=True						,							__snake_case=4						,							__snake_case="no_norm"						,							__snake_case=True						,							__snake_case=None						,							**__snake_case						,							):
			super().__init__(pad_token_id=lowerCamelCase__						,							**lowerCamelCase__)

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

			if self.use_bottleneck:
				_UpperCamelCase :		Tuple								=    intra_bottleneck_size
			else:
				_UpperCamelCase :		Union[str, Any]								=    hidden_size

			_UpperCamelCase :		Dict								=    classifier_dropout







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		@property
		def 		A__       (					self):




			if self.task == "multiple-choice":
				_UpperCamelCase :		Any								=    {0: "batch", 1: "choice", 2: "sequence"}
			else:
				_UpperCamelCase :		List[str]								=    {0: "batch", 1: "sequence"}
			return OrderedDict(
			    [
			        ('input_ids', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			        ('token_type_ids', dynamic_axis),
			    ])








"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								return self.tokenizer.batch_decode(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								return self.tokenizer.decode(*lowerCamelCase__	,					**lowerCamelCase__)







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





