from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase					:   Any							=		{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							pass

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase					:   Optional[int]							=		[
							    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'RoCBertForCausalLM',
							    'RoCBertForMaskedLM',
							    'RoCBertForMultipleChoice',
							    'RoCBertForPreTraining',
							    'RoCBertForQuestionAnswering',
							    'RoCBertForSequenceClassification',
							    'RoCBertForTokenClassification',
							    'RoCBertLayer',
							    'RoCBertModel',
							    'RoCBertPreTrainedModel',
							    'load_tf_weights_in_roc_bert',
							]

if TYPE_CHECKING:
							from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
							from .tokenization_roc_bert import RoCBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														raise OptionalDependencyNotAvailable()

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roc_bert import (
														    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoCBertForCausalLM,
														    RoCBertForMaskedLM,
														    RoCBertForMultipleChoice,
														    RoCBertForPreTraining,
														    RoCBertForQuestionAnswering,
														    RoCBertForSequenceClassification,
														    RoCBertForTokenClassification,
														    RoCBertLayer,
														    RoCBertModel,
														    RoCBertPreTrainedModel,
														    load_tf_weights_in_roc_bert,
														)


else:
							import sys

							_lowerCamelCase					:   Tuple							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_lowerCamelCase					:   Tuple							=		{
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							"""ernie_m"""
							lowercase__						:       Dict[str, str]								=							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					int = 250_002	,							_UpperCamelCase   :					int = 768	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 3_072	,							_UpperCamelCase   :					str = "gelu"	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					int = 514	,							_UpperCamelCase   :					float = 0.0_2	,							_UpperCamelCase   :					int = 1	,							_UpperCamelCase   :					float = 1e-05	,							_UpperCamelCase   :					int=None	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					int=0.0	,							**_UpperCamelCase   :					Union[str, Any]	,							)					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(pad_token_id=_UpperCamelCase	,							**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       classifier_dropout
												SCREAMING_SNAKE_CASE    			=       is_decoder
												SCREAMING_SNAKE_CASE    			=       act_dropout




