






from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase      =				logging.get_logger(__name__)

__UpperCAmelCase      =				{
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				List[Any]  					='''electra'''





     def __init__(		self					:	int  ,  lowerCAmelCase__					:	Any=3_0_5_2_2  ,  lowerCAmelCase__					:	List[str]=1_2_8  ,  lowerCAmelCase__					:	Optional[int]=2_5_6  ,  lowerCAmelCase__					:	List[str]=1_2  ,  lowerCAmelCase__					:	Dict=4  ,  lowerCAmelCase__					:	List[Any]=1_0_2_4  ,  lowerCAmelCase__					:	List[Any]="gelu"  ,  lowerCAmelCase__					:	Union[str, Any]=0.1  ,  lowerCAmelCase__					:	List[str]=0.1  ,  lowerCAmelCase__					:	Tuple=5_1_2  ,  lowerCAmelCase__					:	int=2  ,  lowerCAmelCase__					:	int=0.02  ,  lowerCAmelCase__					:	str=1e-12  ,  lowerCAmelCase__					:	List[str]="first"  ,  lowerCAmelCase__					:	Any=True  ,  lowerCAmelCase__					:	List[Any]="gelu"  ,  lowerCAmelCase__					:	Optional[Any]=0.1  ,  lowerCAmelCase__					:	List[Any]=0  ,  lowerCAmelCase__					:	List[str]="absolute"  ,  lowerCAmelCase__					:	Tuple=True  ,  lowerCAmelCase__					:	List[str]=None  ,  **lowerCAmelCase__					:	int  ,  )							->	List[str]:

      """simple docstring"""
      super().__init__(pad_token_id=lowerCAmelCase__  ,  **lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		vocab_size
      SCREAMING_SNAKE_CASE				    =		embedding_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				    =		type_vocab_size
      SCREAMING_SNAKE_CASE				    =		initializer_range
      SCREAMING_SNAKE_CASE				    =		layer_norm_eps

      SCREAMING_SNAKE_CASE				    =		summary_type
      SCREAMING_SNAKE_CASE				    =		summary_use_proj
      SCREAMING_SNAKE_CASE				    =		summary_activation
      SCREAMING_SNAKE_CASE				    =		summary_last_dropout
      SCREAMING_SNAKE_CASE				    =		position_embedding_type
      SCREAMING_SNAKE_CASE				    =		use_cache
      SCREAMING_SNAKE_CASE				    =		classifier_dropout

class 	a_(		lowercase__	):



     """simple docstring"""



     @property
     def     __UpperCamelCase						(		self					:	List[str])							->	Mapping[str, Mapping[int, str]]:

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





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "LayoutLMForMaskedLM",
				    "LayoutLMForSequenceClassification",
				    "LayoutLMForTokenClassification",
				    "LayoutLMForQuestionAnswering",
				    "LayoutLMModel",
				    "LayoutLMPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFLayoutLMForMaskedLM",
				    "TFLayoutLMForSequenceClassification",
				    "TFLayoutLMForTokenClassification",
				    "TFLayoutLMForQuestionAnswering",
				    "TFLayoutLMMainLayer",
				    "TFLayoutLMModel",
				    "TFLayoutLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
				from .tokenization_layoutlm import LayoutLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlm import (
								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMForMaskedLM,
								    LayoutLMForQuestionAnswering,
								    LayoutLMForSequenceClassification,
								    LayoutLMForTokenClassification,
								    LayoutLMModel,
								    LayoutLMPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlm import (
								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMForMaskedLM,
								    TFLayoutLMForQuestionAnswering,
								    TFLayoutLMForSequenceClassification,
								    TFLayoutLMForTokenClassification,
								    TFLayoutLMMainLayer,
								    TFLayoutLMModel,
								    TFLayoutLMPreTrainedModel,
								)

else:
				import sys

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





