
from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


lowercase__							:							List[Any]									=			{
    "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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'ernie_m'
							_snake_case     		=  {"dropout": "classifier_dropout", "num_classes": "num_labels"}



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = 250002     ,	SCREAMING_SNAKE_CASE_ = 768     ,	SCREAMING_SNAKE_CASE_ = 12     ,	SCREAMING_SNAKE_CASE_ = 12     ,	SCREAMING_SNAKE_CASE_ = 3072     ,	SCREAMING_SNAKE_CASE_ = "gelu"     ,	SCREAMING_SNAKE_CASE_ = 0.1     ,	SCREAMING_SNAKE_CASE_ = 0.1     ,	SCREAMING_SNAKE_CASE_ = 514     ,	SCREAMING_SNAKE_CASE_ = 0.0_2     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = 1E-05     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=0.0     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Dict:





													'''simple docstring'''






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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__							:							Dict									=			{"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			["ViTFeatureExtractor"]
				lowercase__							:							str									=			["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[str]									=			[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[Any]									=			[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

				lowercase__							:							List[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




