


"""simple docstring"""




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__SCREAMING_SNAKE_CASE      		=    {
    """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 __snake_case		(       _a   ):







   """simple docstring"""




   lowerCAmelCase_					: Union[str, Any]  						=      '''ernie_m'''
   lowerCAmelCase_					: Dict[str, str]  						=      {"dropout": "classifier_dropout", "num_classes": "num_labels"}




   def __init__(		self				:List[Any]					,	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 = 0.02					,	UpperCamelCase__				:int = 1					,	UpperCamelCase__				:float = 1E-05					,	UpperCamelCase__				:str=None					,	UpperCamelCase__				:str=False					,	UpperCamelCase__				:Optional[Any]=0.0					,	**UpperCamelCase__				:int					,	):
          super().__init__(pad_token_id=UpperCamelCase__					,	**UpperCamelCase__							)
          _a							 =					vocab_size
          _a							 =					hidden_size
          _a							 =					num_hidden_layers
          _a							 =					num_attention_heads
          _a							 =					intermediate_size
          _a							 =					hidden_act
          _a							 =					hidden_dropout_prob
          _a							 =					attention_probs_dropout_prob
          _a							 =					max_position_embeddings
          _a							 =					initializer_range
          _a							 =					layer_norm_eps
          _a							 =					classifier_dropout
          _a							 =					is_decoder
          _a							 =					act_dropout








from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[List[PIL.Image.Image], np.ndarray]
       __lowerCAmelCase    :		Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


