






from collections import OrderedDict
from typing import Mapping

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


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/config.json""",
    """distilbert-base-uncased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/config.json""",
    """distilbert-base-cased-distilled-squad""": (
        """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"""
    ),
    """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json""",
    """distilbert-base-multilingual-cased""": (
        """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"""
    ),
    """distilbert-base-uncased-finetuned-sst-2-english""": (
        """https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		List[Any]			=	'''distilbert'''
     UpperCamelCase_							:		Tuple			=	{
         '''hidden_size''': '''dim''',
         '''num_attention_heads''': '''n_heads''',
         '''num_hidden_layers''': '''n_layers''',
     }


     def __init__(				self       :	Optional[int]					, UpperCAmelCase_       :	List[Any]=3_0522					, UpperCAmelCase_       :	str=512					, UpperCAmelCase_       :	Tuple=False					, UpperCAmelCase_       :	Optional[Any]=6					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	List[Any]=768					, UpperCAmelCase_       :	int=4 * 768					, UpperCAmelCase_       :	Optional[int]=0.1					, UpperCAmelCase_       :	Tuple=0.1					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	int=0.02					, UpperCAmelCase_       :	List[str]=0.1					, UpperCAmelCase_       :	Tuple=0.2					, UpperCAmelCase_       :	int=0					, **UpperCAmelCase_       :	int					, ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      Tuple							=    sinusoidal_pos_embds
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    n_layers
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    n_heads
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    dim
       SCREAMING_SNAKE_CASE       :      List[Any]							=    hidden_dim
       SCREAMING_SNAKE_CASE       :      Tuple							=    dropout
       SCREAMING_SNAKE_CASE       :      Dict							=    attention_dropout
       SCREAMING_SNAKE_CASE       :      Tuple							=    activation
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    qa_dropout
       SCREAMING_SNAKE_CASE       :      List[str]							=    seq_classif_dropout
       super().__init__(**UpperCAmelCase_					, pad_token_id=UpperCAmelCase_      )





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	int      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Any							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Any							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )














from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .multicontrolnet import MultiControlNetModel
   from .pipeline_controlnet import StableDiffusionControlNetPipeline
   from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
   from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
   from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






