


'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase   : Optional[int]             =				logging.get_logger(__name__)

lowerCAmelCase   : List[Any]             =				{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				'''convbert'''

				def __init__(     self							,      a_=3_0_5_2_2							,      a_=7_6_8							,      a_=1_2							,      a_=1_2							,      a_=3_0_7_2							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=2							,      a_=0.02							,      a_=1e-1_2							,      a_=1							,      a_=0							,      a_=2							,      a_=7_6_8							,      a_=2							,      a_=9							,      a_=1							,      a_=None							,      **a_							,      )    ->  int:
					super().__init__(
					    pad_token_id=a_							,      bos_token_id=a_							,      eos_token_id=a_							,      **a_							,      )

					lowercase						:		int   					=		vocab_size
					lowercase						:		str   					=		hidden_size
					lowercase						:		Any   					=		num_hidden_layers
					lowercase						:		List[Any]   					=		num_attention_heads
					lowercase						:		List[Any]   					=		intermediate_size
					lowercase						:		Any   					=		hidden_act
					lowercase						:		str   					=		hidden_dropout_prob
					lowercase						:		int   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		List[Any]   					=		type_vocab_size
					lowercase						:		List[str]   					=		initializer_range
					lowercase						:		str   					=		layer_norm_eps
					lowercase						:		int   					=		embedding_size
					lowercase						:		Union[str, Any]   					=		head_ratio
					lowercase						:		int   					=		conv_kernel_size
					lowercase						:		Any   					=		num_groups
					lowercase						:		str   					=		classifier_dropout




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				@property
				def 	a__		(     self							)    ->  Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						lowercase						:		List[Any]   					=		{0: "batch", 1: "choice", 2: "sequence"}
					else:
						lowercase						:		Dict   					=		{0: "batch", 1: "sequence"}
					return OrderedDict(
					    [
					        ("input_ids", dynamic_axis),
					        ("attention_mask", dynamic_axis),
					        ("token_type_ids", dynamic_axis),
					    ]							)






'''simple docstring'''
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


