'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
from .controlnets.controlnet import ControlNetConditioningEmbedding, ControlNetModel, ControlNetOutput, zero_module
from typing import Optional, Tuple, Union
from ..utils import deprecate
class ControlNetOutput(ControlNetOutput):
    def __init__(self, *args, **kwargs):
        deprecation_message = 'Importing `ControlNetOutput` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from sapiens_transformers.diffusers.models.controlnets.controlnet import ControlNetOutput`, instead.'
        deprecate('sapiens_transformers.diffusers.models.controlnet.ControlNetOutput', '0.34', deprecation_message)
        super().__init__(*args, **kwargs)
class ControlNetModel(ControlNetModel):
    def __init__(self, in_channels: int=4, conditioning_channels: int=3, flip_sin_to_cos: bool=True, freq_shift: int=0, down_block_types: Tuple[str, ...]=('CrossAttnDownBlock2D',
    'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D'), mid_block_type: Optional[str]='UNetMidBlock2DCrossAttn', only_cross_attention: Union[bool, Tuple[bool]]=False,
    block_out_channels: Tuple[int, ...]=(320, 640, 1280, 1280), layers_per_block: int=2, downsample_padding: int=1, mid_block_scale_factor: float=1, act_fn: str='silu',
    norm_num_groups: Optional[int]=32, norm_eps: float=1e-05, cross_attention_dim: int=1280, transformer_layers_per_block: Union[int, Tuple[int, ...]]=1, encoder_hid_dim: Optional[int]=None,
    encoder_hid_dim_type: Optional[str]=None, attention_head_dim: Union[int, Tuple[int, ...]]=8, num_attention_heads: Optional[Union[int, Tuple[int, ...]]]=None, use_linear_projection: bool=False,
    class_embed_type: Optional[str]=None, addition_embed_type: Optional[str]=None, addition_time_embed_dim: Optional[int]=None, num_class_embeds: Optional[int]=None, upcast_attention: bool=False,
    resnet_time_scale_shift: str='default', projection_class_embeddings_input_dim: Optional[int]=None, controlnet_conditioning_channel_order: str='rgb',
    conditioning_embedding_out_channels: Optional[Tuple[int, ...]]=(16, 32, 96, 256), global_pool_conditions: bool=False, addition_embed_type_num_heads: int=64):
        deprecation_message = 'Importing `ControlNetModel` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from sapiens_transformers.diffusers.models.controlnets.controlnet import ControlNetModel`, instead.'
        deprecate('sapiens_transformers.diffusers.models.controlnet.ControlNetModel', '0.34', deprecation_message)
        super().__init__(in_channels=in_channels, conditioning_channels=conditioning_channels, flip_sin_to_cos=flip_sin_to_cos, freq_shift=freq_shift, down_block_types=down_block_types,
        mid_block_type=mid_block_type, only_cross_attention=only_cross_attention, block_out_channels=block_out_channels, layers_per_block=layers_per_block, downsample_padding=downsample_padding,
        mid_block_scale_factor=mid_block_scale_factor, act_fn=act_fn, norm_num_groups=norm_num_groups, norm_eps=norm_eps, cross_attention_dim=cross_attention_dim,
        transformer_layers_per_block=transformer_layers_per_block, encoder_hid_dim=encoder_hid_dim, encoder_hid_dim_type=encoder_hid_dim_type, attention_head_dim=attention_head_dim,
        num_attention_heads=num_attention_heads, use_linear_projection=use_linear_projection, class_embed_type=class_embed_type, addition_embed_type=addition_embed_type,
        addition_time_embed_dim=addition_time_embed_dim, num_class_embeds=num_class_embeds, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift,
        projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
        conditioning_embedding_out_channels=conditioning_embedding_out_channels, global_pool_conditions=global_pool_conditions, addition_embed_type_num_heads=addition_embed_type_num_heads)
class ControlNetConditioningEmbedding(ControlNetConditioningEmbedding):
    def __init__(self, *args, **kwargs):
        deprecation_message = 'Importing `ControlNetConditioningEmbedding` from `diffusers.models.controlnet` is deprecated and this will be removed in a future version. Please use `from sapiens_transformers.diffusers.models.controlnets.controlnet import ControlNetConditioningEmbedding`, instead.'
        deprecate('sapiens_transformers.diffusers.models.controlnet.ControlNetConditioningEmbedding', '0.34', deprecation_message)
        super().__init__(*args, **kwargs)
'''
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
'''
