'''
    #################################################################################################################################################################################################
    # 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_sparsectrl import SparseControlNetConditioningEmbedding, SparseControlNetModel, SparseControlNetOutput, zero_module
from typing import Optional, Tuple, Union
from ..utils import deprecate
class SparseControlNetOutput(SparseControlNetOutput):
    def __init__(self, *args, **kwargs):
        deprecation_message = 'Importing `SparseControlNetOutput` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please use `from sapiens_transformers.diffusers.models.controlnets.controlnet_sparsectrl import SparseControlNetOutput`, instead.'
        deprecate('sapiens_transformers.diffusers.models.controlnet_sparsectrl.SparseControlNetOutput', '0.34', deprecation_message)
        super().__init__(*args, **kwargs)
class SparseControlNetConditioningEmbedding(SparseControlNetConditioningEmbedding):
    def __init__(self, *args, **kwargs):
        deprecation_message = 'Importing `SparseControlNetConditioningEmbedding` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please use `from sapiens_transformers.diffusers.models.controlnets.controlnet_sparsectrl import SparseControlNetConditioningEmbedding`, instead.'
        deprecate('sapiens_transformers.diffusers.models.controlnet_sparsectrl.SparseControlNetConditioningEmbedding', '0.34', deprecation_message)
        super().__init__(*args, **kwargs)
class SparseControlNetModel(SparseControlNetModel):
    def __init__(self, in_channels: int=4, conditioning_channels: int=4, flip_sin_to_cos: bool=True, freq_shift: int=0, down_block_types: Tuple[str, ...]=('CrossAttnDownBlockMotion',
    'CrossAttnDownBlockMotion', 'CrossAttnDownBlockMotion', 'DownBlockMotion'), 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=768,
    transformer_layers_per_block: Union[int, Tuple[int, ...]]=1, transformer_layers_per_mid_block: Optional[Union[int, Tuple[int]]]=None,
    temporal_transformer_layers_per_block: Union[int, Tuple[int, ...]]=1, attention_head_dim: Union[int, Tuple[int, ...]]=8, num_attention_heads: Optional[Union[int, Tuple[int, ...]]]=None,
    use_linear_projection: bool=False, upcast_attention: bool=False, resnet_time_scale_shift: str='default', conditioning_embedding_out_channels: Optional[Tuple[int, ...]]=(16, 32, 96, 256),
    global_pool_conditions: bool=False, controlnet_conditioning_channel_order: str='rgb', motion_max_seq_length: int=32, motion_num_attention_heads: int=8,
    concat_conditioning_mask: bool=True, use_simplified_condition_embedding: bool=True):
        deprecation_message = 'Importing `SparseControlNetModel` from `diffusers.models.controlnet_sparsectrl` is deprecated and this will be removed in a future version. Please use `from sapiens_transformers.diffusers.models.controlnets.controlnet_sparsectrl import SparseControlNetModel`, instead.'
        deprecate('sapiens_transformers.diffusers.models.controlnet_sparsectrl.SparseControlNetModel', '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,
        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, transformer_layers_per_mid_block=transformer_layers_per_mid_block,
        temporal_transformer_layers_per_block=temporal_transformer_layers_per_block, attention_head_dim=attention_head_dim, num_attention_heads=num_attention_heads,
        use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift,
        conditioning_embedding_out_channels=conditioning_embedding_out_channels, global_pool_conditions=global_pool_conditions,
        controlnet_conditioning_channel_order=controlnet_conditioning_channel_order, motion_max_seq_length=motion_max_seq_length, motion_num_attention_heads=motion_num_attention_heads,
        concat_conditioning_mask=concat_conditioning_mask, use_simplified_condition_embedding=use_simplified_condition_embedding)
'''
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
'''
