
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  42




class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_ ):





     """simple docstring"""







     @register_to_config
     def __init__(							self						,						__SCREAMING_SNAKE_CASE = 6_5_5_3_6						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = 2						,						__SCREAMING_SNAKE_CASE = 2						,						__SCREAMING_SNAKE_CASE = 0						,						__SCREAMING_SNAKE_CASE = "fourier"						,						__SCREAMING_SNAKE_CASE = True						,						__SCREAMING_SNAKE_CASE = False						,						__SCREAMING_SNAKE_CASE = 0.0						,						__SCREAMING_SNAKE_CASE = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")						,						__SCREAMING_SNAKE_CASE = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")						,						__SCREAMING_SNAKE_CASE = "UNetMidBlock1D"						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = (3_2, 3_2, 6_4)						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = 8						,						__SCREAMING_SNAKE_CASE = 1						,						__SCREAMING_SNAKE_CASE = False						,						)  ->   List[str]:




           """simple docstring"""







           super().__init__()
           UpperCamelCase__    :   Optional[Any]					=				sample_size

           # time
           if time_embedding_type == "fourier":
                 UpperCamelCase__    :   Optional[Any]					=				GaussianFourierProjection(
                     embedding_size=8						,						set_W_to_weight=__SCREAMING_SNAKE_CASE						,						log=__SCREAMING_SNAKE_CASE						,						flip_sin_to_cos=__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Any					=				2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                 UpperCamelCase__    :   Any					=				Timesteps(
                     block_out_channels[0]						,						flip_sin_to_cos=__SCREAMING_SNAKE_CASE						,						downscale_freq_shift=__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   List[str]					=				block_out_channels[0]

           if use_timestep_embedding:
                 UpperCamelCase__    :   Union[str, Any]					=				block_out_channels[0] * 4
                 UpperCamelCase__    :   Optional[Any]					=				TimestepEmbedding(
                     in_channels=__SCREAMING_SNAKE_CASE						,						time_embed_dim=__SCREAMING_SNAKE_CASE						,						act_fn=__SCREAMING_SNAKE_CASE						,						out_dim=block_out_channels[0]						,						)

           UpperCamelCase__    :   List[str]					=				nn.ModuleList([]  )
           UpperCamelCase__    :   Dict					=				None
           UpperCamelCase__    :   Tuple					=				nn.ModuleList([]  )
           UpperCamelCase__    :   Optional[Any]					=				None

           # down
           UpperCamelCase__    :   Union[str, Any]					=				in_channels
           for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Optional[int]					=				output_channel
                 UpperCamelCase__    :   Optional[Any]					=				block_out_channels[i]

                 if i == 0:
                       input_channel += extra_in_channels

                 UpperCamelCase__    :   int					=				i == len(__SCREAMING_SNAKE_CASE  ) - 1

                 UpperCamelCase__    :   List[str]					=				get_down_block(
                     __SCREAMING_SNAKE_CASE						,						num_layers=__SCREAMING_SNAKE_CASE						,						in_channels=__SCREAMING_SNAKE_CASE						,						out_channels=__SCREAMING_SNAKE_CASE						,						temb_channels=block_out_channels[0]						,						add_downsample=not is_final_block or downsample_each_block						,						)
                 self.down_blocks.append(__SCREAMING_SNAKE_CASE  )

           # mid
           UpperCamelCase__    :   Dict					=				get_mid_block(
               __SCREAMING_SNAKE_CASE						,						in_channels=block_out_channels[-1]						,						mid_channels=block_out_channels[-1]						,						out_channels=block_out_channels[-1]						,						embed_dim=block_out_channels[0]						,						num_layers=__SCREAMING_SNAKE_CASE						,						add_downsample=__SCREAMING_SNAKE_CASE						,						)

           # up
           UpperCamelCase__    :   Union[str, Any]					=				list(reversed(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Union[str, Any]					=				reversed_block_out_channels[0]
           if out_block_type is None:
                 UpperCamelCase__    :   Optional[Any]					=				out_channels
           else:
                 UpperCamelCase__    :   Any					=				block_out_channels[0]

           for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   str					=				output_channel
                 UpperCamelCase__    :   List[Any]					=				(
                     reversed_block_out_channels[i + 1] if i < len(__SCREAMING_SNAKE_CASE  ) - 1 else final_upsample_channels
                 )

                 UpperCamelCase__    :   Union[str, Any]					=				i == len(__SCREAMING_SNAKE_CASE  ) - 1

                 UpperCamelCase__    :   int					=				get_up_block(
                     __SCREAMING_SNAKE_CASE						,						num_layers=__SCREAMING_SNAKE_CASE						,						in_channels=__SCREAMING_SNAKE_CASE						,						out_channels=__SCREAMING_SNAKE_CASE						,						temb_channels=block_out_channels[0]						,						add_upsample=not is_final_block						,						)
                 self.up_blocks.append(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   List[Any]					=				output_channel

           # out
           UpperCamelCase__    :   int					=				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,						3_2  )
           UpperCamelCase__    :   Optional[int]					=				get_out_block(
               out_block_type=__SCREAMING_SNAKE_CASE						,						num_groups_out=__SCREAMING_SNAKE_CASE						,						embed_dim=block_out_channels[0]						,						out_channels=__SCREAMING_SNAKE_CASE						,						act_fn=__SCREAMING_SNAKE_CASE						,						fc_dim=block_out_channels[-1] // 4						,						)






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = True						,						)  ->   Union[UNetaDOutput, Tuple]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				timestep
           if not torch.is_tensor(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Tuple					=				torch.tensor([timesteps]						,						dtype=torch.long						,						device=sample.device  )
           elif torch.is_tensor(__SCREAMING_SNAKE_CASE  ) and len(timesteps.shape  ) == 0:
                 UpperCamelCase__    :   Optional[Any]					=				timesteps[None].to(sample.device  )

           UpperCamelCase__    :   Tuple					=				self.time_proj(__SCREAMING_SNAKE_CASE  )
           if self.config.use_timestep_embedding:
                 UpperCamelCase__    :   Optional[Any]					=				self.time_mlp(__SCREAMING_SNAKE_CASE  )
           else:
                 UpperCamelCase__    :   Any					=				timestep_embed[..., None]
                 UpperCamelCase__    :   Optional[int]					=				timestep_embed.repeat([1, 1, sample.shape[2]]  ).to(sample.dtype  )
                 UpperCamelCase__    :   List[Any]					=				timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])  )

           # 2. down
           UpperCamelCase__    :   Optional[Any]					=				()
           for downsample_block in self.down_blocks:
                 UpperCamelCase__				,UpperCamelCase__    :   List[str]					=				downsample_block(hidden_states=__SCREAMING_SNAKE_CASE						,						temb=__SCREAMING_SNAKE_CASE  )
                 down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                 UpperCamelCase__    :   Optional[int]					=				self.mid_block(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks  ):
                 UpperCamelCase__    :   Optional[int]					=				down_block_res_samples[-1:]
                 UpperCamelCase__    :   Optional[int]					=				down_block_res_samples[:-1]
                 UpperCamelCase__    :   List[str]					=				upsample_block(__SCREAMING_SNAKE_CASE						,						res_hidden_states_tuple=__SCREAMING_SNAKE_CASE						,						temb=__SCREAMING_SNAKE_CASE  )

           # 5. post-process
           if self.out_block:
                 UpperCamelCase__    :   Tuple					=				self.out_block(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

           if not return_dict:
                 return (sample,)

           return UNetaDOutput(sample=__SCREAMING_SNAKE_CASE  )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      ={
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CLIPSegModel",
        "CLIPSegPreTrainedModel",
        "CLIPSegTextModel",
        "CLIPSegVisionModel",
        "CLIPSegForImageSegmentation",
    ]

if TYPE_CHECKING:
    from .configuration_clipseg import (
        CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPSegConfig,
        CLIPSegTextConfig,
        CLIPSegVisionConfig,
    )
    from .processing_clipseg import CLIPSegProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clipseg import (
            CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPSegForImageSegmentation,
            CLIPSegModel,
            CLIPSegPreTrainedModel,
            CLIPSegTextModel,
            CLIPSegVisionModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


