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 UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    torch.FloatTensor






class 				_SCREAMING_SNAKE_CASE( snake_case__							,   snake_case__						):
   @register_to_config
   def __init__(     self				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = 3				,  lowerCAmelCase_ = 3				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = "positional"				,  lowerCAmelCase_ = 0				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")				,  lowerCAmelCase_ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")				,  lowerCAmelCase_ = (2_24, 4_48, 6_72, 8_96)				,  lowerCAmelCase_ = 2				,  lowerCAmelCase_ = 1				,  lowerCAmelCase_ = 1				,  lowerCAmelCase_ = "conv"				,  lowerCAmelCase_ = "conv"				,  lowerCAmelCase_ = "silu"				,  lowerCAmelCase_ = 8				,  lowerCAmelCase_ = 32				,  lowerCAmelCase_ = 1e-5				,  lowerCAmelCase_ = "default"				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  )						->Any:
        super().__init__()

        a	:     Dict						      =    sample_size
        a	:     Dict						      =    block_out_channels[0] * 4

        # Check inputs
        if len(lowerCAmelCase_     ) != len(lowerCAmelCase_     ):
             raise ValueError(
                 F"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""     )

        if len(lowerCAmelCase_     ) != len(lowerCAmelCase_     ):
             raise ValueError(
                 F"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""     )

        # input
        a	:     List[Any]						      =    nn.Convad(lowerCAmelCase_				,  block_out_channels[0]				,  kernel_size=3				,  padding=(1, 1)     )

        # time
        if time_embedding_type == "fourier":
             a	:     Any						      =    GaussianFourierProjection(embedding_size=block_out_channels[0]				,  scale=16     )
             a	:     List[str]						      =    2 * block_out_channels[0]
        elif time_embedding_type == "positional":
             a	:     Dict						      =    Timesteps(block_out_channels[0]				,  lowerCAmelCase_				,  lowerCAmelCase_     )
             a	:     int						      =    block_out_channels[0]

        a	:     List[Any]						      =    TimestepEmbedding(lowerCAmelCase_				,  lowerCAmelCase_     )

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
             a	:     Any						      =    nn.Embedding(lowerCAmelCase_				,  lowerCAmelCase_     )
        elif class_embed_type == "timestep":
             a	:     str						      =    TimestepEmbedding(lowerCAmelCase_				,  lowerCAmelCase_     )
        elif class_embed_type == "identity":
             a	:     Optional[Any]						      =    nn.Identity(lowerCAmelCase_				,  lowerCAmelCase_     )
        else:
             a	:     List[str]						      =    None

        a	:     Optional[int]						      =    nn.ModuleList([]     )
        a	:     Dict						      =    None
        a	:     Dict						      =    nn.ModuleList([]     )

        # down
        a	:     Tuple						      =    block_out_channels[0]
        for i, down_block_type in enumerate(lowerCAmelCase_     ):
             a	:     Tuple						      =    output_channel
             a	:     Dict						      =    block_out_channels[i]
             a	:     int						      =    i == len(lowerCAmelCase_     ) - 1

             a	:     Any						      =    get_down_block(
                 lowerCAmelCase_				,  num_layers=lowerCAmelCase_				,  in_channels=lowerCAmelCase_				,  out_channels=lowerCAmelCase_				,  temb_channels=lowerCAmelCase_				,  add_downsample=not is_final_block				,  resnet_eps=lowerCAmelCase_				,  resnet_act_fn=lowerCAmelCase_				,  resnet_groups=lowerCAmelCase_				,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel				,  downsample_padding=lowerCAmelCase_				,  resnet_time_scale_shift=lowerCAmelCase_				,  downsample_type=lowerCAmelCase_				,  )
             self.down_blocks.append(lowerCAmelCase_     )

        # mid
        a	:     Optional[int]						      =    UNetMidBlockaD(
            in_channels=block_out_channels[-1]				,  temb_channels=lowerCAmelCase_				,  resnet_eps=lowerCAmelCase_				,  resnet_act_fn=lowerCAmelCase_				,  output_scale_factor=lowerCAmelCase_				,  resnet_time_scale_shift=lowerCAmelCase_				,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]				,  resnet_groups=lowerCAmelCase_				,  add_attention=lowerCAmelCase_				,  )

        # up
        a	:     Dict						      =    list(reversed(lowerCAmelCase_     )     )
        a	:     Dict						      =    reversed_block_out_channels[0]
        for i, up_block_type in enumerate(lowerCAmelCase_     ):
             a	:     Optional[int]						      =    output_channel
             a	:     int						      =    reversed_block_out_channels[i]
             a	:     Optional[int]						      =    reversed_block_out_channels[min(i + 1				,  len(lowerCAmelCase_     ) - 1     )]

             a	:     Union[str, Any]						      =    i == len(lowerCAmelCase_     ) - 1

             a	:     Any						      =    get_up_block(
                 lowerCAmelCase_				,  num_layers=layers_per_block + 1				,  in_channels=lowerCAmelCase_				,  out_channels=lowerCAmelCase_				,  prev_output_channel=lowerCAmelCase_				,  temb_channels=lowerCAmelCase_				,  add_upsample=not is_final_block				,  resnet_eps=lowerCAmelCase_				,  resnet_act_fn=lowerCAmelCase_				,  resnet_groups=lowerCAmelCase_				,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel				,  resnet_time_scale_shift=lowerCAmelCase_				,  upsample_type=lowerCAmelCase_				,  )
             self.up_blocks.append(lowerCAmelCase_     )
             a	:     Optional[int]						      =    output_channel

        # out
        a	:     int						      =    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,  32     )
        a	:     int						      =    nn.GroupNorm(num_channels=block_out_channels[0]				,  num_groups=lowerCAmelCase_				,  eps=lowerCAmelCase_     )
        a	:     Optional[Any]						      =    nn.SiLU()
        a	:     List[str]						      =    nn.Convad(block_out_channels[0]				,  lowerCAmelCase_				,  kernel_size=3				,  padding=1     )


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = True				,  )						->Union[UNetaDOutput, Tuple]:
        # 0. center input if necessary
        if self.config.center_input_sample:
             a	:     Any						      =    2 * sample - 1.0

        # 1. time
        a	:     List[Any]						      =    timestep
        if not torch.is_tensor(lowerCAmelCase_     ):
             a	:     int						      =    torch.tensor([timesteps]				,  dtype=torch.long				,  device=sample.device     )
        elif torch.is_tensor(lowerCAmelCase_     ) and len(timesteps.shape     ) == 0:
             a	:     Dict						      =    timesteps[None].to(sample.device     )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        a	:     Any						      =    timesteps * torch.ones(sample.shape[0]				,  dtype=timesteps.dtype				,  device=timesteps.device     )

        a	:     Union[str, Any]						      =    self.time_proj(lowerCAmelCase_     )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        a	:     List[str]						      =    t_emb.to(dtype=self.dtype     )
        a	:     Union[str, Any]						      =    self.time_embedding(lowerCAmelCase_     )

        if self.class_embedding is not None:
             if class_labels is None:
                  raise ValueError('''class_labels should be provided when doing class conditioning'''     )

             if self.config.class_embed_type == "timestep":
                  a	:     List[str]						      =    self.time_proj(lowerCAmelCase_     )

             a	:     Any						      =    self.class_embedding(lowerCAmelCase_     ).to(dtype=self.dtype     )
             a	:     Dict						      =    emb + class_emb

        # 2. pre-process
        a	:     Optional[int]						      =    sample
        a	:     Optional[Any]						      =    self.conv_in(lowerCAmelCase_     )

        # 3. down
        a	:     Dict						      =    (sample,)
        for downsample_block in self.down_blocks:
             if hasattr(lowerCAmelCase_				,  '''skip_conv'''     ):
                  a      ,				a      ,				a	:     int						      =    downsample_block(
                      hidden_states=lowerCAmelCase_				,  temb=lowerCAmelCase_				,  skip_sample=lowerCAmelCase_     )
             else:
                  a      ,				a	:     List[Any]						      =    downsample_block(hidden_states=lowerCAmelCase_				,  temb=lowerCAmelCase_     )

             down_block_res_samples += res_samples

        # 4. mid
        a	:     Optional[int]						      =    self.mid_block(lowerCAmelCase_				,  lowerCAmelCase_     )

        # 5. up
        a	:     Any						      =    None
        for upsample_block in self.up_blocks:
             a	:     Optional[int]						      =    down_block_res_samples[-len(upsample_block.resnets     ) :]
             a	:     Union[str, Any]						      =    down_block_res_samples[: -len(upsample_block.resnets     )]

             if hasattr(lowerCAmelCase_				,  '''skip_conv'''     ):
                  a      ,				a	:     str						      =    upsample_block(lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )
             else:
                  a	:     Optional[int]						      =    upsample_block(lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )

        # 6. post-process
        a	:     Optional[Any]						      =    self.conv_norm_out(lowerCAmelCase_     )
        a	:     Optional[Any]						      =    self.conv_act(lowerCAmelCase_     )
        a	:     Any						      =    self.conv_out(lowerCAmelCase_     )

        if skip_sample is not None:
             sample += skip_sample

        if self.config.time_embedding_type == "fourier":
             a	:     Union[str, Any]						      =    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]     )))     )
             a	:     Union[str, Any]						      =    sample / timesteps

        if not return_dict:
             return (sample,)

        return UNetaDOutput(sample=lowerCAmelCase_     )
import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (inputs > threshold).type(inputs.type()     )
        if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             a	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Optional[Any]						      =    0
        a	:     List[Any]						      =    1
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Tuple						      =    0
        a	:     List[str]						      =    1
        return mask