





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 				_lowerCamelCase (    a    ):



      """simple docstring"""






      UpperCAmelCase_						:      torch.FloatTensor




class 				_lowerCamelCase (    a ,   a    ):



      """simple docstring"""






      @register_to_config
      def __init__( self				,						UpperCAmelCase = None				,						UpperCAmelCase = 3				,						UpperCAmelCase = 3				,						UpperCAmelCase = False				,						UpperCAmelCase = "positional"				,						UpperCAmelCase = 0				,						UpperCAmelCase = True				,						UpperCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")				,						UpperCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")				,						UpperCAmelCase = (224, 448, 672, 896)				,						UpperCAmelCase = 2				,						UpperCAmelCase = 1				,						UpperCAmelCase = 1				,						UpperCAmelCase = "conv"				,						UpperCAmelCase = "conv"				,						UpperCAmelCase = "silu"				,						UpperCAmelCase = 8				,						UpperCAmelCase = 32				,						UpperCAmelCase = 1E-5				,						UpperCAmelCase = "default"				,						UpperCAmelCase = True				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						)       ->   Any:






        '''simple docstring'''






        super().__init__()

        __snake_case   :				Optional[int]        =       sample_size
        __snake_case   :				List[str]        =       block_out_channels[0] * 4

        # Check inputs
        if len(UpperCAmelCase   ) != len(UpperCAmelCase   ):
          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(UpperCAmelCase   ) != len(UpperCAmelCase   ):
          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
        __snake_case   :				int        =       nn.Convad(UpperCAmelCase				,						block_out_channels[0]				,						kernel_size=3				,						padding=(1, 1)   )

        # time
        if time_embedding_type == "fourier":
          __snake_case   :				Optional[Any]        =       GaussianFourierProjection(embedding_size=block_out_channels[0]				,						scale=16   )
          __snake_case   :				str        =       2 * block_out_channels[0]
        elif time_embedding_type == "positional":
          __snake_case   :				Optional[Any]        =       Timesteps(block_out_channels[0]				,						UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case   :				str        =       block_out_channels[0]

        __snake_case   :				int        =       TimestepEmbedding(UpperCAmelCase				,						UpperCAmelCase   )

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

        __snake_case   :				Optional[Any]        =       nn.ModuleList([]   )
        __snake_case   :				Optional[int]        =       None
        __snake_case   :				Optional[Any]        =       nn.ModuleList([]   )

        # down
        __snake_case   :				Optional[int]        =       block_out_channels[0]
        for i, down_block_type in enumerate(UpperCAmelCase   ):
          __snake_case   :				Dict        =       output_channel
          __snake_case   :				Optional[int]        =       block_out_channels[i]
          __snake_case   :				List[str]        =       i == len(UpperCAmelCase   ) - 1

          __snake_case   :				Union[str, Any]        =       get_down_block(
              UpperCAmelCase				,						num_layers=UpperCAmelCase				,						in_channels=UpperCAmelCase				,						out_channels=UpperCAmelCase				,						temb_channels=UpperCAmelCase				,						add_downsample=not is_final_block				,						resnet_eps=UpperCAmelCase				,						resnet_act_fn=UpperCAmelCase				,						resnet_groups=UpperCAmelCase				,						attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel				,						downsample_padding=UpperCAmelCase				,						resnet_time_scale_shift=UpperCAmelCase				,						downsample_type=UpperCAmelCase				,						)
          self.down_blocks.append(UpperCAmelCase   )

        # mid
        __snake_case   :				Dict        =       UNetMidBlockaD(
            in_channels=block_out_channels[-1]				,						temb_channels=UpperCAmelCase				,						resnet_eps=UpperCAmelCase				,						resnet_act_fn=UpperCAmelCase				,						output_scale_factor=UpperCAmelCase				,						resnet_time_scale_shift=UpperCAmelCase				,						attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]				,						resnet_groups=UpperCAmelCase				,						add_attention=UpperCAmelCase				,						)

        # up
        __snake_case   :				Dict        =       list(reversed(UpperCAmelCase   )   )
        __snake_case   :				Optional[Any]        =       reversed_block_out_channels[0]
        for i, up_block_type in enumerate(UpperCAmelCase   ):
          __snake_case   :				Any        =       output_channel
          __snake_case   :				Tuple        =       reversed_block_out_channels[i]
          __snake_case   :				List[Any]        =       reversed_block_out_channels[min(i + 1				,						len(UpperCAmelCase   ) - 1   )]

          __snake_case   :				Union[str, Any]        =       i == len(UpperCAmelCase   ) - 1

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

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







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						)       ->   Union[UNetaDOutput, Tuple]:






        '''simple docstring'''






        if self.config.center_input_sample:
          __snake_case   :				int        =       2 * sample - 1.0

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

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

        __snake_case   :				List[str]        =       self.time_proj(UpperCAmelCase   )

        # 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.
        __snake_case   :				List[str]        =       t_emb.to(dtype=self.dtype   )
        __snake_case   :				Union[str, Any]        =       self.time_embedding(UpperCAmelCase   )

        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":
            __snake_case   :				str        =       self.time_proj(UpperCAmelCase   )

          __snake_case   :				Any        =       self.class_embedding(UpperCAmelCase   ).to(dtype=self.dtype   )
          __snake_case   :				List[str]        =       emb + class_emb

        # 2. pre-process
        __snake_case   :				Optional[int]        =       sample
        __snake_case   :				str        =       self.conv_in(UpperCAmelCase   )

        # 3. down
        __snake_case   :				List[Any]        =       (sample,)
        for downsample_block in self.down_blocks:
          if hasattr(UpperCAmelCase				,						"skip_conv"   ):
            __snake_case    ,   __snake_case    ,   __snake_case   :				Union[str, Any]        =       downsample_block(
                hidden_states=UpperCAmelCase				,						temb=UpperCAmelCase				,						skip_sample=UpperCAmelCase   )
          else:
            __snake_case    ,   __snake_case   :				Union[str, Any]        =       downsample_block(hidden_states=UpperCAmelCase				,						temb=UpperCAmelCase   )

          down_block_res_samples += res_samples

        # 4. mid
        __snake_case   :				Dict        =       self.mid_block(UpperCAmelCase				,						UpperCAmelCase   )

        # 5. up
        __snake_case   :				List[Any]        =       None
        for upsample_block in self.up_blocks:
          __snake_case   :				Dict        =       down_block_res_samples[-len(upsample_block.resnets   ) :]
          __snake_case   :				Dict        =       down_block_res_samples[: -len(upsample_block.resnets   )]

          if hasattr(UpperCAmelCase				,						"skip_conv"   ):
            __snake_case    ,   __snake_case   :				int        =       upsample_block(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )
          else:
            __snake_case   :				List[Any]        =       upsample_block(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

        # 6. post-process
        __snake_case   :				List[Any]        =       self.conv_norm_out(UpperCAmelCase   )
        __snake_case   :				Dict        =       self.conv_act(UpperCAmelCase   )
        __snake_case   :				Optional[int]        =       self.conv_out(UpperCAmelCase   )

        if skip_sample is not None:
          sample += skip_sample

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

        if not return_dict:
          return (sample,)

        return UNetaDOutput(sample=UpperCAmelCase   )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase       = {
    '''configuration_deta''': ['''DETA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetaConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase       = ['''DetaImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase       = [
         '''DETA_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DetaForObjectDetection''',
         '''DetaModel''',
         '''DetaPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_deta import DetaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deta import (
              DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
              DetaForObjectDetection,
              DetaModel,
              DetaPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase       = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

