





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     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		torch.FloatTensor






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     UpperCAmelCase				):


   @register_to_config
   def __init__(   self      :    List[Any]      ,					a      :    int = 65_536      ,					a      :    Optional[int] = None      ,					a      :    int = 2      ,					a      :    int = 2      ,					a      :    int = 0      ,					a      :    str = "fourier"      ,					a      :    bool = True      ,					a      :    bool = False      ,					a      :    float = 0.0      ,					a      :    Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,					a      :    Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,					a      :    Tuple[str] = "UNetMidBlock1D"      ,					a      :    str = None      ,					a      :    Tuple[int] = (32, 32, 64)      ,					a      :    str = None      ,					a      :    int = 8      ,					a      :    int = 1      ,					a      :    bool = False      ,					)->       Optional[Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  sample_size

     # time
     if time_embedding_type == "fourier":
       lowercase__           =  GaussianFourierProjection(
           embedding_size=8      ,					set_W_to_weight=a      ,					log=a      ,					flip_sin_to_cos=a	)
       lowercase__           =  2 * block_out_channels[0]
     elif time_embedding_type == "positional":
       lowercase__           =  Timesteps(
           block_out_channels[0]      ,					flip_sin_to_cos=a      ,					downscale_freq_shift=a	)
       lowercase__           =  block_out_channels[0]

     if use_timestep_embedding:
       lowercase__           =  block_out_channels[0] * 4
       lowercase__           =  TimestepEmbedding(
           in_channels=a      ,					time_embed_dim=a      ,					act_fn=a      ,					out_dim=block_out_channels[0]      ,					)

     lowercase__           =  nn.ModuleList([]	)
     lowercase__           =  None
     lowercase__           =  nn.ModuleList([]	)
     lowercase__           =  None

     # down
     lowercase__           =  in_channels
     for i, down_block_type in enumerate(a	):
       lowercase__           =  output_channel
       lowercase__           =  block_out_channels[i]

       if i == 0:
         input_channel += extra_in_channels

       lowercase__           =  i == len(a	) - 1

       lowercase__           =  get_down_block(
           a      ,					num_layers=a      ,					in_channels=a      ,					out_channels=a      ,					temb_channels=block_out_channels[0]      ,					add_downsample=not is_final_block or downsample_each_block      ,					)
       self.down_blocks.append(a	)

     # mid
     lowercase__           =  get_mid_block(
         a      ,					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=a      ,					add_downsample=a      ,					)

     # up
     lowercase__           =  list(reversed(a	)	)
     lowercase__           =  reversed_block_out_channels[0]
     if out_block_type is None:
       lowercase__           =  out_channels
     else:
       lowercase__           =  block_out_channels[0]

     for i, up_block_type in enumerate(a	):
       lowercase__           =  output_channel
       lowercase__           =  (
           reversed_block_out_channels[i + 1] if i < len(a	) - 1 else final_upsample_channels
       )

       lowercase__           =  i == len(a	) - 1

       lowercase__           =  get_up_block(
           a      ,					num_layers=a      ,					in_channels=a      ,					out_channels=a      ,					temb_channels=block_out_channels[0]      ,					add_upsample=not is_final_block      ,					)
       self.up_blocks.append(a	)
       lowercase__           =  output_channel

     # out
     lowercase__           =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,					32	)
     lowercase__           =  get_out_block(
         out_block_type=a      ,					num_groups_out=a      ,					embed_dim=block_out_channels[0]      ,					out_channels=a      ,					act_fn=a      ,					fc_dim=block_out_channels[-1] // 4      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    torch.FloatTensor      ,					a      :    Union[torch.Tensor, float, int]      ,					a      :    bool = True      ,					)->       Union[UNetaDOutput, Tuple]:





     """simple docstring"""







     lowercase__           =  timestep
     if not torch.is_tensor(a	):
       lowercase__           =  torch.tensor([timesteps]      ,					dtype=torch.long      ,					device=sample.device	)
     elif torch.is_tensor(a	) and len(timesteps.shape	) == 0:
       lowercase__           =  timesteps[None].to(sample.device	)

     lowercase__           =  self.time_proj(a	)
     if self.config.use_timestep_embedding:
       lowercase__           =  self.time_mlp(a	)
     else:
       lowercase__           =  timestep_embed[..., None]
       lowercase__           =  timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
       lowercase__           =  timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

     # 2. down
     lowercase__           =  ()
     for downsample_block in self.down_blocks:
       lowercase__      ,   lowercase__           =  downsample_block(hidden_states=a      ,					temb=a	)
       down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
       lowercase__           =  self.mid_block(a      ,					a	)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks	):
       lowercase__           =  down_block_res_samples[-1:]
       lowercase__           =  down_block_res_samples[:-1]
       lowercase__           =  upsample_block(a      ,					res_hidden_states_tuple=a      ,					temb=a	)

     # 5. post-process
     if self.out_block:
       lowercase__           =  self.out_block(a      ,					a	)

     if not return_dict:
       return (sample,)

     return UNetaDOutput(sample=a	)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_          =		{
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		["""EfficientFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EfficientFormerForImageClassification""",
          """EfficientFormerForImageClassificationWithTeacher""",
          """EfficientFormerModel""",
          """EfficientFormerPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_          =		[
          """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEfficientFormerForImageClassification""",
          """TFEfficientFormerForImageClassificationWithTeacher""",
          """TFEfficientFormerModel""",
          """TFEfficientFormerPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientformer import EfficientFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientformer import (
                EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientFormerForImageClassification,
                EfficientFormerForImageClassificationWithTeacher,
                EfficientFormerModel,
                EfficientFormerPreTrainedModel,
            )
      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_efficientformer import (
                TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
                TFEfficientFormerModel,
                TFEfficientFormerPreTrainedModel,
            )

else:
      import sys

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


