




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  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       torch.FloatTensor







class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      @register_to_config
      def __init__(   self    :       Optional[Any]       ,				snake_case    :       Optional[Union[int, Tuple[int, int]]] = None       ,				snake_case    :       int = 3       ,				snake_case    :       int = 3       ,				snake_case    :       bool = False       ,				snake_case    :       str = "positional"       ,				snake_case    :       int = 0       ,				snake_case    :       bool = True       ,				snake_case    :       Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")       ,				snake_case    :       Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")       ,				snake_case    :       Tuple[int] = (224, 448, 672, 896)       ,				snake_case    :       int = 2       ,				snake_case    :       float = 1       ,				snake_case    :       int = 1       ,				snake_case    :       str = "conv"       ,				snake_case    :       str = "conv"       ,				snake_case    :       str = "silu"       ,				snake_case    :       Optional[int] = 8       ,				snake_case    :       int = 32       ,				snake_case    :       float = 1E-5       ,				snake_case    :       str = "default"       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[str] = None       ,				snake_case    :       Optional[int] = None       ,				):
          super().__init__()

          __UpperCamelCase     						=  sample_size
          __UpperCamelCase     						=  block_out_channels[0] * 4

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

          # time
          if time_embedding_type == "fourier":
              __UpperCamelCase     						=  GaussianFourierProjection(embedding_size=block_out_channels[0]       ,				scale=16 )
              __UpperCamelCase     						=  2 * block_out_channels[0]
          elif time_embedding_type == "positional":
              __UpperCamelCase     						=  Timesteps(block_out_channels[0]       ,				snake_case       ,				snake_case )
              __UpperCamelCase     						=  block_out_channels[0]

          __UpperCamelCase     						=  TimestepEmbedding(snake_case       ,				snake_case )

          # class embedding
          if class_embed_type is None and num_class_embeds is not None:
              __UpperCamelCase     						=  nn.Embedding(snake_case       ,				snake_case )
          elif class_embed_type == "timestep":
              __UpperCamelCase     						=  TimestepEmbedding(snake_case       ,				snake_case )
          elif class_embed_type == "identity":
              __UpperCamelCase     						=  nn.Identity(snake_case       ,				snake_case )
          else:
              __UpperCamelCase     						=  None

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

          # down
          __UpperCamelCase     						=  block_out_channels[0]
          for i, down_block_type in enumerate(snake_case ):
              __UpperCamelCase     						=  output_channel
              __UpperCamelCase     						=  block_out_channels[i]
              __UpperCamelCase     						=  i == len(snake_case ) - 1

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

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

          # up
          __UpperCamelCase     						=  list(reversed(snake_case ) )
          __UpperCamelCase     						=  reversed_block_out_channels[0]
          for i, up_block_type in enumerate(snake_case ):
              __UpperCamelCase     						=  output_channel
              __UpperCamelCase     						=  reversed_block_out_channels[i]
              __UpperCamelCase     						=  reversed_block_out_channels[min(i + 1       ,				len(snake_case ) - 1 )]

              __UpperCamelCase     						=  i == len(snake_case ) - 1

              __UpperCamelCase     						=  get_up_block(
                  snake_case       ,				num_layers=layers_per_block + 1       ,				in_channels=snake_case       ,				out_channels=snake_case       ,				prev_output_channel=snake_case       ,				temb_channels=snake_case       ,				add_upsample=not is_final_block       ,				resnet_eps=snake_case       ,				resnet_act_fn=snake_case       ,				resnet_groups=snake_case       ,				attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,				resnet_time_scale_shift=snake_case       ,				upsample_type=snake_case       ,				)
              self.up_blocks.append(snake_case )
              __UpperCamelCase     						=  output_channel

          # out
          __UpperCamelCase     						=  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,				32 )
          __UpperCamelCase     						=  nn.GroupNorm(num_channels=block_out_channels[0]       ,				num_groups=snake_case       ,				eps=snake_case )
          __UpperCamelCase     						=  nn.SiLU()
          __UpperCamelCase     						=  nn.Convad(block_out_channels[0]       ,				snake_case       ,				kernel_size=3       ,				padding=1 )







      def 					snake_case		(   self    :       Any       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Union[torch.Tensor, float, int]       ,				snake_case    :       Optional[torch.Tensor] = None       ,				snake_case    :       bool = True       ,				):
          # 0. center input if necessary
          if self.config.center_input_sample:
              __UpperCamelCase     						=  2 * sample - 1.0

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

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

          __UpperCamelCase     						=  self.time_proj(snake_case )

          # 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.
          __UpperCamelCase     						=  t_emb.to(dtype=self.dtype )
          __UpperCamelCase     						=  self.time_embedding(snake_case )

          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":
                  __UpperCamelCase     						=  self.time_proj(snake_case )

              __UpperCamelCase     						=  self.class_embedding(snake_case ).to(dtype=self.dtype )
              __UpperCamelCase     						=  emb + class_emb

          # 2. pre-process
          __UpperCamelCase     						=  sample
          __UpperCamelCase     						=  self.conv_in(snake_case )

          # 3. down
          __UpperCamelCase     						=  (sample,)
          for downsample_block in self.down_blocks:
              if hasattr(snake_case       ,				'''skip_conv''' ):
                  __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  downsample_block(
                      hidden_states=snake_case       ,				temb=snake_case       ,				skip_sample=snake_case )
              else:
                  __UpperCamelCase						,					__UpperCamelCase     						=  downsample_block(hidden_states=snake_case       ,				temb=snake_case )

              down_block_res_samples += res_samples

          # 4. mid
          __UpperCamelCase     						=  self.mid_block(snake_case       ,				snake_case )

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

              if hasattr(snake_case       ,				'''skip_conv''' ):
                  __UpperCamelCase						,					__UpperCamelCase     						=  upsample_block(snake_case       ,				snake_case       ,				snake_case       ,				snake_case )
              else:
                  __UpperCamelCase     						=  upsample_block(snake_case       ,				snake_case       ,				snake_case )

        # 6. post-process
          __UpperCamelCase     						=  self.conv_norm_out(snake_case )
          __UpperCamelCase     						=  self.conv_act(snake_case )
          __UpperCamelCase     						=  self.conv_out(snake_case )

          if skip_sample is not None:
              sample += skip_sample

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

          if not return_dict:
              return (sample,)

          return UNetaDOutput(sample=snake_case )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )


