






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      (		lowercase_   ):



				"""simple docstring"""



				_lowercase					: str	     =	42


class  _lowerCAmelCase      (		lowercase_  ,   lowercase_   ):



				"""simple docstring"""



				@register_to_config
				def __init__(   self     :     Union[str, Any]      ,		UpperCamelCase__     :     Optional[Any] = 6_5_5_3_6      ,		UpperCamelCase__     :     Tuple = None      ,		UpperCamelCase__     :     str = 2      ,		UpperCamelCase__     :     int = 2      ,		UpperCamelCase__     :     Optional[Any] = 0      ,		UpperCamelCase__     :     Union[str, Any] = "fourier"      ,		UpperCamelCase__     :     str = True      ,		UpperCamelCase__     :     Optional[Any] = False      ,		UpperCamelCase__     :     List[Any] = 0.0      ,		UpperCamelCase__     :     Tuple = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,		UpperCamelCase__     :     Dict = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,		UpperCamelCase__     :     Any = "UNetMidBlock1D"      ,		UpperCamelCase__     :     str = None      ,		UpperCamelCase__     :     Tuple = (3_2, 3_2, 6_4)      ,		UpperCamelCase__     :     str = None      ,		UpperCamelCase__     :     Dict = 8      ,		UpperCamelCase__     :     Tuple = 1      ,		UpperCamelCase__     :     Optional[int] = False      ,		):






											'''simple docstring'''







											super().__init__()
											snake_case__							=							sample_size

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

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

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

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

																		if i == 0:
																									input_channel += extra_in_channels

																		snake_case__							=							i == len(UpperCamelCase__) - 1

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

											# mid
											snake_case__							=							get_mid_block(
											    UpperCamelCase__      ,		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=UpperCamelCase__      ,		add_downsample=UpperCamelCase__      ,		)

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

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

																		snake_case__							=							i == len(UpperCamelCase__) - 1

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

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



				def        __magic_name__				(   self     :     Tuple      ,		UpperCamelCase__     :     Optional[Any]      ,		UpperCamelCase__     :     List[Any]      ,		UpperCamelCase__     :     List[Any] = True      ,		):






											'''simple docstring'''







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

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

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

											# 3. mid
											if self.mid_block:
																		snake_case__							=							self.mid_block(UpperCamelCase__      ,		UpperCamelCase__)

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

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

											if not return_dict:
																		return (sample,)

											return UNetaDOutput(sample=UpperCamelCase__)




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

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

