


'''simple docstring'''



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 		_snake_case					(     a__	):
					lowerCAmelCase      :torch.FloatTensor


class 		_snake_case					(     a__       ,   a__	):




					@register_to_config
					def __init__(      self							,							_lowerCamelCase = 6_5536							,							_lowerCamelCase = None							,							_lowerCamelCase = 2							,							_lowerCamelCase = 2							,							_lowerCamelCase = 0							,							_lowerCamelCase = "fourier"							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = 0.0							,							_lowerCamelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")							,							_lowerCamelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")							,							_lowerCamelCase = "UNetMidBlock1D"							,							_lowerCamelCase = None							,							_lowerCamelCase = (32, 32, 64)							,							_lowerCamelCase = None							,							_lowerCamelCase = 8							,							_lowerCamelCase = 1							,							_lowerCamelCase = False							,							):
								super().__init__()
								UpperCAmelCase__  :					Tuple        =     sample_size

								# time
								if time_embedding_type == "fourier":
											UpperCAmelCase__  :					int        =     GaussianFourierProjection(
											    embedding_size=8							,							set_W_to_weight=_lowerCamelCase							,							log=_lowerCamelCase							,							flip_sin_to_cos=_lowerCamelCase)
											UpperCAmelCase__  :					Dict        =     2 * block_out_channels[0]
								elif time_embedding_type == "positional":
											UpperCAmelCase__  :					List[str]        =     Timesteps(
											    block_out_channels[0]							,							flip_sin_to_cos=_lowerCamelCase							,							downscale_freq_shift=_lowerCamelCase)
											UpperCAmelCase__  :					List[Any]        =     block_out_channels[0]

								if use_timestep_embedding:
											UpperCAmelCase__  :					List[str]        =     block_out_channels[0] * 4
											UpperCAmelCase__  :					int        =     TimestepEmbedding(
											    in_channels=_lowerCamelCase							,							time_embed_dim=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							out_dim=block_out_channels[0]							,							)

								UpperCAmelCase__  :					List[str]        =     nn.ModuleList([])
								UpperCAmelCase__  :					Any        =     None
								UpperCAmelCase__  :					Union[str, Any]        =     nn.ModuleList([])
								UpperCAmelCase__  :					int        =     None

								# down
								UpperCAmelCase__  :					Optional[Any]        =     in_channels
								for i, down_block_type in enumerate(_lowerCamelCase):
											UpperCAmelCase__  :					int        =     output_channel
											UpperCAmelCase__  :					Tuple        =     block_out_channels[i]

											if i == 0:
														input_channel += extra_in_channels

											UpperCAmelCase__  :					Union[str, Any]        =     i == len(_lowerCamelCase) - 1

											UpperCAmelCase__  :					Union[str, Any]        =     get_down_block(
											    _lowerCamelCase							,							num_layers=_lowerCamelCase							,							in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							temb_channels=block_out_channels[0]							,							add_downsample=not is_final_block or downsample_each_block							,							)
											self.down_blocks.append(_lowerCamelCase)

								# mid
								UpperCAmelCase__  :					Dict        =     get_mid_block(
								    _lowerCamelCase							,							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=_lowerCamelCase							,							add_downsample=_lowerCamelCase							,							)

								# up
								UpperCAmelCase__  :					Dict        =     list(reversed(_lowerCamelCase))
								UpperCAmelCase__  :					Dict        =     reversed_block_out_channels[0]
								if out_block_type is None:
											UpperCAmelCase__  :					List[Any]        =     out_channels
								else:
											UpperCAmelCase__  :					Dict        =     block_out_channels[0]

								for i, up_block_type in enumerate(_lowerCamelCase):
											UpperCAmelCase__  :					Dict        =     output_channel
											UpperCAmelCase__  :					Any        =     (
											    reversed_block_out_channels[i + 1] if i < len(_lowerCamelCase) - 1 else final_upsample_channels
											)

											UpperCAmelCase__  :					Union[str, Any]        =     i == len(_lowerCamelCase) - 1

											UpperCAmelCase__  :					List[Any]        =     get_up_block(
											    _lowerCamelCase							,							num_layers=_lowerCamelCase							,							in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							temb_channels=block_out_channels[0]							,							add_upsample=not is_final_block							,							)
											self.up_blocks.append(_lowerCamelCase)
											UpperCAmelCase__  :					Optional[Any]        =     output_channel

								# out
								UpperCAmelCase__  :					List[Any]        =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4							,							32)
								UpperCAmelCase__  :					str        =     get_out_block(
								    out_block_type=_lowerCamelCase							,							num_groups_out=_lowerCamelCase							,							embed_dim=block_out_channels[0]							,							out_channels=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							fc_dim=block_out_channels[-1] // 4							,							)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = True							,							):
								UpperCAmelCase__  :					Optional[Any]        =     timestep
								if not torch.is_tensor(_lowerCamelCase):
											UpperCAmelCase__  :					List[Any]        =     torch.tensor([timesteps]							,							dtype=torch.long							,							device=sample.device)
								elif torch.is_tensor(_lowerCamelCase) and len(timesteps.shape) == 0:
											UpperCAmelCase__  :					Union[str, Any]        =     timesteps[None].to(sample.device)

								UpperCAmelCase__  :					List[Any]        =     self.time_proj(_lowerCamelCase)
								if self.config.use_timestep_embedding:
											UpperCAmelCase__  :					Optional[Any]        =     self.time_mlp(_lowerCamelCase)
								else:
											UpperCAmelCase__  :					List[str]        =     timestep_embed[..., None]
											UpperCAmelCase__  :					Optional[int]        =     timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
											UpperCAmelCase__  :					List[Any]        =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

								# 2. down
								UpperCAmelCase__  :					List[Any]        =     ()
								for downsample_block in self.down_blocks:
											UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     downsample_block(hidden_states=_lowerCamelCase							,							temb=_lowerCamelCase)
											down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
											UpperCAmelCase__  :					str        =     self.mid_block(_lowerCamelCase							,							_lowerCamelCase)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks):
											UpperCAmelCase__  :					Union[str, Any]        =     down_block_res_samples[-1:]
											UpperCAmelCase__  :					Optional[int]        =     down_block_res_samples[:-1]
											UpperCAmelCase__  :					Dict        =     upsample_block(_lowerCamelCase							,							res_hidden_states_tuple=_lowerCamelCase							,							temb=_lowerCamelCase)

								# 5. post-process
								if self.out_block:
											UpperCAmelCase__  :					Dict        =     self.out_block(_lowerCamelCase							,							_lowerCamelCase)

								if not return_dict:
											return (sample,)

								return UNetaDOutput(sample=_lowerCamelCase)



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

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