





'''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  _A (					lowercase_		):
		lowercase__:   List[Any]				     =   42





class  _A (					lowercase_						,					lowercase_		):

		@register_to_config
		def __init__(   self					:   int  ,      __magic_name__					:   int = 6_55_36  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   int = 0  ,      __magic_name__					:   str = "fourier"  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   bool = False  ,      __magic_name__					:   float = 0.0  ,      __magic_name__					:   Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")  ,      __magic_name__					:   Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")  ,      __magic_name__					:   Tuple[str] = "UNetMidBlock1D"  ,      __magic_name__					:   str = None  ,      __magic_name__					:   Tuple[int] = (32, 32, 64)  ,      __magic_name__					:   str = None  ,      __magic_name__					:   int = 8  ,      __magic_name__					:   int = 1  ,      __magic_name__					:   bool = False  ,      )					->    str:


					"""simple docstring"""

					super().__init__()
					__snake_case					:      Optional[int]							      =     sample_size

					# time
					if time_embedding_type == "fourier":
								__snake_case					:      Union[str, Any]							      =     GaussianFourierProjection(
								    embedding_size=8  ,      set_W_to_weight=lowerCamelCase_  ,      log=lowerCamelCase_  ,      flip_sin_to_cos=lowerCamelCase_			)
								__snake_case					:      int							      =     2 * block_out_channels[0]
					elif time_embedding_type == "positional":
								__snake_case					:      Union[str, Any]							      =     Timesteps(
								    block_out_channels[0]  ,      flip_sin_to_cos=lowerCamelCase_  ,      downscale_freq_shift=lowerCamelCase_			)
								__snake_case					:      Optional[int]							      =     block_out_channels[0]

					if use_timestep_embedding:
								__snake_case					:      Optional[int]							      =     block_out_channels[0] * 4
								__snake_case					:      Optional[int]							      =     TimestepEmbedding(
								    in_channels=lowerCamelCase_  ,      time_embed_dim=lowerCamelCase_  ,      act_fn=lowerCamelCase_  ,      out_dim=block_out_channels[0]  ,      )

					__snake_case					:      Optional[int]							      =     nn.ModuleList([]			)
					__snake_case					:      List[str]							      =     None
					__snake_case					:      Optional[int]							      =     nn.ModuleList([]			)
					__snake_case					:      Tuple							      =     None

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

								if i == 0:
											input_channel += extra_in_channels

								__snake_case					:      Tuple							      =     i == len(lowerCamelCase_			) - 1

								__snake_case					:      List[str]							      =     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
					__snake_case					:      Tuple							      =     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
					__snake_case					:      int							      =     list(reversed(lowerCamelCase_			)			)
					__snake_case					:      Optional[Any]							      =     reversed_block_out_channels[0]
					if out_block_type is None:
								__snake_case					:      Tuple							      =     out_channels
					else:
								__snake_case					:      List[str]							      =     block_out_channels[0]

					for i, up_block_type in enumerate(lowerCamelCase_			):
								__snake_case					:      int							      =     output_channel
								__snake_case					:      Optional[Any]							      =     (
								    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase_			) - 1 else final_upsample_channels
								)

								__snake_case					:      Dict							      =     i == len(lowerCamelCase_			) - 1

								__snake_case					:      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_			)
								__snake_case					:      Optional[int]							      =     output_channel

					# out
					__snake_case					:      Optional[int]							      =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,      32			)
					__snake_case					:      List[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 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   torch.FloatTensor  ,      __magic_name__					:   Union[torch.Tensor, float, int]  ,      __magic_name__					:   bool = True  ,      )					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     timestep
					if not torch.is_tensor(lowerCamelCase_			):
								__snake_case					:      Dict							      =     torch.tensor([timesteps]  ,      dtype=torch.long  ,      device=sample.device			)
					elif torch.is_tensor(lowerCamelCase_			) and len(timesteps.shape			) == 0:
								__snake_case					:      List[str]							      =     timesteps[None].to(sample.device			)

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

					# 2. down
					__snake_case					:      int							      =     ()
					for downsample_block in self.down_blocks:
								__snake_case					:      Any							      =     downsample_block(hidden_states=lowerCamelCase_  ,      temb=lowerCamelCase_			)
								down_block_res_samples += res_samples

					# 3. mid
					if self.mid_block:
								__snake_case					:      Optional[int]							      =     self.mid_block(lowerCamelCase_  ,      lowerCamelCase_			)

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

					# 5. post-process
					if self.out_block:
								__snake_case					:      Any							      =     self.out_block(lowerCamelCase_  ,      lowerCamelCase_			)

					if not return_dict:
								return (sample,)

					return UNetaDOutput(sample=lowerCamelCase_			)









'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''fnet'''


					def __init__(						self				:			Any      ,						lowerCamelCase_				:			List[str]=3_20_00      ,						lowerCamelCase_				:			List[Any]=7_68      ,						lowerCamelCase_				:			Union[str, Any]=12      ,						lowerCamelCase_				:			Optional[Any]=30_72      ,						lowerCamelCase_				:			Optional[Any]="gelu_new"      ,						lowerCamelCase_				:			Any=0.1      ,						lowerCamelCase_				:			Union[str, Any]=5_12      ,						lowerCamelCase_				:			Optional[int]=4      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			str=1e-12      ,						lowerCamelCase_				:			Any=False      ,						lowerCamelCase_				:			str=5_12      ,						lowerCamelCase_				:			str=3      ,						lowerCamelCase_				:			int=1      ,						lowerCamelCase_				:			Optional[Any]=2      ,						**lowerCamelCase_				:			Optional[int]      ,						):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_      ,						bos_token_id=lowerCamelCase_      ,						eos_token_id=lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			vocab_size
							SCREAMING_SNAKE_CASE :				Dict		=			max_position_embeddings
							SCREAMING_SNAKE_CASE :				str		=			hidden_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			num_hidden_layers
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			intermediate_size
							SCREAMING_SNAKE_CASE :				int		=			hidden_act
							SCREAMING_SNAKE_CASE :				Dict		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			initializer_range
							SCREAMING_SNAKE_CASE :				List[Any]		=			type_vocab_size
							SCREAMING_SNAKE_CASE :				Any		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				int		=			use_tpu_fourier_optimizations
							SCREAMING_SNAKE_CASE :				List[Any]		=			tpu_short_seq_length



