

"""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__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						42







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):







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





								'''simple docstring'''



								super().__init__()
								UpperCAmelCase			:   Tuple      =				sample_size

								# time
								if time_embedding_type == "fourier":
													UpperCAmelCase			:   int      =				GaussianFourierProjection(
													    embedding_size=8			,  set_W_to_weight=lowercase			,  log=lowercase			,  flip_sin_to_cos=lowercase  )
													UpperCAmelCase			:   int      =				2 * block_out_channels[0]
								elif time_embedding_type == "positional":
													UpperCAmelCase			:   Tuple      =				Timesteps(
													    block_out_channels[0]			,  flip_sin_to_cos=lowercase			,  downscale_freq_shift=lowercase  )
													UpperCAmelCase			:   Dict      =				block_out_channels[0]

								if use_timestep_embedding:
													UpperCAmelCase			:   Optional[Any]      =				block_out_channels[0] * 4
													UpperCAmelCase			:   Optional[Any]      =				TimestepEmbedding(
													    in_channels=lowercase			,  time_embed_dim=lowercase			,  act_fn=lowercase			,  out_dim=block_out_channels[0]			,  )

								UpperCAmelCase			:   Dict      =				nn.ModuleList([]  )
								UpperCAmelCase			:   str      =				None
								UpperCAmelCase			:   Any      =				nn.ModuleList([]  )
								UpperCAmelCase			:   Tuple      =				None

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

													if i == 0:
																		input_channel += extra_in_channels

													UpperCAmelCase			:   int      =				i == len(lowercase  ) - 1

													UpperCAmelCase			:   Dict      =				get_down_block(
													    lowercase			,  num_layers=lowercase			,  in_channels=lowercase			,  out_channels=lowercase			,  temb_channels=block_out_channels[0]			,  add_downsample=not is_final_block or downsample_each_block			,  )
													self.down_blocks.append(lowercase  )

								# mid
								UpperCAmelCase			:   Optional[Any]      =				get_mid_block(
								    lowercase			,  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=lowercase			,  add_downsample=lowercase			,  )

								# up
								UpperCAmelCase			:   Tuple      =				list(reversed(lowercase  )  )
								UpperCAmelCase			:   str      =				reversed_block_out_channels[0]
								if out_block_type is None:
													UpperCAmelCase			:   Optional[int]      =				out_channels
								else:
													UpperCAmelCase			:   int      =				block_out_channels[0]

								for i, up_block_type in enumerate(lowercase  ):
													UpperCAmelCase			:   Any      =				output_channel
													UpperCAmelCase			:   str      =				(
													    reversed_block_out_channels[i + 1] if i < len(lowercase  ) - 1 else final_upsample_channels
													)

													UpperCAmelCase			:   Dict      =				i == len(lowercase  ) - 1

													UpperCAmelCase			:   Tuple      =				get_up_block(
													    lowercase			,  num_layers=lowercase			,  in_channels=lowercase			,  out_channels=lowercase			,  temb_channels=block_out_channels[0]			,  add_upsample=not is_final_block			,  )
													self.up_blocks.append(lowercase  )
													UpperCAmelCase			:   List[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			:   Any      =				get_out_block(
								    out_block_type=lowercase			,  num_groups_out=lowercase			,  embed_dim=block_out_channels[0]			,  out_channels=lowercase			,  act_fn=lowercase			,  fc_dim=block_out_channels[-1] // 4			,  )







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: torch.FloatTensor			,  lowercase			: Union[torch.Tensor, float, int]			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				timestep
								if not torch.is_tensor(lowercase  ):
													UpperCAmelCase			:   Optional[int]      =				torch.tensor([timesteps]			,  dtype=torch.long			,  device=sample.device  )
								elif torch.is_tensor(lowercase  ) and len(timesteps.shape  ) == 0:
													UpperCAmelCase			:   Optional[Any]      =				timesteps[None].to(sample.device  )

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

								# 2. down
								UpperCAmelCase			:   Union[str, Any]      =				()
								for downsample_block in self.down_blocks:
													UpperCAmelCase			:   Tuple      =				downsample_block(hidden_states=lowercase			,  temb=lowercase  )
													down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
													UpperCAmelCase			:   Optional[Any]      =				self.mid_block(lowercase			,  lowercase  )

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks  ):
													UpperCAmelCase			:   Any      =				down_block_res_samples[-1:]
													UpperCAmelCase			:   int      =				down_block_res_samples[:-1]
													UpperCAmelCase			:   List[Any]      =				upsample_block(lowercase			,  res_hidden_states_tuple=lowercase			,  temb=lowercase  )

								# 5. post-process
								if self.out_block:
													UpperCAmelCase			:   int      =				self.out_block(lowercase			,  lowercase  )

								if not return_dict:
													return (sample,)

								return UNetaDOutput(sample=lowercase  )





"""simple docstring"""






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


snake_case_   :   List[Any]        =       logging.get_logger(__name__)

snake_case_   :   List[Any]        =       {
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''rwkv'''
			SCREAMING_SNAKE_CASE__     				=						{'''max_position_embeddings''': '''context_length'''}







			def __init__(			self			: int			,  lowercase			: Optional[Any]=5_02_77			,  lowercase			: Union[str, Any]=10_24			,  lowercase			: Union[str, Any]=40_96			,  lowercase			: List[str]=32			,  lowercase			: str=None			,  lowercase			: Tuple=None			,  lowercase			: Dict=1E-5			,  lowercase			: Any=0			,  lowercase			: List[Any]=0			,  lowercase			: int=6			,  lowercase			: Dict=False			,  lowercase			: Dict=True			,  **lowercase			: Optional[Any]			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				vocab_size
								UpperCAmelCase			:   List[str]      =				context_length
								UpperCAmelCase			:   Optional[Any]      =				hidden_size
								UpperCAmelCase			:   List[Any]      =				num_hidden_layers
								UpperCAmelCase			:   Union[str, Any]      =				attention_hidden_size if attention_hidden_size is not None else hidden_size
								UpperCAmelCase			:   List[Any]      =				intermediate_size if intermediate_size is not None else 4 * hidden_size
								UpperCAmelCase			:   Tuple      =				layer_norm_epsilon
								UpperCAmelCase			:   Optional[int]      =				rescale_every
								UpperCAmelCase			:   Tuple      =				use_cache

								UpperCAmelCase			:   Union[str, Any]      =				bos_token_id
								UpperCAmelCase			:   Optional[int]      =				eos_token_id

								super().__init__(
								    tie_word_embeddings=lowercase			,  bos_token_id=lowercase			,  eos_token_id=lowercase			,  **lowercase  )


