

"""simple docstring"""






from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class       snake_case__  ( _UpperCAmelCase     ):







			def __init__(			self			: List[str]			,  lowercase			: Tuple			,  lowercase			: Any  ):





								'''simple docstring'''



								super().__init__()
								self.register_modules(unet=A_			,  scheduler=A_  )







			@torch.no_grad()
			def __call__(			self			: Optional[Any]			,  lowercase			: int = 1			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: int = 50			,  lowercase			: Optional[str] = "pil"			,  lowercase			: bool = True			,  **lowercase			: Optional[Any]			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,  generator=A_			,  )
								UpperCAmelCase			:   Optional[Any]      =				image.to(self.device  )

								# set step values
								self.scheduler.set_timesteps(A_  )

								for t in self.progress_bar(self.scheduler.timesteps  ):
													# 1. predict noise model_output
													UpperCAmelCase			:   Union[str, Any]      =				self.unet(A_			,  A_  ).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase			:   List[Any]      =				self.scheduler.step(A_			,  A_			,  A_  ).prev_sample

								UpperCAmelCase			:   int      =				(image / 2 + 0.5).clamp(0			,  1  )
								UpperCAmelCase			:   Dict      =				image.cpu().permute(0			,  2			,  3			,  1  ).numpy()
								if output_type == "pil":
													UpperCAmelCase			:   Optional[int]      =				self.numpy_to_pil(A_  )

								if not return_dict:
													return (image,), "This is a local test"

								return ImagePipelineOutput(images=A_  ), "This is a local test"





"""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  )


