

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  42
		a__					       =  42


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__()
			self.register_modules(unet=__snake_case	,       scheduler=__snake_case)






		@torch.no_grad()
		def __call__(				self	,       __snake_case = 1	,       __snake_case = 50	,       __snake_case = None	,       __snake_case = "pil"	,       __snake_case = True	,       **__snake_case	,       ):
			_UpperCamelCase   :		Tuple			=						self.unet.config.sample_size
			_UpperCamelCase   :		Optional[int]			=						(batch_size, 3, img_size, img_size)

			_UpperCamelCase   :		str			=						self.unet

			# sample x_0 ~ N(0, sigma_0^2 * I)
			_UpperCamelCase   :		List[str]			=						randn_tensor(__snake_case	,       generator=__snake_case	,       device=self.device) * self.scheduler.init_noise_sigma

			self.scheduler.set_timesteps(__snake_case)

			for t in self.progress_bar(self.scheduler.timesteps):
				# here sigma_t == t_i from the paper
				_UpperCamelCase   :		List[Any]			=						self.scheduler.schedule[t]
				_UpperCamelCase   :		List[Any]			=						self.scheduler.schedule[t - 1] if t > 0 else 0

				# 1. Select temporarily increased noise level sigma_hat
				# 2. Add new noise to move from sample_i to sample_hat
				_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self.scheduler.add_noise_to_input(__snake_case	,       __snake_case	,       generator=__snake_case)

				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
				# The model inputs and output are adjusted by following eq. (213) in [1].
				_UpperCamelCase   :		Tuple			=						(sigma_hat / 2) * model((sample_hat + 1) / 2	,       sigma_hat / 2).sample

				# 4. Evaluate dx/dt at sigma_hat
				# 5. Take Euler step from sigma to sigma_prev
				_UpperCamelCase   :		Tuple			=						self.scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       __snake_case)

				if sigma_prev != 0:
					# 6. Apply 2nd order correction
					# The model inputs and output are adjusted by following eq. (213) in [1].
					_UpperCamelCase   :		List[Any]			=						(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2	,       sigma_prev / 2).sample
					_UpperCamelCase   :		int			=						self.scheduler.step_correct(
					    __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       step_output.prev_sample	,       step_output['derivative']	,       )
				_UpperCamelCase   :		Any			=						step_output.prev_sample

			_UpperCamelCase   :		int			=						(sample / 2 + 0.5).clamp(0	,       1)
			_UpperCamelCase   :		List[Any]			=						sample.cpu().permute(0	,       2	,       3	,       1).numpy()
			if output_type == "pil":
				_UpperCamelCase   :		Any			=						self.numpy_to_pil(__snake_case)

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=__snake_case)







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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """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 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "rwkv"
		a__					       =  {"max_position_embeddings": "context_length"}


		def __init__(				self	,       __snake_case=5_02_77	,       __snake_case=10_24	,       __snake_case=40_96	,       __snake_case=32	,       __snake_case=None	,       __snake_case=None	,       __snake_case=1e-5	,       __snake_case=0	,       __snake_case=0	,       __snake_case=6	,       __snake_case=False	,       __snake_case=True	,       **__snake_case	,       ):
			_UpperCamelCase   :		str			=						vocab_size
			_UpperCamelCase   :		int			=						context_length
			_UpperCamelCase   :		Tuple			=						hidden_size
			_UpperCamelCase   :		Tuple			=						num_hidden_layers
			_UpperCamelCase   :		Dict			=						attention_hidden_size if attention_hidden_size is not None else hidden_size
			_UpperCamelCase   :		Tuple			=						intermediate_size if intermediate_size is not None else 4 * hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						layer_norm_epsilon
			_UpperCamelCase   :		Dict			=						rescale_every
			_UpperCamelCase   :		Optional[Any]			=						use_cache

			_UpperCamelCase   :		str			=						bos_token_id
			_UpperCamelCase   :		Optional[Any]			=						eos_token_id

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




