

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)







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




