
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__											=logging.get_logger(__name__)

lowercase__											={
    'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
        'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
    ),
}







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			Any       =    'gptsan-japanese'
			lowerCamelCase__	:			int       =    [
			    'past_key_values',
			]
			lowerCamelCase__	:			Any       =    {
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}




			def __init__(       self					,   UpperCAmelCase=3_60_00					,   UpperCAmelCase=12_80					,   UpperCAmelCase=10_24					,   UpperCAmelCase=81_92					,   UpperCAmelCase=40_96					,   UpperCAmelCase=1_28					,   UpperCAmelCase=10					,   UpperCAmelCase=0					,   UpperCAmelCase=16					,   UpperCAmelCase=16					,   UpperCAmelCase=1_28					,   UpperCAmelCase=0.0					,   UpperCAmelCase=1e-5					,   UpperCAmelCase=False					,   UpperCAmelCase=0.0					,   UpperCAmelCase="float32"					,   UpperCAmelCase=False					,   UpperCAmelCase=False					,   UpperCAmelCase=False					,   UpperCAmelCase=0.0_02					,   UpperCAmelCase=False					,   UpperCAmelCase=True					,   UpperCAmelCase=3_59_98					,   UpperCAmelCase=3_59_95					,   UpperCAmelCase=3_59_99					,   **UpperCAmelCase					,   ):
				a_      				=						vocab_size
				a_      				=						max_position_embeddings
				a_      				=						d_model
				a_      				=						d_ff
				a_      				=						d_ext
				a_      				=						d_spout
				a_      				=						num_switch_layers
				a_      				=						num_ext_layers
				a_      				=						num_switch_layers + num_ext_layers
				a_      				=						num_heads
				a_      				=						num_experts
				a_      				=						expert_capacity
				a_      				=						dropout_rate
				a_      				=						layer_norm_epsilon
				a_      				=						router_bias
				a_      				=						router_jitter_noise
				a_      				=						router_dtype
				a_      				=						router_ignore_padding_tokens
				a_      				=						output_hidden_states
				a_      				=						output_attentions
				a_      				=						initializer_factor
				a_      				=						output_router_logits
				a_      				=						use_cache

				super().__init__(
				    separator_token_id=UpperCAmelCase					,   pad_token_id=UpperCAmelCase					,   eos_token_id=UpperCAmelCase					,   **UpperCAmelCase					,   )


'''simple docstring'''
import unittest

import torch

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


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

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

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

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

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

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