






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class   lowerCamelCase      (lowerCAmelCase_			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[str]:
							super().__init__()
							self.register_modules(unet=__snake_case				,		scheduler=__snake_case   )



		@torch.no_grad()
		def __call__(							self				,		_UpperCamelCase = 1				,		_UpperCamelCase = None				,		_UpperCamelCase = 5_0				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		**_UpperCamelCase				,		)	->							Union[ImagePipelineOutput, Tuple]:
							UpperCAmelCase_    :      Tuple            =						torch.randn(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,		generator=__snake_case				,		)
							UpperCAmelCase_    :      str            =						image.to(self.device   )

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

							for t in self.progress_bar(self.scheduler.timesteps   ):
												# 1. predict noise model_output
												UpperCAmelCase_    :      Optional[Any]            =						self.unet(__snake_case				,		__snake_case   ).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_    :      int            =						self.scheduler.step(__snake_case				,		__snake_case				,		__snake_case   ).prev_sample

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

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

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









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


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






