from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class        UpperCamelCase						(				__a				):





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				List[Any]:
												super().__init__()

												# make sure scheduler can always be converted to DDIM
												UpperCamelCase_		:							Union[str, Any]  =		DDIMScheduler.from_config(scheduler.config						)

												self.register_modules(unet=__SCREAMING_SNAKE_CASE			,     scheduler=__SCREAMING_SNAKE_CASE						)





						@torch.no_grad()
						def __call__(self			,     __UpperCamelCase = 1			,     __UpperCamelCase = None			,     __UpperCamelCase = 0.0			,     __UpperCamelCase = 50			,     __UpperCamelCase = None			,     __UpperCamelCase = "pil"			,     __UpperCamelCase = True			,     )				->				Union[ImagePipelineOutput, Tuple]:




												if isinstance(self.unet.config.sample_size			,     __SCREAMING_SNAKE_CASE						):
																		UpperCamelCase_		:							str  =		(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCamelCase_		:							Any  =		(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE						) and len(__SCREAMING_SNAKE_CASE						) != batch_size:
																		raise ValueError(
																		    f'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE						)}, but requested an effective batch'''
																		    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

												UpperCamelCase_		:							int  =		randn_tensor(__SCREAMING_SNAKE_CASE			,     generator=__SCREAMING_SNAKE_CASE			,     device=self.device			,     dtype=self.unet.dtype						)

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

												for t in self.progress_bar(self.scheduler.timesteps						):
																		# 1. predict noise model_output
																		UpperCamelCase_		:							Tuple  =		self.unet(__SCREAMING_SNAKE_CASE			,     __SCREAMING_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_		:							Union[str, Any]  =		self.scheduler.step(
																		    __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     eta=__SCREAMING_SNAKE_CASE			,     use_clipped_model_output=__SCREAMING_SNAKE_CASE			,     generator=__SCREAMING_SNAKE_CASE						).prev_sample

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE						)









'''simple docstring'''



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


UpperCAmelCase					:			Tuple       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "ctrl"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Tuple=246_534   ,				__SCREAMING_SNAKE_CASE   :			str=256   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1_280   ,				__SCREAMING_SNAKE_CASE   :			Dict=8_192   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=48   ,				__SCREAMING_SNAKE_CASE   :			Dict=16   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			str=0.1   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1E-6   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.02   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				**__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				)    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = n_positions
      __SCREAMING_SNAKE_CASE      = n_embd
      __SCREAMING_SNAKE_CASE      = n_layer
      __SCREAMING_SNAKE_CASE      = n_head
      __SCREAMING_SNAKE_CASE      = dff
      __SCREAMING_SNAKE_CASE      = resid_pdrop
      __SCREAMING_SNAKE_CASE      = embd_pdrop
      __SCREAMING_SNAKE_CASE      = layer_norm_epsilon
      __SCREAMING_SNAKE_CASE      = initializer_range

      __SCREAMING_SNAKE_CASE      = use_cache

      super().__init__(**__SCREAMING_SNAKE_CASE     )






