






from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   TransformeraDModel ,							SCREAMING_SNAKE_CASE_     :   AutoencoderKL ,							SCREAMING_SNAKE_CASE_     :   KarrasDiffusionSchedulers ,							SCREAMING_SNAKE_CASE_     :   Optional[Dict[int, str]] = None ,							):
											super().__init__()
											self.register_modules(transformer=SCREAMING_SNAKE_CASE_ ,							vae=SCREAMING_SNAKE_CASE_ ,							scheduler=SCREAMING_SNAKE_CASE_	)

											# create a imagenet -> id dictionary for easier use
											lowerCAmelCase__      			=						{}
											if idalabel is not None:
															for key, value in idalabel.items():
																			for label in value.split(''','''	):
																							lowerCAmelCase__      			=						int(SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						dict(sorted(self.labels.items()	)	)




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, List[str]]	):

											if not isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
															lowerCAmelCase__      			=						list(SCREAMING_SNAKE_CASE_	)

											for l in label:
															if l not in self.labels:
																			raise ValueError(
																			    f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'	)

											return [self.labels[l] for l in label]




							@torch.no_grad()
							def __call__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[int] ,							SCREAMING_SNAKE_CASE_     :   float = 4.0 ,							SCREAMING_SNAKE_CASE_     :   Optional[Union[torch.Generator, List[torch.Generator]]] = None ,							SCREAMING_SNAKE_CASE_     :   int = 50 ,							SCREAMING_SNAKE_CASE_     :   Optional[str] = "pil" ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							):
											lowerCAmelCase__      			=						len(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						self.transformer.config.sample_size
											lowerCAmelCase__      			=						self.transformer.config.in_channels

											lowerCAmelCase__      			=						randn_tensor(
											    shape=(batch_size, latent_channels, latent_size, latent_size) ,							generator=SCREAMING_SNAKE_CASE_ ,							device=self.device ,							dtype=self.transformer.dtype ,							)
											lowerCAmelCase__      			=						torch.cat([latents] * 2	) if guidance_scale > 1 else latents

											lowerCAmelCase__      			=						torch.tensor(SCREAMING_SNAKE_CASE_ ,							device=self.device	).reshape(-1	)
											lowerCAmelCase__      			=						torch.tensor([1_000] * batch_size ,							device=self.device	)
											lowerCAmelCase__      			=						torch.cat([class_labels, class_null] ,							0	) if guidance_scale > 1 else class_labels

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

											for t in self.progress_bar(self.scheduler.timesteps	):
															if guidance_scale > 1:
																			lowerCAmelCase__      			=						latent_model_input[: len(SCREAMING_SNAKE_CASE_	) // 2]
																			lowerCAmelCase__      			=						torch.cat([half, half] ,							dim=0	)
															lowerCAmelCase__      			=						self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

															lowerCAmelCase__      			=						t
															if not torch.is_tensor(SCREAMING_SNAKE_CASE_	):
																			# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																			# This would be a good case for the `match` statement (Python 3.10+)
																			lowerCAmelCase__      			=						latent_model_input.device.type == '''mps'''
																			if isinstance(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
																							lowerCAmelCase__      			=						torch.floataa if is_mps else torch.floataa
																			else:
																							lowerCAmelCase__      			=						torch.intaa if is_mps else torch.intaa
																			lowerCAmelCase__      			=						torch.tensor([timesteps] ,							dtype=SCREAMING_SNAKE_CASE_ ,							device=latent_model_input.device	)
															elif len(timesteps.shape	) == 0:
																			lowerCAmelCase__      			=						timesteps[None].to(latent_model_input.device	)
															# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
															lowerCAmelCase__      			=						timesteps.expand(latent_model_input.shape[0]	)
															# predict noise model_output
															lowerCAmelCase__      			=						self.transformer(
															    SCREAMING_SNAKE_CASE_ ,							timestep=SCREAMING_SNAKE_CASE_ ,							class_labels=SCREAMING_SNAKE_CASE_	).sample

															# perform guidance
															if guidance_scale > 1:
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						torch.split(SCREAMING_SNAKE_CASE_ ,							len(SCREAMING_SNAKE_CASE_	) // 2 ,							dim=0	)

																			lowerCAmelCase__      			=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			lowerCAmelCase__      			=						torch.cat([half_eps, half_eps] ,							dim=0	)

																			lowerCAmelCase__      			=						torch.cat([eps, rest] ,							dim=1	)

															# learned sigma
															if self.transformer.config.out_channels // 2 == latent_channels:
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						torch.split(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							dim=1	)
															else:
																			lowerCAmelCase__      			=						noise_pred

															# compute previous image: x_t -> x_t-1
															lowerCAmelCase__      			=						self.scheduler.step(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	).prev_sample

											if guidance_scale > 1:
															lowerCAmelCase__			,		lowerCAmelCase__      			=						latent_model_input.chunk(2 ,							dim=0	)
											else:
															lowerCAmelCase__      			=						latent_model_input

											lowerCAmelCase__      			=						1 / self.vae.config.scaling_factor * latents
											lowerCAmelCase__      			=						self.vae.decode(SCREAMING_SNAKE_CASE_	).sample

											lowerCAmelCase__      			=						(samples / 2 + 0.5).clamp(0 ,							1	)

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											lowerCAmelCase__      			=						samples.cpu().permute(0 ,							2 ,							3 ,							1	).float().numpy()

											if output_type == "pil":
															lowerCAmelCase__      			=						self.numpy_to_pil(SCREAMING_SNAKE_CASE_	)

											if not return_dict:
															return (samples,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)













from ...configuration_utils import PretrainedConfig
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:List[str]		      =    'bert-generation'




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[str]=50_358 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=1_024 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=24 ,							SCREAMING_SNAKE_CASE_     :   Any=16 ,							SCREAMING_SNAKE_CASE_     :   Tuple=4_096 ,							SCREAMING_SNAKE_CASE_     :   str="gelu" ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=512 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=1e-12 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=2 ,							SCREAMING_SNAKE_CASE_     :   int=1 ,							SCREAMING_SNAKE_CASE_     :   str="absolute" ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,							bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						layer_norm_eps
											lowerCAmelCase__      			=						position_embedding_type
											lowerCAmelCase__      			=						use_cache





