


"""simple docstring"""





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


_a														=		logging.get_logger(__name__)

_a														=		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'mra'



		def __init__( self    ,		__a=5_02_65    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=1    ,		__a=0.02    ,		__a=1e-5    ,		__a="absolute"    ,		__a=4    ,		__a="full"    ,		__a=0    ,		__a=0    ,		__a=1    ,		__a=0    ,		__a=2    ,		**__a    ,		)	->	Union[str, Any]:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  block_per_row
							_UpperCamelCase										=  approx_mode
							_UpperCamelCase										=  initial_prior_first_n_blocks
							_UpperCamelCase										=  initial_prior_diagonal_n_blocks








"""simple docstring"""





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    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		)	->	List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(transformer=__a    ,		vae=__a    ,		scheduler=__a)

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



		def 						UpperCAmelCase							( self    ,		__a)	->	List[int]:






							'''simple docstring'''

							if not isinstance(__a    ,		__a):
												_UpperCamelCase										=  list(__a)

							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    ,		__a    ,		__a = 4.0    ,		__a = None    ,		__a = 50    ,		__a = "pil"    ,		__a = True    ,		)	->	Union[ImagePipelineOutput, Tuple]:






							'''simple docstring'''
							_UpperCamelCase										=  len(__a)
							_UpperCamelCase										=  self.transformer.config.sample_size
							_UpperCamelCase										=  self.transformer.config.in_channels

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

							_UpperCamelCase										=  torch.tensor(__a    ,		device=self.device).reshape(-1)
							_UpperCamelCase										=  torch.tensor([10_00] * batch_size    ,		device=self.device)
							_UpperCamelCase										=  torch.cat([class_labels, class_null]    ,		0) if guidance_scale > 1 else class_labels

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

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

												_UpperCamelCase										=  t
												if not torch.is_tensor(__a):
																	# 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+)
																	_UpperCamelCase										=  latent_model_input.device.type == '''mps'''
																	if isinstance(__a    ,		__a):
																						_UpperCamelCase										=  torch.floataa if is_mps else torch.floataa
																	else:
																						_UpperCamelCase										=  torch.intaa if is_mps else torch.intaa
																	_UpperCamelCase										=  torch.tensor([timesteps]    ,		dtype=__a    ,		device=latent_model_input.device)
												elif len(timesteps.shape) == 0:
																	_UpperCamelCase										=  timesteps[None].to(latent_model_input.device)
												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												_UpperCamelCase										=  timesteps.expand(latent_model_input.shape[0])
												# predict noise model_output
												_UpperCamelCase										=  self.transformer(
												    __a    ,		timestep=__a    ,		class_labels=__a).sample

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

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

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

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

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

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

							_UpperCamelCase										=  1 / self.vae.config.scaling_factor * latents
							_UpperCamelCase										=  self.vae.decode(__a).sample

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

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

							if output_type == "pil":
												_UpperCamelCase										=  self.numpy_to_pil(__a)

							if not return_dict:
												return (samples,)

							return ImagePipelineOutput(images=__a)






