


'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


_UpperCAmelCase				: Tuple							    =				re.compile(r"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase				: Union[str, Any]							    =				re.compile(r"""([a-z\d])([A-Z])""")

_UpperCAmelCase				: Any							    =				re.compile(r"""(?<!_)_(?!_)""")
_UpperCAmelCase				: str							    =				re.compile(r"""(_{2,})""")

_UpperCAmelCase				: Optional[int]							    =				r"""^\w+(\.\w+)*$"""

_UpperCAmelCase				: str							    =				r"""<>:/\|?*"""

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				_uppercase_uppercase_re.sub(r'''\1_\2''',				lowerCamelCase)
						__lowerCAmelCase							       =				_lowercase_uppercase_re.sub(r'''\1_\2''',				lowerCamelCase)
						return name.lower()

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				_single_underscore_re.split(lowerCamelCase)
						__lowerCAmelCase							       =				[_multiple_underscores_re.split(lowerCamelCase) for n in name]
						return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase) if n != '''''')

def 							__magic_name__(						lowerCamelCase):
						if os.path.basename(lowerCamelCase) != name:
												raise ValueError(F"""Should be a dataset name, not a path: {name}""")
						return camelcase_to_snakecase(lowerCamelCase)

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						if os.path.basename(lowerCamelCase) != name:
												raise ValueError(F"""Should be a dataset name, not a path: {name}""")
						if not re.match(_split_re,				lowerCamelCase):
												raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'.""")
						return F"""{filename_prefix_for_name(lowerCamelCase)}-{split}"""

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=None):
						__lowerCAmelCase							       =				filename_prefix_for_split(lowerCamelCase,				lowerCamelCase)
						if filetype_suffix:
												prefix += F""".{filetype_suffix}"""
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				lowerCamelCase)
						return F"""{filepath}*"""




def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=None,				lowerCamelCase=None):
						__lowerCAmelCase							       =				filename_prefix_for_split(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				os.path.join(lowerCamelCase,				lowerCamelCase)

						if shard_lengths:
												__lowerCAmelCase							       =				len(lowerCamelCase)
												__lowerCAmelCase							       =				[F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(lowerCamelCase)]
												if filetype_suffix:
																		__lowerCAmelCase							       =				[filename + F""".{filetype_suffix}""" for filename in filenames]
												return filenames
						else:
												__lowerCAmelCase							       =				prefix
												if filetype_suffix:
																		filename += F""".{filetype_suffix}"""
												return [filename]





'''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       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			):
										super().__init__()
										self.register_modules(transformer=__lowercase       ,			vae=__lowercase       ,			scheduler=__lowercase		)

										# 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(__lowercase		)
																__lowerCAmelCase							       =				dict(sorted(self.labels.items()		)		)




				def 					_snake_case  (self       ,			__lowercase		):

										if not isinstance(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				list(__lowercase		)

										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       ,			__lowercase       ,			__lowercase = 4.0       ,			__lowercase = None       ,			__lowercase = 50       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										__lowerCAmelCase							       =				len(__lowercase		)
										__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=__lowercase       ,			device=self.device       ,			dtype=self.transformer.dtype       ,			)
										__lowerCAmelCase							       =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

										__lowerCAmelCase							       =				torch.tensor(__lowercase       ,			device=self.device		).reshape(-1		)
										__lowerCAmelCase							       =				torch.tensor([10_00] * 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(__lowercase		)

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

																__lowerCAmelCase							       =				t
																if not torch.is_tensor(__lowercase		):
																						# 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(__lowercase       ,			__lowercase		):
																												__lowerCAmelCase							       =				torch.floataa if is_mps else torch.floataa
																						else:
																												__lowerCAmelCase							       =				torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase							       =				torch.tensor([timesteps]       ,			dtype=__lowercase       ,			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(
																    __lowercase       ,			timestep=__lowercase       ,			class_labels=__lowercase		).sample

																# perform guidance
																if guidance_scale > 1:
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																						__lowerCAmelCase       ,					__lowerCAmelCase							       =				torch.split(__lowercase       ,			len(__lowercase		) // 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(__lowercase       ,			__lowercase       ,			dim=1		)
																else:
																						__lowerCAmelCase							       =				noise_pred

																# compute previous image: x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(__lowercase       ,			__lowercase       ,			__lowercase		).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(__lowercase		).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(__lowercase		)

										if not return_dict:
																return (samples,)

										return ImagePipelineOutput(images=__lowercase		)

