






"""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_			(__A	):





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

				# create a imagenet -> id dictionary for easier use
				UpperCAmelCase_	:					str     	=   {}
				if idalabel is not None:
							for key, value in idalabel.items():
										for label in value.split(","		):
													UpperCAmelCase_	:					Union[str, Any]     	=   int(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   dict(sorted(self.labels.items()		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Union[str, List[str]]		)		->     List[int]:

				if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					str     	=   list(lowerCAmelCase_		)

				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   :					List[str]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					float = 4.0      ,      lowerCAmelCase_   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase_   :					int = 50      ,      lowerCAmelCase_   :					Optional[str] = "pil"      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Union[ImagePipelineOutput, Tuple]:
				UpperCAmelCase_	:					Optional[int]     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   self.transformer.config.sample_size
				UpperCAmelCase_	:					Optional[Any]     	=   self.transformer.config.in_channels

				UpperCAmelCase_	:					Tuple     	=   randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)      ,      generator=lowerCAmelCase_      ,      device=self.device      ,      dtype=self.transformer.dtype      ,      )
				UpperCAmelCase_	:					str     	=   torch.cat([latents] * 2		) if guidance_scale > 1 else latents

				UpperCAmelCase_	:					List[Any]     	=   torch.tensor(lowerCAmelCase_      ,      device=self.device		).reshape(-1		)
				UpperCAmelCase_	:					Optional[Any]     	=   torch.tensor([1_000] * batch_size      ,      device=self.device		)
				UpperCAmelCase_	:					int     	=   torch.cat([class_labels, class_null]      ,      0		) if guidance_scale > 1 else class_labels

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

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

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

							# perform guidance
							if guidance_scale > 1:
										UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   torch.split(lowerCAmelCase_      ,      len(lowerCAmelCase_		) // 2      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										UpperCAmelCase_	:					Tuple     	=   torch.cat([half_eps, half_eps]      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   torch.cat([eps, rest]      ,      dim=1		)

							# learned sigma
							if self.transformer.config.out_channels // 2 == latent_channels:
										UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   torch.split(lowerCAmelCase_      ,      lowerCAmelCase_      ,      dim=1		)
							else:
										UpperCAmelCase_	:					Optional[Any]     	=   noise_pred

							# compute previous image: x_t -> x_t-1
							UpperCAmelCase_	:					Union[str, Any]     	=   self.scheduler.step(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		).prev_sample

				if guidance_scale > 1:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input.chunk(2      ,      dim=0		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   latent_model_input

				UpperCAmelCase_	:					Dict     	=   1 / self.vae.config.scaling_factor * latents
				UpperCAmelCase_	:					Dict     	=   self.vae.decode(lowerCAmelCase_		).sample

				UpperCAmelCase_	:					Tuple     	=   (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_	:					Optional[Any]     	=   samples.cpu().permute(0      ,      2      ,      3      ,      1		).float().numpy()

				if output_type == "pil":
							UpperCAmelCase_	:					Optional[int]     	=   self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase_		)









"""simple docstring"""

import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


a							      =     logging.get_logger(__name__)

a							      =     {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Dict=None       ,				**_UpperCAmelCase			:			Optional[Any]						):
       logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'						)
       _A     =  model
       _A     =  kwargs.get('model_save_dir'       ,				_UpperCAmelCase						)
       _A     =  kwargs.get('latest_model_name'       ,				_UpperCAmelCase						)

 def __call__(					self			:			Dict       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  {k: np.array(_UpperCAmelCase						) for k, v in kwargs.items()}
       return self.model.run(_UpperCAmelCase       ,				_UpperCAmelCase						)

 @staticmethod
 def      lowerCAmelCase_				(					_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			List[Any]=None						):
       if provider is None:
             logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
             _A     =  'CPUExecutionProvider'

       return ort.InferenceSession(_UpperCAmelCase       ,				providers=[provider]       ,				sess_options=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

       _A     =  self.model_save_dir.joinpath(self.latest_model_name						)
       _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
       try:
             shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
       except shutil.SameFileError:
             pass

       # copy external weights (for models >2GB)
       _A     =  self.model_save_dir.joinpath(_UpperCAmelCase						)
       if src_path.exists():
             _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
             try:
                   shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
             except shutil.SameFileError:
                   pass

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, os.PathLike]       ,				**_UpperCAmelCase			:			List[str]       ,				):
       if os.path.isfile(_UpperCAmelCase						):
             logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''						)
             return

       os.makedirs(_UpperCAmelCase       ,				exist_ok=_UpperCAmelCase						)

       # saving model weights/files
       self._save_pretrained(_UpperCAmelCase       ,				**_UpperCAmelCase						)

 @classmethod
 def      lowerCAmelCase_				(					cls			:			Tuple       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[Union[bool, str, None]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, None]] = None       ,				_UpperCAmelCase			:			bool = False       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional["ort.SessionOptions"] = None       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
       # load model from local directory
       if os.path.isdir(_UpperCAmelCase						):
             _A     =  OnnxRuntimeModel.load_model(
                 os.path.join(_UpperCAmelCase       ,				_UpperCAmelCase						)       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
             _A     =  Path(_UpperCAmelCase						)
       # load model from hub
       else:
             # download model
             _A     =  hf_hub_download(
                 repo_id=_UpperCAmelCase       ,				filename=_UpperCAmelCase       ,				use_auth_token=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				cache_dir=_UpperCAmelCase       ,				force_download=_UpperCAmelCase       ,				)
             _A     =  Path(_UpperCAmelCase						).parent
             _A     =  Path(_UpperCAmelCase						).name
             _A     =  OnnxRuntimeModel.load_model(_UpperCAmelCase       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
       return cls(model=_UpperCAmelCase       ,				**_UpperCAmelCase						)



 @classmethod
 def      lowerCAmelCase_				(					cls			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  None
       if len(str(_UpperCAmelCase						).split('@'						)						) == 2:
             _A  ,       _A     =  model_id.split('@'						)

       return cls._from_pretrained(
           model_id=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				cache_dir=_UpperCAmelCase       ,				force_download=_UpperCAmelCase       ,				use_auth_token=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

