


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, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, )			->  Tuple:




           """simple docstring"""

           super().__init__()
           self.register_modules(transformer=__lowerCamelCase, vae=__lowerCamelCase, scheduler=__lowerCamelCase)

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




           """simple docstring"""

           if not isinstance(__lowerCamelCase, __lowerCamelCase):
               _lowercase			:				Optional[int] 						=						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, lowerCamelCase, lowerCamelCase = 4.0, lowerCamelCase = None, lowerCamelCase = 50, lowerCamelCase = "pil", lowerCamelCase = True, )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				int 						=						len(__lowerCamelCase)
           _lowercase			:				List[str] 						=						self.transformer.config.sample_size
           _lowercase			:				int 						=						self.transformer.config.in_channels

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

           _lowercase			:				List[str] 						=						torch.tensor(__lowerCamelCase, device=self.device).reshape(-1)
           _lowercase			:				Any 						=						torch.tensor([10_00] * batch_size, device=self.device)
           _lowercase			:				Union[str, Any] 						=						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:
                   _lowercase			:				Optional[Any] 						=						latent_model_input[: len(__lowerCamelCase) // 2]
                   _lowercase			:				str 						=						torch.cat([half, half], dim=0)
               _lowercase			:				List[Any] 						=						self.scheduler.scale_model_input(__lowerCamelCase, __lowerCamelCase)

               _lowercase			:				Tuple 						=						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+)
                   _lowercase			:				List[Any] 						=						latent_model_input.device.type == 'mps'
                   if isinstance(__lowerCamelCase, __lowerCamelCase):
                       _lowercase			:				Tuple 						=						torch.floataa if is_mps else torch.floataa
                   else:
                       _lowercase			:				Optional[Any] 						=						torch.intaa if is_mps else torch.intaa
                   _lowercase			:				List[Any] 						=						torch.tensor([timesteps], dtype=__lowerCamelCase, device=latent_model_input.device)
               elif len(timesteps.shape) == 0:
                   _lowercase			:				Optional[int] 						=						timesteps[None].to(latent_model_input.device)
               # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
               _lowercase			:				Any 						=						timesteps.expand(latent_model_input.shape[0])
               # predict noise model_output
               _lowercase			:				List[str] 						=						self.transformer(
                   __lowerCamelCase, timestep=__lowerCamelCase, class_labels=__lowerCamelCase).sample

               # perform guidance
               if guidance_scale > 1:
                   _lowercase						,							_lowercase			:				List[Any] 						=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   _lowercase						,							_lowercase			:				str 						=						torch.split(__lowerCamelCase, len(__lowerCamelCase) // 2, dim=0)

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

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

               # learned sigma
               if self.transformer.config.out_channels // 2 == latent_channels:
                   _lowercase						,							_lowercase			:				List[str] 						=						torch.split(__lowerCamelCase, __lowerCamelCase, dim=1)
               else:
                   _lowercase			:				int 						=						noise_pred

               # compute previous image: x_t -> x_t-1
               _lowercase			:				List[Any] 						=						self.scheduler.step(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase).prev_sample

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

           _lowercase			:				List[Any] 						=						1 / self.vae.config.scaling_factor * latents
           _lowercase			:				Optional[Any] 						=						self.vae.decode(__lowerCamelCase).sample

           _lowercase			:				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
           _lowercase			:				Tuple 						=						samples.cpu().permute(0, 2, 3, 1).float().numpy()

           if output_type == "pil":
               _lowercase			:				Any 						=						self.numpy_to_pil(__lowerCamelCase)

           if not return_dict:
               return (samples,)

           return ImagePipelineOutput(images=__lowerCamelCase)





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


__A			:					Dict						=			logging.get_logger(__name__)





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





   def __init__(       self					:			List[Any]     , **__lowerCamelCase					:			Any			):
       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               SCREAMING_SNAKE_CASE										=   deprecated_arg[3:]
               SCREAMING_SNAKE_CASE										=   not kwargs.pop(__lowerCamelCase			)
               logger.warning(
                   f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                   f" {positive_arg}={kwargs[positive_arg]}"			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("tpu_name"     , self.tpu_name			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("device_idx"     , self.device_idx			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("eager_mode"     , self.eager_mode			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("use_xla"     , self.use_xla			)
       super().__init__(**__lowerCamelCase			)

   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Name of TPU"}					,       )
   lowerCamelCase__				=      field(
       default=0					,       metadata={"help": "CPU / GPU device index. Defaults to 0."}					,       )
   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Benchmark models in eager model."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
       }					,       )





   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       requires_backends(self     , ["tf"]			)
       SCREAMING_SNAKE_CASE										=   None
       if self.tpu:
           try:
               if self.tpu_name:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
               else:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               SCREAMING_SNAKE_CASE										=   None
       return tpu





   @cached_property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu			)
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

           SCREAMING_SNAKE_CASE										=   tf.distribute.TPUStrategy(self._setup_tpu			)
       else:
           # currently no multi gpu is allowed
           if self.is_gpu:
               # TODO: Currently only single GPU is supported
               tf.config.set_visible_devices(self.gpu_list[self.device_idx]     , "GPU"			)
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"			)
           else:
               tf.config.set_visible_devices([]     , "GPU"			)  # disable GPU
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"			)

       return strategy





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       return self._setup_tpu is not None





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       requires_backends(self     , ["tf"]			)
       return self._setup_strategy





   @property
   def      _snake_case		(       self					:			List[str]			):
       requires_backends(self     , ["tf"]			)
       return tf.config.list_physical_devices("GPU"			)





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.cuda:
           return len(self.gpu_list			)
       return 0





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.n_gpu > 0