



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  __magic_name__   (							A__						):






		def __init__(						self      :     Tuple				,		UpperCamelCase__      :     TransformeraDModel				,		UpperCamelCase__      :     AutoencoderKL				,		UpperCamelCase__      :     KarrasDiffusionSchedulers				,		UpperCamelCase__      :     Optional[Dict[int, str]] = None				,		)				->					Union[str, Any]:
								'''simple docstring'''







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

								# 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(UpperCamelCase__      )
														UpperCAmelCase         =     dict(sorted(self.labels.items()      )      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     Union[str, List[str]]      )				->					List[int]:
								'''simple docstring'''







								if not isinstance(UpperCamelCase__				,		UpperCamelCase__      ):
														UpperCAmelCase         =     list(UpperCamelCase__      )

								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      :     int				,		UpperCamelCase__      :     List[int]				,		UpperCamelCase__      :     float = 4.0				,		UpperCamelCase__      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,		UpperCamelCase__      :     int = 50				,		UpperCamelCase__      :     Optional[str] = "pil"				,		UpperCamelCase__      :     bool = True				,		)				->					Union[ImagePipelineOutput, Tuple]:
								'''simple docstring'''







								UpperCAmelCase         =     len(UpperCamelCase__      )
								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=UpperCamelCase__				,		device=self.device				,		dtype=self.transformer.dtype				,		)
								UpperCAmelCase         =     torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

								UpperCAmelCase         =     torch.tensor(UpperCamelCase__				,		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(UpperCamelCase__      )

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

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

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

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

								if not return_dict:
														return (samples,)

								return ImagePipelineOutput(images=UpperCamelCase__      )








import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch





@require_torch
@require_optimum
@slow
class  __magic_name__   (							unittest.TestCase						):






		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoTokenizer.from_pretrained(UpperCamelCase__      )
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer("This is me"				,		return_tensors="pt"      )

								UpperCAmelCase         =     model.to_bettertransformer()

								self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								UpperCAmelCase         =     model.generate(**UpperCamelCase__      )

								UpperCAmelCase         =     model.reverse_bettertransformer()

								self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

														self.assertFalse(
														    any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

														UpperCAmelCase         =     model_reloaded.generate(**UpperCamelCase__      )
														self.assertTrue(torch.allclose(UpperCamelCase__				,		UpperCamelCase__      )      )







		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     "hf-internal-testing/tiny-random-t5"
								UpperCAmelCase         =     AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      )

								UpperCAmelCase         =     model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
														with self.assertRaises(UpperCamelCase__      ):
																				model.save_pretrained(UpperCamelCase__      )

														UpperCAmelCase         =     model.reverse_bettertransformer()
														model.save_pretrained(UpperCamelCase__      )



