



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 		_lowercase		(       UpperCAmelCase__	):

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       TransformeraDModel							,   __lowerCAmelCase			:       AutoencoderKL							,   __lowerCAmelCase			:       KarrasDiffusionSchedulers							,   __lowerCAmelCase			:       Optional[Dict[int, str]] = None							,   )       ->				List[str]:


    """simple docstring"""






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

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

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[str, List[str]]							)       ->				List[int]:


    """simple docstring"""






    if not isinstance(__lowerCAmelCase							,   __lowerCAmelCase							):
     a						=		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			:       Optional[int]							,   __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]:


    """simple docstring"""






    a						=		len(__lowerCAmelCase							)
    a						=		self.transformer.config.sample_size
    a						=		self.transformer.config.in_channels

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

    a						=		torch.tensor(__lowerCAmelCase							,   device=self.device							).reshape(-1							)
    a						=		torch.tensor([1000] * batch_size							,   device=self.device							)
    a						=		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:
      a						=		latent_model_input[: len(__lowerCAmelCase							) // 2]
      a						=		torch.cat([half, half]							,   dim=0							)
     a						=		self.scheduler.scale_model_input(__lowerCAmelCase							,   __lowerCAmelCase							)

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

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

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

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

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

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

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

    a						=		1 / self.vae.config.scaling_factor * latents
    a						=		self.vae.decode(__lowerCAmelCase							).sample

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

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

    if output_type == "pil":
     a						=		self.numpy_to_pil(__lowerCAmelCase							)

    if not return_dict:
     return (samples,)

    return ImagePipelineOutput(images=__lowerCAmelCase							)






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(UpperCAmelCase__	),   '''Tatoeba directory does not exist.'''	)
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		tempfile.mkdtemp()
    return TatoebaConverter(save_dir=__lowerCAmelCase							)

   @slow
   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    self.resolver.convert_models(["heb-eng"]							)




   @slow
   def 		A							(      self			:       Dict							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.resolver.write_model_card("opus-mt-he-en"							,   dry_run=__lowerCAmelCase							)
    assert mmeta["long_pair"] == "heb-eng"

