





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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       Dict	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       List[Any] = None	,			)	->List[Any]:



   '''simple docstring'''



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

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[int])	->List[int]:



   '''simple docstring'''



   if not isinstance(lowerCamelCase_	,			lowerCamelCase_):
     lowerCamelCase__:				List[Any]        =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    :       Union[str, Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       List[str] = 4.0	,			UpperCAmelCase_    :       Any = None	,			UpperCAmelCase_    :       Optional[int] = 50	,			UpperCAmelCase_    :       Optional[Any] = "pil"	,			UpperCAmelCase_    :       int = True	,			)	->Union[ImagePipelineOutput, Tuple]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =len(lowerCamelCase_)
   lowerCamelCase__:				List[Any]        =self.transformer.config.sample_size
   lowerCamelCase__:				Optional[int]        =self.transformer.config.in_channels

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

   lowerCamelCase__:				Any        =torch.tensor(lowerCamelCase_	,			device=self.device).reshape(-1)
   lowerCamelCase__:				int        =torch.tensor([1_000] * batch_size	,			device=self.device)
   lowerCamelCase__:				Optional[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:
       lowerCamelCase__:				str        =latent_model_input[: len(lowerCamelCase_) // 2]
       lowerCamelCase__:				Union[str, Any]        =torch.cat([half, half]	,			dim=0)
     lowerCamelCase__:				List[Any]        =self.scheduler.scale_model_input(lowerCamelCase_	,			lowerCamelCase_)

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

     # perform guidance
     if guidance_scale > 1:
       lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
       lowerCamelCase__  , lowerCamelCase__:				List[Any]        =torch.split(lowerCamelCase_	,			len(lowerCamelCase_) // 2	,			dim=0)

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

       lowerCamelCase__:				Tuple        =torch.cat([eps, rest]	,			dim=1)

     # learned sigma
     if self.transformer.config.out_channels // 2 == latent_channels:
       lowerCamelCase__  , lowerCamelCase__:				Tuple        =torch.split(lowerCamelCase_	,			lowerCamelCase_	,			dim=1)
     else:
       lowerCamelCase__:				str        =noise_pred

     # compute previous image: x_t -> x_t-1
     lowerCamelCase__:				int        =self.scheduler.step(lowerCamelCase_	,			lowerCamelCase_	,			lowerCamelCase_).prev_sample

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

   lowerCamelCase__:				Any        =1 / self.vae.config.scaling_factor * latents
   lowerCamelCase__:				Any        =self.vae.decode(lowerCamelCase_).sample

   lowerCamelCase__:				Dict        =(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__:				Union[str, Any]        =samples.cpu().permute(0	,			2	,			3	,			1).float().numpy()

   if output_type == "pil":
     lowerCamelCase__:				int        =self.numpy_to_pil(lowerCamelCase_)

   if not return_dict:
     return (samples,)

   return ImagePipelineOutput(images=lowerCamelCase_)








import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      CTRLTokenizer
 lowercase_		=      False
 lowercase_		=      False

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Union[str, Any]:



   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase__:				Optional[Any]        =["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
   lowerCamelCase__:				Optional[Any]        =dict(zip(UpperCAmelCase_	,			range(len(UpperCAmelCase_))))
   lowerCamelCase__:				Union[str, Any]        =["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
   lowerCamelCase__:				Union[str, Any]        ={"unk_token": "<unk>"}

   lowerCamelCase__:				Union[str, Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:				Optional[Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["merges_file"])
   with open(self.vocab_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write(json.dumps(UpperCAmelCase_) + "\n")
   with open(self.merges_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write("\n".join(UpperCAmelCase_))

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			**UpperCAmelCase_    :       str)	->List[Any]:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return CTRLTokenizer.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				int        ="adapt react readapt apt"
   lowerCamelCase__:				int        ="adapt react readapt apt"
   return input_text, output_text







 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =CTRLTokenizer(self.vocab_file	,			self.merges_file	,			**self.special_tokens_map)
   lowerCamelCase__:				Union[str, Any]        ="adapt react readapt apt"
   lowerCamelCase__:				Tuple        ="adapt re@@ a@@ c@@ t re@@ adapt apt".split()
   lowerCamelCase__:				Dict        =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =tokens + [tokenizer.unk_token]

   lowerCamelCase__:				List[str]        =[0, 1, 2, 4, 5, 1, 0, 3, 6]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)	,			UpperCAmelCase_)

