


import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					List[str]		,						_lowercase      :					VQModel		,						_lowercase      :					UNetaDModel		,						_lowercase      :					DDIMScheduler ):




         """simple docstring"""







         super().__init__()
         self.register_modules(vqvae=_lowercase		,						unet=_lowercase		,						scheduler=_lowercase )






  @torch.no_grad()
  def __call__(					self      :					Optional[Any]		,						_lowercase      :					int = 1		,						_lowercase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None		,						_lowercase      :					float = 0.0		,						_lowercase      :					int = 50		,						_lowercase      :					Optional[str] = "pil"		,						_lowercase      :					bool = True		,						**_lowercase      :					List[Any]		,						):




         """simple docstring"""







         UpperCAmelCase__										=     randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,						generator=_lowercase		,						)
         UpperCAmelCase__										=     latents.to(self.device )

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase__										=     latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(_lowercase )

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase__										=     "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )

         UpperCAmelCase__										=     {}
         if accepts_eta:
                UpperCAmelCase__										=     eta

         for t in self.progress_bar(self.scheduler.timesteps ):
                UpperCAmelCase__										=     self.scheduler.scale_model_input(_lowercase		,						_lowercase )
                # predict the noise residual
                UpperCAmelCase__										=     self.unet(_lowercase		,						_lowercase ).sample
                # compute the previous noisy sample x_t -> x_t-1
                UpperCAmelCase__										=     self.scheduler.step(_lowercase		,						_lowercase		,						_lowercase		,						**_lowercase ).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase__										=     self.vqvae.decode(_lowercase ).sample

         UpperCAmelCase__										=     (image / 2 + 0.5).clamp(0		,						1 )
         UpperCAmelCase__										=     image.cpu().permute(0		,						2		,						3		,						1 ).numpy()
         if output_type == "pil":
                UpperCAmelCase__										=     self.numpy_to_pil(_lowercase )

         if not return_dict:
                return (image,)

         return ImagePipelineOutput(images=_lowercase )









import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


A      = get_tests_dir("fixtures/test_sentencepiece_bpe.model")


class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							unittest.TestCase		):
  A__=  BartphoTokenizer
  A__=  False
  A__=  True






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         super().setUp()

         UpperCAmelCase__										=     ["▁This", "▁is", "▁a", "▁t", "est"]
         UpperCAmelCase__										=     dict(zip(_lowercase		,						range(len(_lowercase ) ) ) )
         UpperCAmelCase__										=     {"unk_token": "<unk>"}

         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["monolingual_vocab_file"] )
         with open(self.monolingual_vocab_file		,						"w"		,						encoding="utf-8" ) as fp:
                for token in vocab_tokens:
                       fp.write(F"""{token} {vocab_tokens[token]}\n""" )

         UpperCAmelCase__										=     BartphoTokenizer(_lowercase		,						self.monolingual_vocab_file		,						**self.special_tokens_map )
         tokenizer.save_pretrained(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					Dict		,						**_lowercase      :					Optional[Any] ):




         """simple docstring"""







         kwargs.update(self.special_tokens_map )
         return BartphoTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     "This is a là test"
         UpperCAmelCase__										=     "This is a<unk><unk> test"
         return input_text, output_text






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BartphoTokenizer(_lowercase		,						self.monolingual_vocab_file		,						**self.special_tokens_map )
         UpperCAmelCase__										=     "This is a là test"
         UpperCAmelCase__										=     "▁This ▁is ▁a ▁l à ▁t est".split()
         UpperCAmelCase__										=     tokenizer.tokenize(_lowercase )
         self.assertListEqual(_lowercase		,						_lowercase )

         UpperCAmelCase__										=     tokens + [tokenizer.unk_token]
         UpperCAmelCase__										=     [4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase )		,						_lowercase )





