



'''simple docstring'''





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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a  ,    _a      =  image.size
     _a  ,    _a      =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     _a      =  image.resize((w, h)  ,	resample=PIL_INTERPOLATION['''lanczos'''])
     _a      =  np.array(__A).astype(np.floataa) / 2_55.0
     _a      =  image[None].transpose(0  ,	3  ,	1  ,	2)
     _a      =  torch.from_numpy(__A)
     return 2.0 * image - 1.0






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A ,   )    ->     int:


      """simple docstring"""



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



 @torch.no_grad()
 def __call__(self ,   A = None ,   A = 1 ,   A = 100 ,   A = 0.0 ,   A = None ,   A = "pil" ,   A = True ,   )    ->     str:


      """simple docstring"""



      if isinstance(_snake_case ,   PIL.Image.Image       ):
           _a      =  1
      elif isinstance(_snake_case ,   torch.Tensor       ):
           _a      =  image.shape[0]
      else:
           raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_snake_case       )}'''       )

      if isinstance(_snake_case ,   PIL.Image.Image       ):
           _a      =  preprocess(_snake_case       )

      _a  ,    _a      =  image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      _a      =  (batch_size, self.unet.config.in_channels // 2, height, width)
      _a      =  next(self.unet.parameters()       ).dtype

      _a      =  randn_tensor(_snake_case ,   generator=_snake_case ,   device=self.device ,   dtype=_snake_case       )

      _a      =  image.to(device=self.device ,   dtype=_snake_case       )

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(_snake_case ,   device=self.device       )
      _a      =  self.scheduler.timesteps

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

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      _a      =  '''eta''' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
      _a      =  {}
      if accepts_eta:
           _a      =  eta

      for t in self.progress_bar(_snake_case       ):
           # concat latents and low resolution image in the channel dimension.
           _a      =  torch.cat([latents, image] ,   dim=1       )
           _a      =  self.scheduler.scale_model_input(_snake_case ,   _snake_case       )
           # predict the noise residual
           _a      =  self.unet(_snake_case ,   _snake_case       ).sample
           # compute the previous noisy sample x_t -> x_t-1
           _a      =  self.scheduler.step(_snake_case ,   _snake_case ,   _snake_case ,   **_snake_case       ).prev_sample

      # decode the image latents with the VQVAE
      _a      =  self.vqvae.decode(_snake_case       ).sample
      _a      =  torch.clamp(_snake_case ,   -1.0 ,   1.0       )
      _a      =  image / 2 + 0.5
      _a      =  image.cpu().permute(0 ,   2 ,   3 ,   1       ).numpy()

      if output_type == "pil":
           _a      =  self.numpy_to_pil(_snake_case       )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=_snake_case       )









import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Any          =  PhobertTokenizer
   UpperCAmelCase__  :    List[str]          =  False







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase_          =							['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
        UpperCAmelCase_          =							dict(zip(_snake_case					,						range(len(_snake_case))))
        UpperCAmelCase_          =							['''#version: 0.2''', '''l à</w>''']
        UpperCAmelCase_          =							{'''unk_token''': '''<unk>'''}

        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file'''])
        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''merges_file'''])

        with open(self.vocab_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             for token in vocab_tokens:
                  fp.write(F"""{token} {vocab_tokens[token]}\n""")
        with open(self.merges_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             fp.write('''\n'''.join(_snake_case))







   def lowerCamelCase						(      self		:						int					,						**_snake_case		:						Any):

        """simple docstring"""


        kwargs.update(self.special_tokens_map)
        return PhobertTokenizer.from_pretrained(self.tmpdirname					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
        return input_text, output_text







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							PhobertTokenizer(self.vocab_file					,						self.merges_file					,						**self.special_tokens_map)
        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        print(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							tokens + [tokenizer.unk_token]

        UpperCAmelCase_          =							[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case)					,						_snake_case)






