
'''simple docstring'''

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,):
    super().__init__()

    self.register_modules(vae=__SCREAMING_SNAKE_CASE    ,text_encoder=__SCREAMING_SNAKE_CASE    ,tokenizer=__SCREAMING_SNAKE_CASE    ,unet=__SCREAMING_SNAKE_CASE    ,scheduler=__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  List[Any]								=		self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
     SCREAMING_SNAKE_CASE  :  List[str]								=		0.1_8215 * latent.latent_dist.sample()
    return latent






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		(1 / 0.1_8215) * latent
    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  List[Any]								=		self.vae.decode(__SCREAMING_SNAKE_CASE      ).sample
    SCREAMING_SNAKE_CASE  :  str								=		(img / 2 + 0.5).clamp(0    ,1      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		img.detach().cpu().permute(0    ,2    ,3    ,1      ).numpy()
    SCREAMING_SNAKE_CASE  :  str								=		(img * 255).round().astype('uint8'      )
    return Image.fromarray(img[0]      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		self.tokenizer(
        __SCREAMING_SNAKE_CASE    ,padding='max_length'    ,max_length=self.tokenizer.model_max_length    ,truncation=__SCREAMING_SNAKE_CASE    ,return_tensors='pt'    ,)

    SCREAMING_SNAKE_CASE  :  Dict								=		self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

    SCREAMING_SNAKE_CASE  :  Dict								=		self.tokenizer(
        ''    ,padding='max_length'    ,max_length=self.tokenizer.model_max_length    ,truncation=__SCREAMING_SNAKE_CASE    ,return_tensors='pt'    ,)

    SCREAMING_SNAKE_CASE  :  Dict								=		self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

    return torch.cat([uncond_embedding, text_embedding]      )






   def __call__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 0.3    ,__SCREAMING_SNAKE_CASE = 0.6    ,__SCREAMING_SNAKE_CASE = 0.5    ,__SCREAMING_SNAKE_CASE = 42    ,__SCREAMING_SNAKE_CASE = 50    ,__SCREAMING_SNAKE_CASE = 7.5    ,):
    SCREAMING_SNAKE_CASE  :  Any								=		steps - int(kmin * steps      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		steps - int(kmax * steps      )

    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.prep_text(__SCREAMING_SNAKE_CASE      )

    self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Optional[int]								=		img.size
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.encode(__SCREAMING_SNAKE_CASE      )

    torch.manual_seed(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  int								=		torch.randn(
        (1, self.unet.config.in_channels, height // 8, width // 8)    ,).to(self.device      )

    SCREAMING_SNAKE_CASE  :  Any								=		self.scheduler.add_noise(
        __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,timesteps=self.scheduler.timesteps[tmax]    ,)

    SCREAMING_SNAKE_CASE  :  int								=		torch.cat([latents] * 2      )

    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE    ,self.scheduler.timesteps[tmax]      )

    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  Any								=		self.unet(
         __SCREAMING_SNAKE_CASE    ,self.scheduler.timesteps[tmax]    ,encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,).sample

    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  int								=		pred.chunk(2      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		pred_uncond + guidance_scale * (pred_text - pred_uncond)

    SCREAMING_SNAKE_CASE  :  Dict								=		self.scheduler.step(__SCREAMING_SNAKE_CASE    ,self.scheduler.timesteps[tmax]    ,__SCREAMING_SNAKE_CASE      ).prev_sample

    for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
     if i > tmax:
      if i < tmin:  # layout generation phase
       SCREAMING_SNAKE_CASE  :  List[str]								=		self.scheduler.add_noise(
           __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,timesteps=__SCREAMING_SNAKE_CASE    ,)

       SCREAMING_SNAKE_CASE  :  int								=		(mix_factor * latents) + (
           1 - mix_factor
       ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
       SCREAMING_SNAKE_CASE  :  Dict								=		torch.cat([input] * 2      )

      else:  # content generation phase
       SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.cat([latents] * 2      )

      SCREAMING_SNAKE_CASE  :  Any								=		self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

      with torch.no_grad():
       SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.unet(
           __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,).sample

      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  str								=		pred.chunk(2      )
      SCREAMING_SNAKE_CASE  :  Tuple								=		pred_uncond + guidance_scale * (pred_text - pred_uncond)

      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.scheduler.step(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ).prev_sample

    return self.decode(__SCREAMING_SNAKE_CASE      )




'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


