


'''simple docstring'''


import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class        __magic_name__  (					lowerCAmelCase						,		unittest.TestCase   ):

     """simple docstring"""






     lowerCamelCase__						=							SpeechaTextaTokenizer
     lowerCamelCase__						=							False


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          super().setUp()

          __A  :					List[Any]						     =       "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" "		)
          __A  :					str						     =       ["he re</w> 123", "here a 1456"]
          __A  :					Dict						     =       dict(zip(lowerCamelCase ,    range(len(lowerCamelCase		)		)		)		)

          __A  :					Any						     =       {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

          __A  :					Union[str, Any]						     =       tempfile.mkdtemp()
          __A  :					Tuple						     =       os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES["vocab_file"]		)
          __A  :					Union[str, 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(lowerCamelCase		) + "\n"		)

          with open(self.merges_file ,    "w"		) as fp:
               fp.write("\n".join(lowerCamelCase		)		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       list(self.get_tokenizer().get_vocab().keys()		)

          self.assertEqual(vocab_keys[0] ,    "<s>"		)
          self.assertEqual(vocab_keys[1] ,    "<pad>"		)
          self.assertEqual(vocab_keys[-1] ,    "vocab"		)
          self.assertEqual(len(lowerCamelCase		) ,    14		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          self.assertEqual(self.get_tokenizer().vocab_size ,    14		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

          # make sure @@ is correctly concatenated
          __A  :					Union[str, Any]						     =       [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
          __A  :					Optional[int]						     =       tokenizer.decode(lowerCamelCase		)

          self.assertTrue(output_string == "herecouple words ofthe"		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       SpeechaTextaTokenizer.from_pretrained(self.tmpdirname		)

          with tempfile.TemporaryDirectory() as tmp_dirname:
               tokenizer.save_pretrained(lowerCamelCase		)
               os.remove(os.path.join(lowerCamelCase ,    "merges.txt"		)		)

               # load tokenizer without merges file should not throw an error
               __A  :					Optional[int]						     =       SpeechaTextaTokenizer.from_pretrained(lowerCamelCase		)

          with tempfile.TemporaryDirectory() as tmp_dirname:
               # save tokenizer and load again
               tokenizer.save_pretrained(lowerCamelCase		)
               __A  :					str						     =       SpeechaTextaTokenizer.from_pretrained(lowerCamelCase		)

          self.assertIsNotNone(lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          if not self.test_slow_tokenizer:
               return

          __A  :					int						     =       inspect.signature(self.tokenizer_class.__init__		)
          __A  :					Union[str, Any]						     =       self.get_tokenizer()

          for parameter_name, parameter in signature.parameters.items():
               if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                    self.assertIn(lowerCamelCase ,    tokenizer.init_kwargs		)





'''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        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__()

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          with torch.no_grad():
               __A  :					Optional[Any]						     =       self.vae.encode(tfms.ToTensor()(lowerCamelCase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
               __A  :					int						     =       0.1_8215 * latent.latent_dist.sample()
          return latent


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       self.tokenizer(
              lowerCamelCase ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					str						     =       self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

          __A  :					List[str]						     =       self.tokenizer(
              "" ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					Union[str, Any]						     =       self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

          return torch.cat([uncond_embedding, text_embedding]		)


     def __call__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 0.3 ,    lowerCamelCase = 0.6 ,    lowerCamelCase = 0.5 ,    lowerCamelCase = 42 ,    lowerCamelCase = 50 ,    lowerCamelCase = 7.5 ,    ):


          '''simple docstring'''





          __A  :					Optional[int]						     =       steps - int(kmin * steps		)
          __A  :					Union[str, Any]						     =       steps - int(kmax * steps		)

          __A  :					Any						     =       self.prep_text(lowerCamelCase		)

          self.scheduler.set_timesteps(lowerCamelCase		)

          __A     ,__A  :					List[str]						     =       img.size
          __A  :					str						     =       self.encode(lowerCamelCase		)

          torch.manual_seed(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8) ,    ).to(self.device		)

          __A  :					Dict						     =       self.scheduler.add_noise(
              lowerCamelCase ,    lowerCamelCase ,    timesteps=self.scheduler.timesteps[tmax] ,    )

          __A  :					str						     =       torch.cat([latents] * 2		)

          __A  :					Optional[int]						     =       self.scheduler.scale_model_input(lowerCamelCase ,    self.scheduler.timesteps[tmax]		)

          with torch.no_grad():
               __A  :					Union[str, Any]						     =       self.unet(
                   lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    encoder_hidden_states=lowerCamelCase ,    ).sample

          __A     ,__A  :					int						     =       pred.chunk(2		)
          __A  :					int						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __A  :					Tuple						     =       self.scheduler.step(lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    lowerCamelCase		).prev_sample

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

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

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

                    __A  :					Dict						     =       self.scheduler.scale_model_input(lowerCamelCase ,    lowerCamelCase		)

                    with torch.no_grad():
                         __A  :					Dict						     =       self.unet(
                             lowerCamelCase ,    lowerCamelCase ,    encoder_hidden_states=lowerCamelCase ,    ).sample

                    __A     ,__A  :					str						     =       pred.chunk(2		)
                    __A  :					Optional[Any]						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

                    __A  :					Any						     =       self.scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          return self.decode(lowerCamelCase		)

