




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			(		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(    self  :   Optional[Any]	,  A__  :   AutoencoderKL	,  A__  :   CLIPTextModel	,  A__  :   CLIPTokenizer	,  A__  :   UNetaDConditionModel	,  A__  :   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]	,  )				->       Any:
           super().__init__()

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




     def  __snake_case   (    self  :   str	,  A__  :   List[Any]			)				->       Any:
           with torch.no_grad():
                 lowerCamelCase__        =				self.vae.encode(tfms.ToTensor()(A__			).unsqueeze(0			).to(self.device			) * 2 - 1			)
                 lowerCamelCase__        =				0.18215 * latent.latent_dist.sample()
           return latent




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   str			)				->       Tuple:
           lowerCamelCase__        =				(1 / 0.18215) * latent
           with torch.no_grad():
                 lowerCamelCase__        =				self.vae.decode(A__			).sample
           lowerCamelCase__        =				(img / 2 + 0.5).clamp(0	,  1			)
           lowerCamelCase__        =				img.detach().cpu().permute(0	,  2	,  3	,  1			).numpy()
           lowerCamelCase__        =				(img * 2_5_5).round().astype('uint8'			)
           return Image.fromarray(img[0]			)




     def  __snake_case   (    self  :   Dict	,  A__  :   Union[str, Any]			)				->       List[str]:
           lowerCamelCase__        =				self.tokenizer(
               A__	,  padding='max_length'	,  max_length=self.tokenizer.model_max_length	,  truncation=A__	,  return_tensors='pt'	,  )

           lowerCamelCase__        =				self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

           lowerCamelCase__        =				self.tokenizer(
               ''	,  padding='max_length'	,  max_length=self.tokenizer.model_max_length	,  truncation=A__	,  return_tensors='pt'	,  )

           lowerCamelCase__        =				self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

           return torch.cat([uncond_embedding, text_embedding]			)




     def __call__(    self  :   str	,  A__  :   Image.Image	,  A__  :   str	,  A__  :   float = 0.3	,  A__  :   float = 0.6	,  A__  :   float = 0.5	,  A__  :   int = 4_2	,  A__  :   int = 5_0	,  A__  :   float = 7.5	,  )				->       Image.Image:
           lowerCamelCase__        =				steps - int(kmin * steps			)
           lowerCamelCase__        =				steps - int(kmax * steps			)

           lowerCamelCase__        =				self.prep_text(A__			)

           self.scheduler.set_timesteps(A__			)

           lowerCamelCase__  ,		lowerCamelCase__        =				img.size
           lowerCamelCase__        =				self.encode(A__			)

           torch.manual_seed(A__			)
           lowerCamelCase__        =				torch.randn(
               (1, self.unet.config.in_channels, height // 8, width // 8)	,  ).to(self.device			)

           lowerCamelCase__        =				self.scheduler.add_noise(
               A__	,  A__	,  timesteps=self.scheduler.timesteps[tmax]	,  )

           lowerCamelCase__        =				torch.cat([latents] * 2			)

           lowerCamelCase__        =				self.scheduler.scale_model_input(A__	,  self.scheduler.timesteps[tmax]			)

           with torch.no_grad():
                 lowerCamelCase__        =				self.unet(
                     A__	,  self.scheduler.timesteps[tmax]	,  encoder_hidden_states=A__	,  ).sample

           lowerCamelCase__  ,		lowerCamelCase__        =				pred.chunk(2			)
           lowerCamelCase__        =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

           lowerCamelCase__        =				self.scheduler.step(A__	,  self.scheduler.timesteps[tmax]	,  A__			).prev_sample

           for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
                 if i > tmax:
                       if i < tmin:  # layout generation phase
                             lowerCamelCase__        =				self.scheduler.add_noise(
                                 A__	,  A__	,  timesteps=A__	,  )

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

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

                       lowerCamelCase__        =				self.scheduler.scale_model_input(A__	,  A__			)

                       with torch.no_grad():
                             lowerCamelCase__        =				self.unet(
                                 A__	,  A__	,  encoder_hidden_states=A__	,  ).sample

                       lowerCamelCase__  ,		lowerCamelCase__        =				pred.chunk(2			)
                       lowerCamelCase__        =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

                       lowerCamelCase__        =				self.scheduler.step(A__	,  A__	,  A__			).prev_sample

           return self.decode(A__			)





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE    :   List[str]       =logging.getLogger()

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowerCamelCase__        =				parser.parse_args()
      return args.f






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				logging.StreamHandler(sys.stdout			)
           logger.addHandler(A__			)

           lowerCamelCase__        =				self.get_auto_remove_tmp_dir()
           lowerCamelCase__        =				F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

           with patch.object(A__	,  'argv'	,  A__			):
                 lowerCamelCase__        =				run_glue_with_pabee.main()
                 for value in result.values():
                       self.assertGreaterEqual(A__	,  0.75			)