





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 	UpperCamelCase_						(     __lowerCAmelCase							,     unittest.TestCase    ):
					lowerCAmelCase_							=		MPNetTokenizer
					lowerCAmelCase_							=		MPNetTokenizerFast
					lowerCAmelCase_							=		True
					lowerCAmelCase_							=		True




					def 		lowerCAmelCase	(						self     )						->							Optional[Any]:
										super().setUp()

										_snake_case													= [
										    '[UNK]',
										    '[CLS]',
										    '[SEP]',
										    '[PAD]',
										    '[MASK]',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										_snake_case													= 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 		lowerCAmelCase	(						self ,  lowerCAmelCase_     )						->							Tuple:
										_snake_case													= 'UNwant\u00E9d,running'
										_snake_case													= 'unwanted, running'
										return input_text, output_text




					def 		lowerCAmelCase	(						self     )						->							int:
										_snake_case													= self.tokenizer_class(self.vocab_file     )

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






					@slow
					def 		lowerCAmelCase	(						self     )						->							Dict:
										_snake_case													= self.tokenizer_class.from_pretrained('microsoft/mpnet-base'     )

										_snake_case													= tokenizer.encode('sequence builders' ,  add_special_tokens=_lowerCamelCase     )
										_snake_case													= tokenizer.encode('multi-sequence build' ,  add_special_tokens=_lowerCamelCase     )

										_snake_case													= tokenizer.build_inputs_with_special_tokens(_lowerCamelCase     )
										_snake_case													= tokenizer.build_inputs_with_special_tokens(_lowerCamelCase ,  _lowerCamelCase     )

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




"""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    __UpperCAmelCase						(	__lowerCAmelCase	):

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

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

				def 		_a	(      self	, _lowerCamelCase      ):
										with torch.no_grad():
																lowerCamelCase__					=self.vae.encode(tfms.ToTensor()(_lowerCamelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
																lowerCamelCase__					=0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=(1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
																lowerCamelCase__					=self.vae.decode(_lowerCamelCase      ).sample
										lowerCamelCase__					=(img / 2 + 0.5).clamp(0	, 1      )
										lowerCamelCase__					=img.detach().cpu().permute(0	, 2	, 3	, 1      ).numpy()
										lowerCamelCase__					=(img * 255).round().astype("uint8"      )
										return Image.fromarray(img[0]      )

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=self.tokenizer(
										    _lowerCamelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, 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=_lowerCamelCase	, 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	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = 0.3	, _lowerCamelCase = 0.6	, _lowerCamelCase = 0.5	, _lowerCamelCase = 42	, _lowerCamelCase = 50	, _lowerCamelCase = 7.5	, ):
										lowerCamelCase__					=steps - int(kmin * steps      )
										lowerCamelCase__					=steps - int(kmax * steps      )

										lowerCamelCase__					=self.prep_text(_lowerCamelCase      )

										self.scheduler.set_timesteps(_lowerCamelCase      )

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

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

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

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

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

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

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

										lowerCamelCase__					=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
																												lowerCamelCase__					=self.scheduler.add_noise(
																												    _lowerCamelCase	, _lowerCamelCase	, timesteps=_lowerCamelCase	, )

																												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(_lowerCamelCase	, _lowerCamelCase      )

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

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

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

										return self.decode(_lowerCamelCase      )


