

'''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__					( A__						):





  def __init__(	self   :			Any							,      _a   :			AutoencoderKL							,      _a   :			CLIPTextModel							,      _a   :			CLIPTokenizer							,      _a   :			UNetaDConditionModel							,      _a   :			Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]							,      )			->					int:





       '''simple docstring'''






       super().__init__()

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





  def 		A						(	self   :			Optional[int]							,      _a   :			str				)			->					List[Any]:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE												=self.vae.encode(tfms.ToTensor()(_a				).unsqueeze(0				).to(self.device				) * 2 - 1				)
            _SCREAMING_SNAKE_CASE												=0.1_82_15 * latent.latent_dist.sample()
       return latent





  def 		A						(	self   :			Tuple							,      _a   :			Tuple				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=(1 / 0.1_82_15) * latent
       with torch.no_grad():
            _SCREAMING_SNAKE_CASE												=self.vae.decode(_a				).sample
       _SCREAMING_SNAKE_CASE												=(img / 2 + 0.5).clamp(0							,      1				)
       _SCREAMING_SNAKE_CASE												=img.detach().cpu().permute(0							,      2							,      3							,      1				).numpy()
       _SCREAMING_SNAKE_CASE												=(img * 255).round().astype('uint8'				)
       return Image.fromarray(img[0]				)





  def 		A						(	self   :			Optional[int]							,      _a   :			Union[str, Any]				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.tokenizer(
           _a							,      padding='max_length'							,      max_length=self.tokenizer.model_max_length							,      truncation=_a							,      return_tensors='pt'							,      )

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

       _SCREAMING_SNAKE_CASE												=self.tokenizer(
           ''							,      padding='max_length'							,      max_length=self.tokenizer.model_max_length							,      truncation=_a							,      return_tensors='pt'							,      )

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

       return torch.cat([uncond_embedding, text_embedding]				)





  def __call__(	self   :			Tuple							,      _a   :			Image.Image							,      _a   :			str							,      _a   :			float = 0.3							,      _a   :			float = 0.6							,      _a   :			float = 0.5							,      _a   :			int = 42							,      _a   :			int = 50							,      _a   :			float = 7.5							,      )			->					Image.Image:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=steps - int(kmin * steps				)
       _SCREAMING_SNAKE_CASE												=steps - int(kmax * steps				)

       _SCREAMING_SNAKE_CASE												=self.prep_text(_a				)

       self.scheduler.set_timesteps(_a				)

       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=img.size
       _SCREAMING_SNAKE_CASE												=self.encode(_a				)

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

       _SCREAMING_SNAKE_CASE												=self.scheduler.add_noise(
           _a							,      _a							,      timesteps=self.scheduler.timesteps[tmax]							,      )

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

       _SCREAMING_SNAKE_CASE												=self.scheduler.scale_model_input(_a							,      self.scheduler.timesteps[tmax]				)

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

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

       _SCREAMING_SNAKE_CASE												=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
                      _SCREAMING_SNAKE_CASE												=self.scheduler.add_noise(
                          _a							,      _a							,      timesteps=_a							,      )

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

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

                 _SCREAMING_SNAKE_CASE												=self.scheduler.scale_model_input(_a							,      _a				)

                 with torch.no_grad():
                      _SCREAMING_SNAKE_CASE												=self.unet(
                          _a							,      _a							,      encoder_hidden_states=_a							,      ).sample

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

                 _SCREAMING_SNAKE_CASE												=self.scheduler.step(_a							,      _a							,      _a				).prev_sample

       return self.decode(_a				)



'''simple docstring'''
import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class 							A__					( unittest.TestCase						):
  A__					=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  A__					=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





  def 		A						(	self   :			Optional[Any]							,      _a   :			Tuple							,      _a   :			Union[str, Any]							,      _a   :			str				)			->					List[Any]:





       '''simple docstring'''






       if isinstance(model.config							,      _a				):
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=list(tokenizer.lang_code_to_id.keys()				)[:2]
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a							,      src_lang=_a							,      tgt_lang=_a				)
       else:
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a				)
       return translator, ["Some string", "Some other text"]





  def 		A						(	self   :			Union[str, Any]							,      _a   :			List[Any]							,      _a   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=translator('Some string'				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}]				)

       _SCREAMING_SNAKE_CASE												=translator(['Some string']				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}]				)

       _SCREAMING_SNAKE_CASE												=translator(['Some string', 'other string']				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}, {'translation_text': ANY(_a				)}]				)





  @require_torch
  def 		A						(	self   :			Dict				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Tuple				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_torch
  def 		A						(	self   :			Any				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Dict				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )







class 							A__					( unittest.TestCase						):





  @require_torch
  @slow
  def 		A						(	self   :			int				)			->					Optional[Any]:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation_cn_to_ar'				)

       # but we do for this one
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation_en_to_de'				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'en'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'de'				)





  @require_torch
  @slow
  def 		A						(	self   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)
       _SCREAMING_SNAKE_CASE												=MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)

       _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a							,      tokenizer=_a				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(_a				):
            translator('This is a test'				)

       _SCREAMING_SNAKE_CASE												=translator('This is a test'							,      src_lang='en_XX'							,      tgt_lang='ar_AR'				)
       self.assertEqual(_a							,      [{'translation_text': 'هذا إختبار'}]				)

       _SCREAMING_SNAKE_CASE												=translator('This is a test'							,      src_lang='en_XX'							,      tgt_lang='hi_IN'				)
       self.assertEqual(_a							,      [{'translation_text': 'यह एक परीक्षण है'}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a							,      tokenizer=_a							,      src_lang='en_XX'							,      tgt_lang='ar_AR'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test'				)
       self.assertEqual(_a							,      [{'translation_text': 'هذا إختبار'}]				)





  @require_torch
  def 		A						(	self   :			int				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation_cn_to_ar'							,      model=_a				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'cn'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'ar'				)





  @require_torch
  def 		A						(	self   :			str				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       with pytest.warns(_a							,      match=r'.*translation_en_to_de.*'				):
            _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a				)
       self.assertEqual(translator.task							,      'translation_en_to_de'				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'en'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'de'				)





  @require_torch
  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation'				)
