



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    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case		,							):



									"""simple docstring"""





									super().__init__()

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




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									with torch.no_grad():
														_lowerCAmelCase         =       self.vae.encode(tfms.ToTensor()(_snake_case						).unsqueeze(0						).to(self.device						) * 2 - 1						)
														_lowerCAmelCase         =       0.1_8215 * latent.latent_dist.sample()
									return latent




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       (1 / 0.1_8215) * latent
									with torch.no_grad():
														_lowerCAmelCase         =       self.vae.decode(_snake_case						).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 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.tokenizer(
									    _snake_case		,							padding="""max_length"""		,							max_length=self.tokenizer.model_max_length		,							truncation=_snake_case		,							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=_snake_case		,							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		,							_snake_case		,							_snake_case		,							_snake_case = 0.3		,							_snake_case = 0.6		,							_snake_case = 0.5		,							_snake_case = 42		,							_snake_case = 50		,							_snake_case = 7.5		,							):



									"""simple docstring"""





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

									_lowerCAmelCase         =       self.prep_text(_snake_case						)

									self.scheduler.set_timesteps(_snake_case						)

									_lowerCAmelCase      ,      _lowerCAmelCase         =       img.size
									_lowerCAmelCase         =       self.encode(_snake_case						)

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

									_lowerCAmelCase         =       self.scheduler.add_noise(
									    _snake_case		,							_snake_case		,							timesteps=self.scheduler.timesteps[tmax]		,							)

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

									_lowerCAmelCase         =       self.scheduler.scale_model_input(_snake_case		,							self.scheduler.timesteps[tmax]						)

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

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

									_lowerCAmelCase         =       self.scheduler.step(_snake_case		,							self.scheduler.timesteps[tmax]		,							_snake_case						).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(
																								    _snake_case		,							_snake_case		,							timesteps=_snake_case		,							)

																								_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(_snake_case		,							_snake_case						)

																			with torch.no_grad():
																								_lowerCAmelCase         =       self.unet(
																								    _snake_case		,							_snake_case		,							encoder_hidden_states=_snake_case		,							).sample

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

																			_lowerCAmelCase         =       self.scheduler.step(_snake_case		,							_snake_case		,							_snake_case						).prev_sample

									return self.decode(_snake_case						)







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  lowerCamelCase__  (			unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     SCREAMING_SNAKE_CASE__      =     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      if isinstance(model.config						,	UpperCAmelCase			):
       _lowercase ,       _lowercase				 =list(tokenizer.lang_code_to_id.keys()			)[:2]
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase						,	src_lang=UpperCAmelCase						,	tgt_lang=UpperCAmelCase			)
      else:
       _lowercase				 =TranslationPipeline(model=UpperCAmelCase						,	tokenizer=UpperCAmelCase			)
      return translator, ["Some string", "Some other text"]





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =translator('''Some string'''			)
      self.assertEqual(UpperCAmelCase						,	[{'''translation_text''': ANY(UpperCAmelCase			)}]			)

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

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





     @require_torch
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_tf
     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =pipeline('''translation_en_to_ro'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                      ''' Beide Beide'''
                  )
              }
          ]						,	)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''pt'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''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			)	->  Optional[int]:
      _lowercase				 =pipeline('''translation_en_to_de'''						,	model='''patrickvonplaten/t5-tiny-random'''						,	framework='''tf'''			)
      _lowercase				 =translator('''This is a test string'''						,	max_length=2_0			)
      self.assertEqual(
          UpperCAmelCase						,	[
              {
                  '''translation_text''': (
                      '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                      ''' urine urine urine urine urine urine urine'''
                  )
              }
          ]						,	)







class  lowerCamelCase__  (			unittest.TestCase):





     @require_torch
     @slow
     def 	__A   (self			)	->  Optional[Any]:
      # We don't provide a default for this pair
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation_cn_to_ar'''			)

      # but we do for this one
      _lowercase				 =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]:
      _lowercase				 =MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)
      _lowercase				 =MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt'''			)

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

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

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

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





     @require_torch
     def 	__A   (self			)	->  Tuple:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      _lowercase				 =pipeline(task='''translation_cn_to_ar'''						,	model=UpperCAmelCase			)
      self.assertEqual(translator._preprocess_params['''src_lang''']						,	'''cn'''			)
      self.assertEqual(translator._preprocess_params['''tgt_lang''']						,	'''ar'''			)





     @require_torch
     def 	__A   (self			)	->  str:
      _lowercase				 ='''patrickvonplaten/t5-tiny-random'''
      with pytest.warns(UpperCAmelCase						,	match=R'''.*translation_en_to_de.*'''			):
       _lowercase				 =pipeline(task='''translation'''						,	model=UpperCAmelCase			)
      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			)	->  List[str]:
      with self.assertRaises(UpperCAmelCase			):
       pipeline(task='''translation'''			)

