
"""simple docstring"""
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase_							:    str         =						transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def  _lowerCAmelCase     (  lowerCamelCase__				:				Optional[int]					)	->     Any:
  if isinstance(lowerCamelCase__,    torch.Tensor					):
    return image
  elif isinstance(lowerCamelCase__,    PIL.Image.Image					):
    _SCREAMING_SNAKE_CASE :					List[Any]				     =			[image]

  _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			[trans(img.convert("RGB"					)					) for img in image]
  _SCREAMING_SNAKE_CASE :					List[Any]				     =			torch.stack(lowerCamelCase__					)
  return image





class       UpperCamelCase   (				_lowercase	):
     def __init__(  self	,  snake_case__	,  snake_case__					):




       """simple docstring"""


       super().__init__()

       # make sure scheduler can always be converted to DDIM
       _SCREAMING_SNAKE_CASE :					List[Any]				     =			DDIMScheduler.from_config(scheduler.config					)

       self.register_modules(unet=__SCREAMING_SNAKE_CASE	,  scheduler=__SCREAMING_SNAKE_CASE					)
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__					):




       """simple docstring"""


       if strength < 0 or strength > 1:
         raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''					)
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__	,  snake_case__	,  snake_case__					):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			min(int(num_inference_steps * strength					)	,  __SCREAMING_SNAKE_CASE					)

       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			max(num_inference_steps - init_timestep	,  0					)
       _SCREAMING_SNAKE_CASE :					List[str]				     =			self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start
     def 					__SCREAMING_SNAKE_CASE				(  self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__=None					):




       """simple docstring"""


       if not isinstance(__SCREAMING_SNAKE_CASE	,  (torch.Tensor, PIL.Image.Image, list)					):
         raise ValueError(
             F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE					)}'''					)

       _SCREAMING_SNAKE_CASE :					str				     =			image.to(device=__SCREAMING_SNAKE_CASE	,  dtype=__SCREAMING_SNAKE_CASE					)

       if isinstance(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE					) and len(__SCREAMING_SNAKE_CASE					) != batch_size:
         raise ValueError(
             F'''You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE					)}, but requested an effective batch'''
             F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

       _SCREAMING_SNAKE_CASE :					int				     =			init_latents.shape
       _SCREAMING_SNAKE_CASE :					Tuple				     =			randn_tensor(__SCREAMING_SNAKE_CASE	,  generator=__SCREAMING_SNAKE_CASE	,  device=__SCREAMING_SNAKE_CASE	,  dtype=__SCREAMING_SNAKE_CASE					)

       # get latents
       print("add noise to latents at timestep"	,  __SCREAMING_SNAKE_CASE					)
       _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			self.scheduler.add_noise(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE					)
       _SCREAMING_SNAKE_CASE :					str				     =			init_latents

       return latents




     @torch.no_grad()
     def __call__(  self	,  snake_case__ = None	,  snake_case__ = 0.8	,  snake_case__ = 1	,  snake_case__ = None	,  snake_case__ = 0.0	,  snake_case__ = 50	,  snake_case__ = None	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):




       """simple docstring"""


       self.check_inputs(__SCREAMING_SNAKE_CASE					)

       # 2. Preprocess image
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			preprocess(__SCREAMING_SNAKE_CASE					)

       # 3. set timesteps
       self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	,  device=self.device					)
       _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE :					Optional[int]				     =			self.get_timesteps(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE	,  self.device					)
       _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			timesteps[:1].repeat(__SCREAMING_SNAKE_CASE					)

       # 4. Prepare latent variables
       _SCREAMING_SNAKE_CASE :					Any				     =			self.prepare_latents(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE	,  self.unet.dtype	,  self.device	,  __SCREAMING_SNAKE_CASE					)
       _SCREAMING_SNAKE_CASE :					int				     =			latents

       # 5. Denoising loop
       for t in self.progress_bar(__SCREAMING_SNAKE_CASE					):
         # 1. predict noise model_output
         _SCREAMING_SNAKE_CASE :					List[Any]				     =			self.unet(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE					).sample

         # 2. predict previous mean of image x_t-1 and add variance depending on eta
         # eta corresponds to η in paper and should be between [0, 1]
         # do x_t -> x_t-1
         _SCREAMING_SNAKE_CASE :					int				     =			self.scheduler.step(
             __SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE	,  eta=__SCREAMING_SNAKE_CASE	,  use_clipped_model_output=__SCREAMING_SNAKE_CASE	,  generator=__SCREAMING_SNAKE_CASE	,  ).prev_sample

       _SCREAMING_SNAKE_CASE :					int				     =			(image / 2 + 0.5).clamp(0	,  1					)
       _SCREAMING_SNAKE_CASE :					int				     =			image.cpu().permute(0	,  2	,  3	,  1					).numpy()
       if output_type == "pil":
         _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			self.numpy_to_pil(__SCREAMING_SNAKE_CASE					)

       if not return_dict:
         return (image, latent_timestep.item())

       return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE					)





"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			'laion/clap-htsat-unfused'
         a__		=			tempfile.mkdtemp()



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return RobertaTokenizer.from_pretrained(self.checkpoint     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         shutil.rmtree(self.tmpdirname   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			self.get_tokenizer()
         a__		=			self.get_feature_extractor()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         processor.save_pretrained(self.tmpdirname   )
         a__		=			ClapProcessor.from_pretrained(self.tmpdirname   )

         self.assertEqual(processor.tokenizer.get_vocab()     ,				tokenizer.get_vocab()   )
         self.assertIsInstance(processor.tokenizer     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,				feature_extractor.to_json_string()   )
         self.assertIsInstance(processor.feature_extractor     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         a__		=			ClapProcessor(tokenizer=self.get_tokenizer()     ,				feature_extractor=self.get_feature_extractor()   )
         processor.save_pretrained(self.tmpdirname   )

         a__		=			self.get_tokenizer(bos_token='(BOS)'     ,				eos_token='(EOS)'   )
         a__		=			self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE     ,				padding_value=1.0   )

         a__		=			ClapProcessor.from_pretrained(
             self.tmpdirname     ,				bos_token='(BOS)'     ,				eos_token='(EOS)'     ,				do_normalize=__SCREAMING_SNAKE_CASE     ,				padding_value=1.0   )

         self.assertEqual(processor.tokenizer.get_vocab()     ,				tokenizer_add_kwargs.get_vocab()   )
         self.assertIsInstance(processor.tokenizer     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,				feature_extractor_add_kwargs.to_json_string()   )
         self.assertIsInstance(processor.feature_extractor     ,				__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			floats_list((3, 1_0_0_0)   )

         a__		=			feature_extractor(__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )
         a__		=			processor(audios=__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )

         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()     ,				input_processor[key].sum()     ,				delta=1e-2   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			'This is a test string'

         a__		=			processor(text=__SCREAMING_SNAKE_CASE   )

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]     ,				encoded_processor[key]   )



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         a__		=			processor.batch_decode(__SCREAMING_SNAKE_CASE   )
         a__		=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(
             processor.model_input_names[2:]     ,				feature_extractor.model_input_names     ,				msg='`processor` and `feature_extractor` model input names do not match'     ,				)


