



"""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


snake_case   					=				transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def 			snake_case		(		lowerCAmelCase_	)						->				Tuple:
  if isinstance(lowerCAmelCase_  ,						torch.Tensor	):
    return image
  elif isinstance(lowerCAmelCase_  ,						PIL.Image.Image	):
    _snake_case   	=      [image]

  _snake_case   	=      [trans(img.convert('''RGB'''	)	) for img in image]
  _snake_case   	=      torch.stack(lowerCAmelCase_	)
  return image







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	str							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     super().__init__()

     # make sure scheduler can always be converted to DDIM
     _snake_case   	=      DDIMScheduler.from_config(scheduler.config				)

     self.register_modules(unet=__lowerCamelCase							,       scheduler=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	Tuple				):



     """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    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     # get the original timestep using init_timestep
     _snake_case   	=      min(int(num_inference_steps * strength				)							,       __lowerCamelCase				)

     _snake_case   	=      max(num_inference_steps - init_timestep							,       0				)
     _snake_case   	=      self.scheduler.timesteps[t_start:]

     return timesteps, num_inference_steps - t_start



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]=None				):



     """simple docstring"""
     if not isinstance(__lowerCamelCase							,       (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(__lowerCamelCase				)}"""				)

     _snake_case   	=      image.to(device=__lowerCamelCase							,       dtype=__lowerCamelCase				)

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

     _snake_case   	=      init_latents.shape
     _snake_case   	=      randn_tensor(__lowerCamelCase							,       generator=__lowerCamelCase							,       device=__lowerCamelCase							,       dtype=__lowerCamelCase				)

     # get latents
     print('''add noise to latents at timestep'''							,       __lowerCamelCase				)
     _snake_case   	=      self.scheduler.add_noise(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)
     _snake_case   	=      init_latents

     return latents





   @torch.no_grad()
   def __call__(       self    :	int							,       __lowerCamelCase    :	Union[torch.FloatTensor, PIL.Image.Image] = None							,       __lowerCamelCase    :	float = 0.8							,       __lowerCamelCase    :	int = 1							,       __lowerCamelCase    :	Optional[Union[torch.Generator, List[torch.Generator]]] = None							,       __lowerCamelCase    :	float = 0.0							,       __lowerCamelCase    :	int = 5_0							,       __lowerCamelCase    :	Optional[bool] = None							,       __lowerCamelCase    :	Optional[str] = "pil"							,       __lowerCamelCase    :	bool = True							,       ):



     """simple docstring"""
     self.check_inputs(__lowerCamelCase				)

     # 2. Preprocess image
     _snake_case   	=      preprocess(__lowerCamelCase				)

     # 3. set timesteps
     self.scheduler.set_timesteps(__lowerCamelCase							,       device=self.device				)
     _snake_case	,     _snake_case   	=      self.get_timesteps(__lowerCamelCase							,       __lowerCamelCase							,       self.device				)
     _snake_case   	=      timesteps[:1].repeat(__lowerCamelCase				)

     # 4. Prepare latent variables
     _snake_case   	=      self.prepare_latents(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       self.unet.dtype							,       self.device							,       __lowerCamelCase				)
     _snake_case   	=      latents

     # 5. Denoising loop
     for t in self.progress_bar(__lowerCamelCase				):
       # 1. predict noise model_output
       _snake_case   	=      self.unet(__lowerCamelCase							,       __lowerCamelCase				).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
       _snake_case   	=      self.scheduler.step(
           __lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase							,       eta=__lowerCamelCase							,       use_clipped_model_output=__lowerCamelCase							,       generator=__lowerCamelCase							,       ).prev_sample

     _snake_case   	=      (image / 2 + 0.5).clamp(0							,       1				)
     _snake_case   	=      image.cpu().permute(0							,       2							,       3							,       1				).numpy()
     if output_type == "pil":
       _snake_case   	=      self.numpy_to_pil(__lowerCamelCase				)

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

     return ImagePipelineOutput(images=__lowerCamelCase				)










"""simple docstring"""






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset

snake_case   					=				random.Random()




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1.0  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				List[str]:
  if rng is None:
    _snake_case   	=      global_rng

  _snake_case   	=      []
  for batch_idx in range(shape[0]	):
    values.append([]	)
    for _ in range(shape[1]	):
      values[-1].append(rng.random() * scale	)

  return values



class     UpperCAmelCase    (	unittest.TestCase  ):



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[Any]=7							,       __lowerCamelCase    :	List[Any]=4_0_0							,       __lowerCamelCase    :	Any=2_0_0_0							,       __lowerCamelCase    :	Any=2_0_4_8							,       __lowerCamelCase    :	Any=1_2_8							,       __lowerCamelCase    :	Any=1							,       __lowerCamelCase    :	Optional[int]=5_1_2							,       __lowerCamelCase    :	Tuple=3_0							,       __lowerCamelCase    :	List[Any]=4_4_1_0_0							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      min_seq_length
     _snake_case   	=      max_seq_length
     _snake_case   	=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     _snake_case   	=      spectrogram_length
     _snake_case   	=      feature_size
     _snake_case   	=      num_audio_channels
     _snake_case   	=      hop_length
     _snake_case   	=      chunk_length
     _snake_case   	=      sampling_rate



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     return {
         "spectrogram_length": self.spectrogram_length,
         "feature_size": self.feature_size,
         "num_audio_channels": self.num_audio_channels,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "sampling_rate": self.sampling_rate,
     }





   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Any=False							,       __lowerCamelCase    :	int=False				):



     """simple docstring"""
     def _flatten(__lowerCamelCase    :	List[str]				):
       return list(itertools.chain(*__lowerCamelCase				)				)

     if equal_length:
       _snake_case   	=      [floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
     else:
       # make sure that inputs increase in size
       _snake_case   	=      [
           floats_list((x, self.feature_size)				)
           for x in range(self.min_seq_length							,       self.max_seq_length							,       self.seq_length_diff				)
       ]
     if numpify:
       _snake_case   	=      [np.asarray(__lowerCamelCase				) for x in speech_inputs]
     return speech_inputs



@require_torch
@require_torchaudio
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Tuple           =  TvltFeatureExtractor



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      TvltFeatureExtractionTester(self				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''spectrogram_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''feature_size'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''num_audio_channels'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''hop_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''chunk_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''sampling_rate'''				)				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      feat_extract_first.save_pretrained(__lowerCamelCase				)[0]
       check_json_file_has_correct_format(__lowerCamelCase				)
       _snake_case   	=      self.feature_extraction_class.from_pretrained(__lowerCamelCase				)

     _snake_case   	=      feat_extract_first.to_dict()
     _snake_case   	=      feat_extract_second.to_dict()
     _snake_case   	=      dict_first.pop('''mel_filters'''				)
     _snake_case   	=      dict_second.pop('''mel_filters'''				)
     self.assertTrue(np.allclose(__lowerCamelCase							,       __lowerCamelCase				)				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     with tempfile.TemporaryDirectory() as tmpdirname:
       _snake_case   	=      os.path.join(__lowerCamelCase							,       '''feat_extract.json'''				)
       feat_extract_first.to_json_file(__lowerCamelCase				)
       _snake_case   	=      self.feature_extraction_class.from_json_file(__lowerCamelCase				)

     _snake_case   	=      feat_extract_first.to_dict()
     _snake_case   	=      feat_extract_second.to_dict()
     _snake_case   	=      dict_first.pop('''mel_filters'''				)
     _snake_case   	=      dict_second.pop('''mel_filters'''				)
     self.assertTrue(np.allclose(__lowerCamelCase							,       __lowerCamelCase				)				)
     self.assertEqual(__lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     # Initialize feature_extractor
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     # create three inputs of length 800, 1000, and 1200
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      [np.asarray(__lowerCamelCase				) for speech_input in speech_inputs]

     # Test not batched input
     _snake_case   	=      feature_extractor(np_speech_inputs[0]							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).audio_values

     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

     # Test batched
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).audio_values

     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

     # Test audio masking
     _snake_case   	=      feature_extractor(
         __lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0							,       mask_audio=__lowerCamelCase				).audio_values

     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

     # Test 2-D numpy arrays are batched.
     _snake_case   	=      [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     _snake_case   	=      np.asarray(__lowerCamelCase				)
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).audio_values
     self.assertTrue(encoded_audios.ndim == 4				)
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''							,       '''clean'''							,       split='''validation'''				)
     # automatic decoding with librispeech
     _snake_case   	=      ds.sort('''id'''				).select(range(__lowerCamelCase				)				)[:num_samples]['''audio''']

     return [x["array"] for x in speech_samples]





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self._load_datasamples(1				)
     _snake_case   	=      TvltFeatureExtractor()
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''pt'''				).audio_values

     self.assertEquals(audio_values.shape							,       (1, 1, 1_9_2, 1_2_8)				)

     _snake_case   	=      torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]				)
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,       __lowerCamelCase							,       atol=1E-4				)				)





