





'''simple docstring'''


import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[Any]								=       ["input_values", "attention_mask"]




 def __init__(						self      ,  _lowerCAmelCase = 1      ,  _lowerCAmelCase = 16000      ,  _lowerCAmelCase = 0.0      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 80      ,  _lowerCAmelCase = 16      ,  _lowerCAmelCase = 64      ,  _lowerCAmelCase = "hann_window"      ,  _lowerCAmelCase = 1.0      ,  _lowerCAmelCase = 80      ,  _lowerCAmelCase = 7600      ,  _lowerCAmelCase = 1E-10      ,  _lowerCAmelCase = 2      ,  _lowerCAmelCase = True      ,  **_lowerCAmelCase      ,  )     ->				Optional[int]:
     super().__init__(feature_size=_lowerCAmelCase      ,  sampling_rate=_lowerCAmelCase      ,  padding_value=_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   do_normalize
     _lowerCAmelCase						      =   return_attention_mask

     _lowerCAmelCase						      =   num_mel_bins
     _lowerCAmelCase						      =   hop_length
     _lowerCAmelCase						      =   win_length
     _lowerCAmelCase						      =   win_function
     _lowerCAmelCase						      =   frame_signal_scale
     _lowerCAmelCase						      =   fmin
     _lowerCAmelCase						      =   fmax
     _lowerCAmelCase						      =   mel_floor
     _lowerCAmelCase						      =   reduction_factor

     _lowerCAmelCase						      =   win_length * sampling_rate // 1000
     _lowerCAmelCase						      =   hop_length * sampling_rate // 1000
     _lowerCAmelCase						      =   optimal_fft_length(self.sample_size		)
     _lowerCAmelCase						      =   (self.n_fft // 2) + 1

     _lowerCAmelCase						      =   window_function(window_length=self.sample_size      ,  name=self.win_function      ,  periodic=_lowerCAmelCase		)

     _lowerCAmelCase						      =   mel_filter_bank(
         num_frequency_bins=self.n_freqs      ,  num_mel_filters=self.num_mel_bins      ,  min_frequency=self.fmin      ,  max_frequency=self.fmax      ,  sampling_rate=self.sampling_rate      ,  norm="slaney"      ,  mel_scale="slaney"      ,  )

     if frame_signal_scale != 1.0:
         warnings.warn(
             "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"      ,  _lowerCAmelCase      ,  )
     if reduction_factor != 2.0:
         warnings.warn(
             "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"      ,  _lowerCAmelCase      ,  )




 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 				_snake_case (						_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = 0.0		)     ->				List[np.ndarray]:
     if attention_mask is not None:
         _lowerCAmelCase						      =   np.array(_lowerCAmelCase      ,  np.intaa		)
         _lowerCAmelCase						      =   []

         for vector, length in zip(_lowerCAmelCase      ,  attention_mask.sum(-1		)		):
             _lowerCAmelCase						      =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7		)
             if length < normed_slice.shape[0]:
                 _lowerCAmelCase						      =   padding_value

             normed_input_values.append(_lowerCAmelCase		)
     else:
         _lowerCAmelCase						      =   [(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

     return normed_input_values




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   spectrogram(
         _lowerCAmelCase      ,  window=self.window      ,  frame_length=self.sample_size      ,  hop_length=self.sample_stride      ,  fft_length=self.n_fft      ,  mel_filters=self.mel_filters      ,  mel_floor=self.mel_floor      ,  log_mel="log10"      ,  )
     return log_mel_spec.T




 def __call__(						self      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     if audio is None and audio_target is None:
         raise ValueError("You must provide either `audio` or `audio_target` values."		)

     if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
             raise ValueError(
                 f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                 f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
                 f''' {self.sampling_rate} and not {sampling_rate}.'''		)
     else:
         logger.warning(
             "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
             "Failing to do so can result in silent errors that might be hard to debug."		)

     if audio is not None:
         _lowerCAmelCase						      =   self._process_audio(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase      ,  )
     else:
         _lowerCAmelCase						      =   None

     if audio_target is not None:
         _lowerCAmelCase						      =   self._process_audio(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  **_lowerCAmelCase      ,  )

         if inputs is None:
             return inputs_target
         else:
             _lowerCAmelCase						      =   inputs_target["input_values"]
             _lowerCAmelCase						      =   inputs_target.get("attention_mask"		)
             if decoder_attention_mask is not None:
                 _lowerCAmelCase						      =   decoder_attention_mask

     return inputs




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     _lowerCAmelCase						      =   isinstance(_lowerCAmelCase      ,  np.ndarray		) and len(speech.shape		) > 1
     if is_batched_numpy and len(speech.shape		) > 2:
         raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''		)
     _lowerCAmelCase						      =   is_batched_numpy or (
         isinstance(_lowerCAmelCase      ,  (list, tuple)		) and (isinstance(speech[0]      ,  (np.ndarray, tuple, list)		))
     )

     if is_batched:
         _lowerCAmelCase						      =   [np.asarray(_lowerCAmelCase      ,  dtype=np.floataa		) for speech in speech]
     elif not is_batched and not isinstance(_lowerCAmelCase      ,  np.ndarray		):
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase      ,  dtype=np.floataa		)
     elif isinstance(_lowerCAmelCase      ,  np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
         _lowerCAmelCase						      =   speech.astype(np.floataa		)

     # always return batch
     if not is_batched:
         _lowerCAmelCase						      =   [speech]

     # needed to make pad() work on spectrogram inputs
     _lowerCAmelCase						      =   self.feature_size

     # convert into correct format for padding
     if is_target:
         _lowerCAmelCase						      =   [self._extract_mel_features(_lowerCAmelCase		) for waveform in speech]
         _lowerCAmelCase						      =   BatchFeature({"input_values": features}		)
         _lowerCAmelCase						      =   self.num_mel_bins
     else:
         _lowerCAmelCase						      =   BatchFeature({"input_values": speech}		)

     _lowerCAmelCase						      =   self.pad(
         _lowerCAmelCase      ,  padding=_lowerCAmelCase      ,  max_length=_lowerCAmelCase      ,  truncation=_lowerCAmelCase      ,  pad_to_multiple_of=_lowerCAmelCase      ,  return_attention_mask=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )

     _lowerCAmelCase						      =   feature_size_hack

     # convert input values to correct format
     _lowerCAmelCase						      =   padded_inputs["input_values"]
     if not isinstance(input_values[0]      ,  np.ndarray		):
         _lowerCAmelCase						      =   [np.asarray(_lowerCAmelCase      ,  dtype=np.floataa		) for array in input_values]
     elif (
         not isinstance(_lowerCAmelCase      ,  np.ndarray		)
         and isinstance(input_values[0]      ,  np.ndarray		)
         and input_values[0].dtype is np.dtype(np.floataa		)
     ):
         _lowerCAmelCase						      =   [array.astype(np.floataa		) for array in input_values]
     elif isinstance(_lowerCAmelCase      ,  np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
         _lowerCAmelCase						      =   input_values.astype(np.floataa		)

     # convert attention_mask to correct format
     _lowerCAmelCase						      =   padded_inputs.get("attention_mask"		)
     if attention_mask is not None:
         _lowerCAmelCase						      =   [np.asarray(_lowerCAmelCase      ,  dtype=np.intaa		) for array in attention_mask]

     # zero-mean and unit-variance normalization
     if not is_target and self.do_normalize:
         _lowerCAmelCase						      =   (
             attention_mask
             if self._get_padding_strategies(_lowerCAmelCase      ,  max_length=_lowerCAmelCase		) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         _lowerCAmelCase						      =   self.zero_mean_unit_var_norm(
             padded_inputs["input_values"]      ,  attention_mask=_lowerCAmelCase      ,  padding_value=self.padding_value		)

     if return_tensors is not None:
         _lowerCAmelCase						      =   padded_inputs.convert_to_tensors(_lowerCAmelCase		)

     return padded_inputs





 def 				_snake_case (						self		)     ->				Dict[str, Any]:
     _lowerCAmelCase						      =   super().to_dict()

     # Don't serialize these as they are derived from the other properties.
     _lowerCAmelCase						      =   ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
     for name in names:
         if name in output:
             del output[name]

     return output









'''simple docstring'''


import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[Any]								=       AutoencoderKL
 __lowerCamelCase    :				List[Any]								=       "sample"
 __lowerCamelCase    :				Tuple								=       1e-2




 @property
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   4
     _lowerCAmelCase						      =   3
     _lowerCAmelCase						      =   (32, 32)

     _lowerCAmelCase						      =   floats_tensor((batch_size, num_channels) + sizes		).to(_lowerCAmelCase		)

     return {"sample": image}




 @property
 def 				_snake_case (						self		)     ->				Any:
     return (3, 32, 32)




 @property
 def 				_snake_case (						self		)     ->				List[Any]:
     return (3, 32, 32)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   {
         "block_out_channels": [32, 64],
         "in_channels": 3,
         "out_channels": 3,
         "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
         "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
         "latent_channels": 4,
     }
     _lowerCAmelCase						      =   self.dummy_input
     return init_dict, inputs_dict




 def 				_snake_case (						self		)     ->				Optional[int]:
     pass




 def 				_snake_case (						self		)     ->				Any:
     pass




 @unittest.skipIf(torch_device == "mps"      ,  "Gradient checkpointing skipped on MPS"		)
 def 				_snake_case (						self		)     ->				str:
     # enable deterministic behavior for gradient checkpointing
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.prepare_init_args_and_inputs_for_common()
     _lowerCAmelCase						      =   self.model_class(**_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)

     assert not model.is_gradient_checkpointing and model.training

     _lowerCAmelCase						      =   model(**_lowerCAmelCase		).sample
     # run the backwards pass on the model. For backwards pass, for simplicity purpose,
     # we won't calculate the loss and rather backprop on out.sum()
     model.zero_grad()

     _lowerCAmelCase						      =   torch.randn_like(_lowerCAmelCase		)
     _lowerCAmelCase						      =   (out - labels).mean()
     loss.backward()

     # re-instantiate the model now enabling gradient checkpointing
     _lowerCAmelCase						      =   self.model_class(**_lowerCAmelCase		)
     # clone model
     model_a.load_state_dict(model.state_dict()		)
     model_a.to(_lowerCAmelCase		)
     model_a.enable_gradient_checkpointing()

     assert model_a.is_gradient_checkpointing and model_a.training

     _lowerCAmelCase						      =   model_a(**_lowerCAmelCase		).sample
     # run the backwards pass on the model. For backwards pass, for simplicity purpose,
     # we won't calculate the loss and rather backprop on out.sum()
     model_a.zero_grad()
     _lowerCAmelCase						      =   (out_a - labels).mean()
     loss_a.backward()

     # compare the output and parameters gradients
     self.assertTrue((loss - loss_a).abs() < 1E-5		)
     _lowerCAmelCase						      =   dict(model.named_parameters()		)
     _lowerCAmelCase						      =   dict(model_a.named_parameters()		)
     for name, param in named_params.items():
         self.assertTrue(torch_all_close(param.grad.data      ,  named_params_a[name].grad.data      ,  atol=5E-5		)		)




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"      ,  output_loading_info=_lowerCAmelCase		)
     self.assertIsNotNone(_lowerCAmelCase		)
     self.assertEqual(len(loading_info["missing_keys"]		)      ,  0		)

     model.to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model(**self.dummy_input		)

     assert image is not None, "Make sure output is not None"





 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		)
     _lowerCAmelCase						      =   model.to(_lowerCAmelCase		)
     model.eval()

     if torch_device == "mps":
         _lowerCAmelCase						      =   torch.manual_seed(0		)
     else:
         _lowerCAmelCase						      =   torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)

     _lowerCAmelCase						      =   torch.randn(
         1      ,  model.config.in_channels      ,  model.config.sample_size      ,  model.config.sample_size      ,  generator=torch.manual_seed(0		)      ,  )
     _lowerCAmelCase						      =   image.to(_lowerCAmelCase		)
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  sample_posterior=_lowerCAmelCase      ,  generator=_lowerCAmelCase		).sample

     _lowerCAmelCase						      =   output[0, -1, -3:, -3:].flatten().cpu()

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     if torch_device == "mps":
         _lowerCAmelCase						      =   torch.tensor(
             [
                 -4.0078E-01,
                 -3.8323E-04,
                 -1.2681E-01,
                 -1.1462E-01,
                 2.0095E-01,
                 1.0893E-01,
                 -8.8247E-02,
                 -3.0361E-01,
                 -9.8644E-03,
             ]		)
     elif torch_device == "cpu":
         _lowerCAmelCase						      =   torch.tensor(
             [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
     else:
         _lowerCAmelCase						      =   torch.tensor(
             [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]		)

     self.assertTrue(torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  rtol=1E-2		)		)







@slow
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Union[str, Any]:
     return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCAmelCase		) for s in shape]		)}.npy'''




 def 				_snake_case (						self		)     ->				List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=(4, 3, 512, 512)      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _lowerCAmelCase						      =   torch.floataa if fpaa else torch.floataa
     _lowerCAmelCase						      =   torch.from_numpy(load_hf_numpy(self.get_file_format(_lowerCAmelCase      ,  _lowerCAmelCase		)		)		).to(_lowerCAmelCase		).to(_lowerCAmelCase		)
     return image




 def 				_snake_case (						self      ,  _lowerCAmelCase="CompVis/stable-diffusion-v1-4"      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _lowerCAmelCase						      =   "fp16" if fpaa else None
     _lowerCAmelCase						      =   torch.floataa if fpaa else torch.floataa

     _lowerCAmelCase						      =   AutoencoderKL.from_pretrained(
         _lowerCAmelCase      ,  subfolder="vae"      ,  torch_dtype=_lowerCAmelCase      ,  revision=_lowerCAmelCase      ,  )
     model.to(_lowerCAmelCase		).eval()

     return model




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				str:
     if torch_device == "mps":
         return torch.manual_seed(_lowerCAmelCase		)
     return torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)




 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  sample_posterior=_lowerCAmelCase		).sample

     assert sample.shape == image.shape

     _lowerCAmelCase						      =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
     _lowerCAmelCase						      =   torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice		)

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=3E-3		)




 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
         [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  sample_posterior=_lowerCAmelCase		).sample

     assert sample.shape == image.shape

     _lowerCAmelCase						      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
     _lowerCAmelCase						      =   torch.tensor(_lowerCAmelCase		)

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-2		)




 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
         [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
         # fmt: on
     ]		)
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase		).sample

     assert sample.shape == image.shape

     _lowerCAmelCase						      =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
     _lowerCAmelCase						      =   torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice		)

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=3E-3		)




 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
         [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     _lowerCAmelCase						      =   sample[-1, -2:, :2, -2:].flatten().cpu()
     _lowerCAmelCase						      =   torch.tensor(_lowerCAmelCase		)

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-3		)




 @parameterized.expand(
     [
         # fmt: off
         [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
         [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
         # fmt: on
     ]		)
 @require_torch_gpu
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)      ,  fpaa=_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     _lowerCAmelCase						      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
     _lowerCAmelCase						      =   torch.tensor(_lowerCAmelCase		)

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=5E-3		)




 @parameterized.expand([(13,), (16,), (27,)]		)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()      ,  reason="xformers is not required when using PyTorch 2.0."		)
 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)      ,  fpaa=_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-1		)




 @parameterized.expand([(13,), (16,), (37,)]		)
 @require_torch_gpu
 @unittest.skipIf(not is_xformers_available()      ,  reason="xformers is not required when using PyTorch 2.0."		)
 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=1E-2		)





 @parameterized.expand(
     [
         # fmt: off
         [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
         [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
         # fmt: on
     ]		)
 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.encode(_lowerCAmelCase		).latent_dist
         _lowerCAmelCase						      =   dist.sample(generator=_lowerCAmelCase		)

     assert list(sample.shape		) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

     _lowerCAmelCase						      =   sample[0, -1, -3:, -3:].flatten().cpu()
     _lowerCAmelCase						      =   torch.tensor(_lowerCAmelCase		)

     _lowerCAmelCase						      =   3E-3 if torch_device != "mps" else 1E-2
     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=_lowerCAmelCase		)


