
"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class __lowerCAmelCase		(				unittest.TestCase      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	UNetaDModel(
          sample_size=(32, 64)     ,					in_channels=1     ,					out_channels=1     ,					layers_per_block=2     ,					block_out_channels=(128, 128)     ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D')     ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D')     ,					)
      return model





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	UNetaDConditionModel(
          sample_size=(64, 32)     ,					in_channels=1     ,					out_channels=1     ,					layers_per_block=2     ,					block_out_channels=(128, 128)     ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')     ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')     ,					cross_attention_dim=10     ,					)
      return model





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	AutoencoderKL(
          sample_size=(128, 64)     ,					in_channels=1     ,					out_channels=1     ,					latent_channels=1     ,					layers_per_block=2     ,					block_out_channels=(128, 128)     ,					down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')     ,					up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')     ,					)
      __UpperCamelCase      =	UNetaDModel(
          sample_size=(64, 32)     ,					in_channels=1     ,					out_channels=1     ,					layers_per_block=2     ,					block_out_channels=(128, 128)     ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D')     ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D')     ,					)
      return vqvae, unet







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'cpu'  # ensure determinism for the device-dependent torch.Generator
      __UpperCamelCase      =	Mel(
          x_res=self.dummy_unet.config.sample_size[1]     ,					y_res=self.dummy_unet.config.sample_size[0]     ,					)

      __UpperCamelCase      =	DDPMScheduler()
      __UpperCamelCase      =	AudioDiffusionPipeline(vqvae=__UpperCAmelCase     ,					unet=self.dummy_unet     ,					mel=__UpperCAmelCase     ,					scheduler=__UpperCAmelCase				)
      __UpperCamelCase      =	pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(42				)
      __UpperCamelCase      =	pipe(generator=__UpperCAmelCase     ,					steps=4				)
      __UpperCamelCase      =	output.audios[0]
      __UpperCamelCase      =	output.images[0]

      __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(42				)
      __UpperCamelCase      =	pipe(generator=__UpperCAmelCase     ,					steps=4     ,					return_dict=__UpperCAmelCase				)
      __UpperCamelCase      =	output[0][0]

      assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
      assert (
          image.height == self.dummy_unet.config.sample_size[0]
          and image.width == self.dummy_unet.config.sample_size[1]
      )
      __UpperCamelCase      =	np.frombuffer(image.tobytes()     ,					dtype='uint8'				)[:10]
      __UpperCamelCase      =	np.frombuffer(image_from_tuple.tobytes()     ,					dtype='uint8'				)[:10]
      __UpperCamelCase      =	np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() == 0
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() == 0

      __UpperCamelCase      =	Mel(
          x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]     ,					y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]     ,					)

      __UpperCamelCase      =	DDIMScheduler()
      __UpperCamelCase      =	self.dummy_vqvae_and_unet
      __UpperCamelCase      =	AudioDiffusionPipeline(
          vqvae=self.dummy_vqvae_and_unet[0]     ,					unet=dummy_vqvae_and_unet[1]     ,					mel=__UpperCAmelCase     ,					scheduler=__UpperCAmelCase				)
      __UpperCamelCase      =	pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      np.random.seed(0				)
      __UpperCamelCase      =	np.random.uniform(-1     ,					1     ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)				)
      __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(42				)
      __UpperCamelCase      =	pipe(raw_audio=__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					start_step=5     ,					steps=10				)
      __UpperCamelCase      =	output.images[0]

      assert (
          image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
          and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
      )
      __UpperCamelCase      =	np.frombuffer(image.tobytes()     ,					dtype='uint8'				)[:10]
      __UpperCamelCase      =	np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() == 0

      __UpperCamelCase      =	self.dummy_unet_condition
      __UpperCamelCase      =	AudioDiffusionPipeline(
          vqvae=self.dummy_vqvae_and_unet[0]     ,					unet=__UpperCAmelCase     ,					mel=__UpperCAmelCase     ,					scheduler=__UpperCAmelCase				)
      __UpperCamelCase      =	pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      np.random.seed(0				)
      __UpperCamelCase      =	torch.rand((1, 1, 10)				)
      __UpperCamelCase      =	pipe(generator=__UpperCAmelCase     ,					encoding=__UpperCAmelCase				)
      __UpperCamelCase      =	output.images[0]
      __UpperCamelCase      =	np.frombuffer(image.tobytes()     ,					dtype='uint8'				)[:10]
      __UpperCamelCase      =	np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() == 0


@slow
@require_torch_gpu
class __lowerCAmelCase		(				unittest.TestCase      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	torch_device

      __UpperCamelCase      =	DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'				)
      __UpperCamelCase      =	pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

      __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(42				)
      __UpperCamelCase      =	pipe(generator=__UpperCAmelCase				)
      __UpperCamelCase      =	output.audios[0]
      __UpperCamelCase      =	output.images[0]

      assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
      assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
      __UpperCamelCase      =	np.frombuffer(image.tobytes()     ,					dtype='uint8'				)[:10]
      __UpperCamelCase      =	np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() == 0





"""simple docstring"""

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase     :    str       =			logging.get_logger(__name__)

UpperCamelCase     :    Dict       =			{
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"wavlm"





     def __init__(     self     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="group"     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)     ,					__UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=800     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0_5     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=100     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="mean"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=(512, 512, 512, 512, 1500)     ,					__UpperCAmelCase=(5, 3, 3, 1, 1)     ,					__UpperCAmelCase=(1, 2, 3, 1, 1)     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=80     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase     ,					bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase				)
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	feat_extract_norm
      __UpperCamelCase      =	feat_extract_activation
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	conv_bias
      __UpperCamelCase      =	num_buckets
      __UpperCamelCase      =	max_bucket_distance
      __UpperCamelCase      =	num_conv_pos_embeddings
      __UpperCamelCase      =	num_conv_pos_embedding_groups
      __UpperCamelCase      =	len(self.conv_dim				)
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	intermediate_size
      __UpperCamelCase      =	hidden_act
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	hidden_dropout
      __UpperCamelCase      =	attention_dropout
      __UpperCamelCase      =	activation_dropout
      __UpperCamelCase      =	feat_proj_dropout
      __UpperCamelCase      =	final_dropout
      __UpperCamelCase      =	layerdrop
      __UpperCamelCase      =	layer_norm_eps
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	num_ctc_classes
      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	do_stable_layer_norm
      __UpperCamelCase      =	use_weighted_layer_sum
      __UpperCamelCase      =	classifier_proj_size

      if (
          (len(self.conv_stride				) != self.num_feat_extract_layers)
          or (len(self.conv_kernel				) != self.num_feat_extract_layers)
          or (len(self.conv_dim				) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
           ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
           F' {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,'
           F' `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'				)

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __UpperCamelCase      =	apply_spec_augment
      __UpperCamelCase      =	mask_time_prob
      __UpperCamelCase      =	mask_time_length
      __UpperCamelCase      =	mask_time_min_masks
      __UpperCamelCase      =	mask_feature_prob
      __UpperCamelCase      =	mask_feature_length

      # parameters for pretraining with codevector quantized representations
      __UpperCamelCase      =	num_codevectors_per_group
      __UpperCamelCase      =	num_codevector_groups
      __UpperCamelCase      =	contrastive_logits_temperature
      __UpperCamelCase      =	num_negatives
      __UpperCamelCase      =	codevector_dim
      __UpperCamelCase      =	proj_codevector_dim
      __UpperCamelCase      =	diversity_loss_weight

      # ctc loss
      __UpperCamelCase      =	ctc_loss_reduction
      __UpperCamelCase      =	ctc_zero_infinity

      # adapter
      __UpperCamelCase      =	add_adapter
      __UpperCamelCase      =	adapter_kernel_size
      __UpperCamelCase      =	adapter_stride
      __UpperCamelCase      =	num_adapter_layers
      __UpperCamelCase      =	output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      __UpperCamelCase      =	classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	xvector_output_dim







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return functools.reduce(operator.mul     ,					self.conv_stride     ,					1				)



