




import math

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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class       a__   ( lowercase__				):
						_a							:   List[Any] =   '''data2vec-audio'''






						def __init__(       self		,      _A=3_2		,      _A=7_6_8		,      _A=1_2		,      _A=1_2		,      _A=3_0_7_2		,      _A="gelu"		,      _A=0.1		,      _A=0.1		,      _A=0.1		,      _A=0.0		,      _A=0.1		,      _A=0.1		,      _A=0.02		,      _A=1E-5		,      _A="gelu"		,      _A=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,      _A=(5, 2, 2, 2, 2, 2, 2)		,      _A=(1_0, 3, 3, 3, 3, 2, 2)		,      _A=False		,      _A=1_6		,      _A=1_9		,      _A=5		,      _A=0.05		,      _A=1_0		,      _A=2		,      _A=0.0		,      _A=1_0		,      _A=0		,      _A="sum"		,      _A=False		,      _A=False		,      _A=2_5_6		,      _A=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,      _A=(5, 3, 3, 1, 1)		,      _A=(1, 2, 3, 1, 1)		,      _A=5_1_2		,      _A=0		,      _A=1		,      _A=2		,      _A=False		,      _A=3		,      _A=2		,      _A=3		,      _A=None		,      **_A		,      ):




											"""simple docstring"""



											super().__init__(**_a		,      pad_token_id=_a		,      bos_token_id=_a		,      eos_token_id=_a      )
											__lowerCAmelCase           =	hidden_size
											__lowerCAmelCase           =	feat_extract_activation
											__lowerCAmelCase           =	list(_a      )
											__lowerCAmelCase           =	list(_a      )
											__lowerCAmelCase           =	list(_a      )
											__lowerCAmelCase           =	conv_bias
											__lowerCAmelCase           =	num_conv_pos_embeddings
											__lowerCAmelCase           =	num_conv_pos_embedding_groups
											__lowerCAmelCase           =	conv_pos_kernel_size
											__lowerCAmelCase           =	len(self.conv_dim      )
											__lowerCAmelCase           =	num_hidden_layers
											__lowerCAmelCase           =	intermediate_size
											__lowerCAmelCase           =	hidden_act
											__lowerCAmelCase           =	num_attention_heads
											__lowerCAmelCase           =	hidden_dropout
											__lowerCAmelCase           =	attention_dropout
											__lowerCAmelCase           =	activation_dropout
											__lowerCAmelCase           =	feat_proj_dropout
											__lowerCAmelCase           =	final_dropout
											__lowerCAmelCase           =	layerdrop
											__lowerCAmelCase           =	layer_norm_eps
											__lowerCAmelCase           =	initializer_range
											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	use_weighted_layer_sum

											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
											__lowerCAmelCase           =	mask_time_prob
											__lowerCAmelCase           =	mask_time_length
											__lowerCAmelCase           =	mask_time_min_masks
											__lowerCAmelCase           =	mask_feature_prob
											__lowerCAmelCase           =	mask_feature_length
											__lowerCAmelCase           =	mask_feature_min_masks

											# ctc loss
											__lowerCAmelCase           =	ctc_loss_reduction
											__lowerCAmelCase           =	ctc_zero_infinity

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

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

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






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return math.prod(self.conv_stride      )










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       a__   ( unittest.TestCase				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	UNetaDModel(
											    block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_uncond_unet
											__lowerCAmelCase           =	ScoreSdeVeScheduler()

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A		,      return_dict=_A      )[
											    0
											]

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]
											__lowerCAmelCase           =	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase           =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






@slow
@require_torch
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"google/ncsnpp-church-256"
											__lowerCAmelCase           =	UNetaDModel.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVeScheduler.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=1_0		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)

											__lowerCAmelCase           =	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




