




'''simple docstring'''







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
    import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class     _a						(			unittest.TestCase				):
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
       @property
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	ort.SessionOptions()
           UpperCAmelCase     =	False
           return options







       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/in_paint/overture-creations-5sI6fQgYIuo.png'''       )
           UpperCAmelCase     =	load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''       )
           UpperCAmelCase     =	load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''       )

           # using the PNDM scheduler by default
           UpperCAmelCase     =	OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
               '''CompVis/stable-diffusion-v1-4'''     ,       revision='''onnx'''     ,       safety_checker=lowercase     ,       feature_extractor=lowercase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
           pipe.set_progress_bar_config(disable=lowercase       )

           UpperCAmelCase     =	'''A red cat sitting on a park bench'''

           UpperCAmelCase     =	np.random.RandomState(0       )
           UpperCAmelCase     =	pipe(
               prompt=lowercase     ,       image=lowercase     ,       mask_image=lowercase     ,       strength=0.75     ,       guidance_scale=7.5     ,       num_inference_steps=15     ,       generator=lowercase     ,       output_type='''np'''     ,       )

           UpperCAmelCase     =	output.images[0]

           assert image.shape == (512, 512, 3)
           assert np.abs(expected_image - image       ).max() < 1E-2










'''simple docstring'''







import math
from typing import Optional

import numpy as np

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


A									=logging.get_logger(__name__)

A									={
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class     _a						(			__a				):
       __a       :  Union[str, Any]            =      """encodec"""
       def __init__(			self		:			Optional[int]     ,       lowercase		:			Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,       lowercase		:			str=24_000     ,       lowercase		:			int=1     ,       lowercase		:			int=False     ,       lowercase		:			List[str]=None     ,       lowercase		:			Tuple=None     ,       lowercase		:			int=128     ,       lowercase		:			List[Any]=32     ,       lowercase		:			Optional[int]=1     ,       lowercase		:			Optional[int]=[8, 5, 4, 2]     ,       lowercase		:			Union[str, Any]="weight_norm"     ,       lowercase		:			Optional[int]=7     ,       lowercase		:			Tuple=7     ,       lowercase		:			Tuple=3     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Tuple="reflect"     ,       lowercase		:			Any=2     ,       lowercase		:			List[str]=2     ,       lowercase		:			str=1.0     ,       lowercase		:			List[str]=1_024     ,       lowercase		:			Optional[Any]=None     ,       lowercase		:			Any=True     ,       **lowercase		:			Optional[Any]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	target_bandwidths
           UpperCAmelCase     =	sampling_rate
           UpperCAmelCase     =	audio_channels
           UpperCAmelCase     =	normalize
           UpperCAmelCase     =	chunk_length_s
           UpperCAmelCase     =	overlap
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_filters
           UpperCAmelCase     =	num_residual_layers
           UpperCAmelCase     =	upsampling_ratios
           UpperCAmelCase     =	norm_type
           UpperCAmelCase     =	kernel_size
           UpperCAmelCase     =	last_kernel_size
           UpperCAmelCase     =	residual_kernel_size
           UpperCAmelCase     =	dilation_growth_rate
           UpperCAmelCase     =	use_causal_conv
           UpperCAmelCase     =	pad_mode
           UpperCAmelCase     =	compress
           UpperCAmelCase     =	num_lstm_layers
           UpperCAmelCase     =	trim_right_ratio
           UpperCAmelCase     =	codebook_size
           UpperCAmelCase     =	codebook_dim if codebook_dim is not None else hidden_size
           UpperCAmelCase     =	use_conv_shortcut

           if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"       )

           super().__init__(**lowercase       )
       @property
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           if self.chunk_length_s is None:
               return None
           else:
               return int(self.chunk_length_s * self.sampling_rate       )
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           if self.chunk_length_s is None or self.overlap is None:
               return None
           else:
               return max(1     ,       int((1.0 - self.overlap) * self.chunk_length       )       )
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	np.prod(self.upsampling_ratios       )
           return math.ceil(self.sampling_rate / hop_length       )







       @property
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )




