


'''simple docstring'''



from math import ceil
from typing import List, Optional, Union

import numpy as np

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


A__		:					Dict												=  logging.get_logger(__name__)





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			['audio_values', 'audio_mask']







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=20_48			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=[16, 16]			,     SCREAMING_SNAKE_CASE_=1_28			,     SCREAMING_SNAKE_CASE_=4_41_00			,     SCREAMING_SNAKE_CASE_=86			,     SCREAMING_SNAKE_CASE_=20_48			,     SCREAMING_SNAKE_CASE_=0.0			,     **SCREAMING_SNAKE_CASE_			,     )				->     Union[str, Any]:
         super().__init__(
             feature_size=SCREAMING_SNAKE_CASE_			,     sampling_rate=SCREAMING_SNAKE_CASE_			,     padding_value=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	int								=		spectrogram_length
         __lowerCamelCase   :	Dict								=		num_channels
         __lowerCamelCase   :	str								=		patch_size
         __lowerCamelCase   :	Any								=		feature_size // self.patch_size[1]
         __lowerCamelCase   :	Optional[Any]								=		n_fft
         __lowerCamelCase   :	List[str]								=		sampling_rate // hop_length_to_sampling_rate
         __lowerCamelCase   :	Optional[int]								=		sampling_rate
         __lowerCamelCase   :	List[str]								=		padding_value
         __lowerCamelCase   :	Dict								=		mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2			,     num_mel_filters=SCREAMING_SNAKE_CASE_			,     min_frequency=0.0			,     max_frequency=2_2_0_5_0.0			,     sampling_rate=SCREAMING_SNAKE_CASE_			,     norm='slaney'			,     mel_scale='slaney'			,     ).T







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     np.ndarray:
         __lowerCamelCase   :	str								=		spectrogram(
             SCREAMING_SNAKE_CASE_			,     window_function(self.n_fft			,     'hann'	)			,     frame_length=self.n_fft			,     hop_length=self.hop_length			,     power=2.0			,     mel_filters=self.mel_filters.T			,     log_mel='dB'			,     db_range=8_0.0			,     )
         __lowerCamelCase   :	Optional[int]								=		log_spec[:, :-1]
         __lowerCamelCase   :	Optional[Any]								=		log_spec - 2_0.0
         __lowerCamelCase   :	Optional[Any]								=		np.clip(log_spec / 4_0.0			,     -2.0			,     0.0	) + 1.0
         return log_spec







    def __call__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = True			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = False			,     SCREAMING_SNAKE_CASE_ = False			,     **SCREAMING_SNAKE_CASE_			,     )				->     BatchFeature:

         if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       'This feature extractor is set to support sampling rate'
                       f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
                       f' with {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.'	)

         __lowerCamelCase   :	List[Any]								=		isinstance(SCREAMING_SNAKE_CASE_			,     np.ndarray	) and len(raw_speech.shape	) > 1
         if is_batched_numpy and len(raw_speech.shape	) > 2:
              raise ValueError(f'Only mono-channel audio is supported for input to {self}'	)
         __lowerCamelCase   :	Optional[int]								=		is_batched_numpy or (
             isinstance(SCREAMING_SNAKE_CASE_			,     (list, tuple)	) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list)	))
         )
         if is_batched:
              __lowerCamelCase   :	List[str]								=		[np.asarray([speech]			,     dtype=np.floataa	).T for speech in raw_speech]
         elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_			,     np.ndarray	):
              __lowerCamelCase   :	Tuple								=		np.asarray(SCREAMING_SNAKE_CASE_			,     dtype=np.floataa	)
         elif isinstance(SCREAMING_SNAKE_CASE_			,     np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
              __lowerCamelCase   :	List[Any]								=		raw_speech.astype(np.floataa	)
         # always return batch
         if not is_batched:
              __lowerCamelCase   :	List[str]								=		[np.asarray([raw_speech]	).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         __lowerCamelCase   :	List[Any]								=		[
             self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]			,     SCREAMING_SNAKE_CASE_	):
              __lowerCamelCase   :	Any								=		[np.asarray(SCREAMING_SNAKE_CASE_			,     dtype=np.floataa	) for feature in audio_features]

         # Create audio attention mask
         __lowerCamelCase   :	Optional[Any]								=		max(
             [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
         if return_attention_mask:
              __lowerCamelCase   :	int								=		[
                  (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
                  + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
                  for feature in audio_features
              ]
              __lowerCamelCase   :	List[Any]								=		np.array(SCREAMING_SNAKE_CASE_	).astype(np.floataa	)

         # convert into correct format for padding
         __lowerCamelCase   :	Any								=		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         __lowerCamelCase   :	int								=		np.ones([len(SCREAMING_SNAKE_CASE_	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
         __lowerCamelCase   :	Dict								=		padded_audio_features * self.padding_value
         for i in range(len(SCREAMING_SNAKE_CASE_	)	):
              __lowerCamelCase   :	Union[str, Any]								=		audio_features[i]
              __lowerCamelCase   :	List[str]								=		feature

         # return as BatchFeature
         if return_attention_mask:
              __lowerCamelCase   :	Dict								=		{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
         else:
              __lowerCamelCase   :	Union[str, Any]								=		{'audio_values': padded_audio_features}

         __lowerCamelCase   :	List[Any]								=		BatchFeature(data=SCREAMING_SNAKE_CASE_			,     tensor_type=SCREAMING_SNAKE_CASE_	)
         return encoded_inputs










'''simple docstring'''



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort





class    UpperCAmelCase_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @property
    def       lowercase_				(							self	)				->     int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	int								=		ort.SessionOptions()
         __lowerCamelCase   :	Optional[Any]								=		False
         return options







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Any								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Tuple								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Union[str, Any]								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	Optional[Any]								=		output.images
         __lowerCamelCase   :	Tuple								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	List[str]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Dict								=		LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
         __lowerCamelCase   :	Tuple								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Tuple								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Optional[int]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	List[str]								=		output.images
         __lowerCamelCase   :	Dict								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Tuple								=		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3






