"""simple docstring"""



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

import numpy as np

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


__lowercase		:		Optional[int]														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Union[str, Any]							=					['''input_features''']
    def __init__(		self							:     int	,      A_							:     Optional[int]=80	,      A_							:     Optional[Any]=16_000	,      A_							:     str=160	,      A_							:     Optional[int]=30	,      A_							:     Tuple=400	,      A_							:     Dict=0.0	,      A_							:     Any=False	,      **A_							:     Tuple	,      )    ->       Optional[int]:
         super().__init__(
             feature_size=A_	,      sampling_rate=A_	,      padding_value=A_	,      return_attention_mask=A_	,      **A_	,      )
         __snake_case				=							n_fft
         __snake_case				=							hop_length
         __snake_case				=							chunk_length
         __snake_case				=							chunk_length * sampling_rate
         __snake_case				=							self.n_samples // hop_length
         __snake_case				=							sampling_rate
         __snake_case				=							mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2	,      num_mel_filters=A_	,      min_frequency=0.0	,      max_frequency=80_00.0	,      sampling_rate=A_	,      norm='''slaney'''	,      mel_scale='''slaney'''	,      )
    def  lowercase					(		self							:     Any	,      A_							:     np.array    )    ->       np.ndarray:
         __snake_case				=							spectrogram(
             A_	,      window_function(self.n_fft	,      '''hann'''    )	,      frame_length=self.n_fft	,      hop_length=self.hop_length	,      power=2.0	,      mel_filters=self.mel_filters	,      log_mel='''log10'''	,      )
         __snake_case				=							log_spec[:, :-1]
         __snake_case				=							np.maximum(A_	,      log_spec.max() - 8.0    )
         __snake_case				=							(log_spec + 4.0) / 4.0
         return log_spec
    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def  lowercase					(		A_							:     List[np.ndarray]	,      A_							:     List[np.ndarray]	,      A_							:     float = 0.0    )    ->       List[np.ndarray]:
         if attention_mask is not None:
              __snake_case				=							np.array(A_	,      np.intaa    )
              __snake_case				=							[]

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

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

         return normed_input_values
    def __call__(		self							:     List[str]	,      A_							:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,      A_							:     bool = True	,      A_							:     Optional[int] = None	,      A_							:     Optional[Union[str, TensorType]] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[str] = "max_length"	,      A_							:     Optional[int] = None	,      A_							:     Optional[int] = None	,      A_							:     Optional[bool] = None	,      **A_							:     Optional[int]	,      )    ->       BatchFeature:

         if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                       f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                       f" was sampled 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.'''    )

         __snake_case				=							isinstance(A_	,      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}"    )
         __snake_case				=							is_batched_numpy or (
             isinstance(A_	,      (list, tuple)    ) and (isinstance(raw_speech[0]	,      (np.ndarray, tuple, list)    ))
         )

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

         # always return batch
         if not is_batched:
              __snake_case				=							[np.asarray([raw_speech]    ).T]

         __snake_case				=							BatchFeature({'''input_features''': raw_speech}    )

         # convert into correct format for padding

         __snake_case				=							self.pad(
             A_	,      padding=A_	,      max_length=max_length if max_length else self.n_samples	,      truncation=A_	,      pad_to_multiple_of=A_	,      return_attention_mask=return_attention_mask or do_normalize	,      )

         # zero-mean and unit-variance normalization
         if do_normalize:
              __snake_case				=							self.zero_mean_unit_var_norm(
                  padded_inputs['''input_features''']	,      attention_mask=padded_inputs['''attention_mask''']	,      padding_value=self.padding_value	,      )
              __snake_case				=							np.stack(padded_inputs['''input_features''']	,      axis=0    )

         # make sure list is in array format
         __snake_case				=							padded_inputs.get('''input_features'''    ).transpose(2	,      0	,      1    )

         __snake_case				=							[self._np_extract_fbank_features(A_    ) for waveform in input_features[0]]

         if isinstance(input_features[0]	,      A_    ):
              __snake_case				=							[np.asarray(A_	,      dtype=np.floataa    ) for feature in input_features]
         else:
              __snake_case				=							input_features

         if return_attention_mask:
              # rescale from sample (48000) to feature (3000)
              __snake_case				=							padded_inputs['''attention_mask'''][:, :: self.hop_length]

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

         return padded_inputs







    def  lowercase					(		self							:     Optional[int]    )    ->       Dict[str, Any]:
         __snake_case				=							copy.deepcopy(self.__dict__    )
         __snake_case				=							self.__class__.__name__
         if "mel_filters" in output:
              del output["mel_filters"]
         return output
"""simple docstring"""



import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
    def  lowercase					(		self							:     Optional[int]	,      A_							:     List[str]=0    )    ->       int:
         __snake_case				=							floats_tensor((1, 3, 128, 128)	,      rng=random.Random(A_    )    )
         __snake_case				=							np.random.RandomState(A_    )
         __snake_case				=							{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.75,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]    )
         assert np.abs(image_slice - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Tuple    )    ->       Optional[int]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							PNDMScheduler.from_config(pipe.scheduler.config	,      skip_prk_steps=A_    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Optional[int]    )    ->       str:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         # warmup pass to apply optimizations
         __snake_case				=							pipe(**self.get_dummy_inputs()    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]    )

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







    def  lowercase					(		self							:     List[str]    )    ->       Any:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]    )

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






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




    """simple docstring"""




    @property
    def  lowercase					(		self							:     str    )    ->       int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							ort.SessionOptions()
         __snake_case				=							False
         return options
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         # using the PNDM scheduler by default
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''	,      revision='''onnx'''	,      safety_checker=A_	,      feature_extractor=A_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							'''A fantasy landscape, trending on artstation'''

         __snake_case				=							np.random.RandomState(0    )
         __snake_case				=							pipe(
             prompt=A_	,      image=A_	,      strength=0.75	,      guidance_scale=7.5	,      num_inference_steps=10	,      generator=A_	,      output_type='''np'''	,      )
         __snake_case				=							output.images
         __snake_case				=							images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]    )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







    def  lowercase					(		self							:     str    )    ->       Optional[int]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         __snake_case				=							LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''	,      subfolder='''scheduler'''	,      revision='''onnx'''    )
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''	,      revision='''onnx'''	,      scheduler=A_	,      safety_checker=A_	,      feature_extractor=A_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							'''A fantasy landscape, trending on artstation'''

         __snake_case				=							np.random.RandomState(0    )
         __snake_case				=							pipe(
             prompt=A_	,      image=A_	,      strength=0.75	,      guidance_scale=7.5	,      num_inference_steps=20	,      generator=A_	,      output_type='''np'''	,      )
         __snake_case				=							output.images
         __snake_case				=							images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]    )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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