


'''simple docstring'''





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class     lowercase__  (					_UpperCamelCase		):
  A__=  'ClapFeatureExtractor'
  A__=  ('RobertaTokenizer', 'RobertaTokenizerFast')






  def __init__(					self      :					List[Any]		,						_lowercase      :					Any		,						_lowercase      :					Tuple ):




         """simple docstring"""







         super().__init__(_UpperCAmelCase		,						_UpperCAmelCase )






  def __call__(					self      :					Tuple		,						_lowercase      :					Dict=None		,						_lowercase      :					Any=None		,						_lowercase      :					str=None		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     kwargs.pop("sampling_rate"		,						_UpperCAmelCase )

         if text is None and audios is None:
                raise ValueError("You have to specify either text or audios. Both cannot be none." )

         if text is not None:
                UpperCAmelCase__										=     self.tokenizer(_UpperCAmelCase		,						return_tensors=_UpperCAmelCase		,						**_UpperCAmelCase )

         if audios is not None:
                UpperCAmelCase__										=     self.feature_extractor(
                    _UpperCAmelCase		,						sampling_rate=_UpperCAmelCase		,						return_tensors=_UpperCAmelCase		,						**_UpperCAmelCase )

         if text is not None and audios is not None:
                UpperCAmelCase__										=     audio_features.input_features
                return encoding
         elif text is not None:
                return encoding
         else:
                return BatchEncoding(data=dict(**_UpperCAmelCase )		,						tensor_type=_UpperCAmelCase )






  def 			_UpperCAmelCase  (					self      :					int		,						*_lowercase      :					Any		,						**_lowercase      :					Tuple ):




         """simple docstring"""







         return self.tokenizer.batch_decode(*_UpperCAmelCase		,						**_UpperCAmelCase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						*_lowercase      :					List[str]		,						**_lowercase      :					Any ):




         """simple docstring"""







         return self.tokenizer.decode(*_UpperCAmelCase		,						**_UpperCAmelCase )






  @property
  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.tokenizer.model_input_names
         UpperCAmelCase__										=     self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )









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     lowercase__  (					unittest.TestCase		):






  @property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """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=0.7_5		,						guidance_scale=7.5		,						num_inference_steps=15		,						generator=_lowercase		,						output_type="np"		,						)

         UpperCAmelCase__										=     output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image ).max() < 1E-2





