




import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

a_										=       logging.get_logger(__name__)




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		np.array:

    """simple docstring"""


    __UpperCamelCase											=  F"{sampling_rate}"
    __UpperCamelCase											=  '''1'''
    __UpperCamelCase											=  '''f32le'''
    __UpperCamelCase											=  [
        '''ffmpeg''',
        '''-i''',
        '''pipe:0''',
        '''-ac''',
        ac,
        '''-ar''',
        ar,
        '''-f''',
        format_for_conversion,
        '''-hide_banner''',
        '''-loglevel''',
        '''quiet''',
        '''pipe:1''',
    ]

    try:
        __UpperCamelCase											=  subprocess.Popen(lowercase_   ,       stdin=subprocess.PIPE   ,       stdout=subprocess.PIPE )
    except FileNotFoundError:
        raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' )
    __UpperCamelCase											=  ffmpeg_process.communicate(lowercase_ )
    __UpperCamelCase											=  output_stream[0]

    __UpperCamelCase											=  np.frombuffer(lowercase_   ,       np.floataa )
    if audio.shape[0] == 0:
        raise ValueError('''Malformed soundfile''' )
    return audio


@add_end_docstrings(SCREAMING_SNAKE_CASE_				)
class 	_lowerCamelCase  (     SCREAMING_SNAKE_CASE_				):

      """simple docstring"""

      def __init__(       self   :	Union[str, Any]	,    *snake_case   :	int	,    **snake_case   :	Optional[Any]			):
          # Default, might be overriden by the model.config.
          __UpperCamelCase											=  5
          super().__init__(*lowercase_	,    **lowercase_			)

          if self.framework != "pt":
              raise ValueError(F"The {self.__class__} is only available in PyTorch."			)

          self.check_model_type(lowercase_			)







      def __call__(       self   :	List[str]	,    snake_case   :	str	,    **snake_case   :	str	,    ):
          return super().__call__(lowercase_	,    **lowercase_			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Tuple=None	,    **snake_case   :	Tuple			):
          # No parameters on this pipeline right now
          __UpperCamelCase											=  {}
          if top_k is not None:
              if top_k > self.model.config.num_labels:
                  __UpperCamelCase											=  self.model.config.num_labels
              __UpperCamelCase											=  top_k
          return {}, {}, postprocess_params







      def  snake_case		(       self   :	Optional[int]	,    snake_case   :	Tuple			):
          if isinstance(lowercase_	,    lowercase_			):
              if inputs.startswith('''http://'''			) or inputs.startswith('''https://'''			):
                  # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                  # like http_huggingface_co.png
                  __UpperCamelCase											=  requests.get(lowercase_			).content
              else:
                  with open(lowercase_	,    '''rb'''			) as f:
                      __UpperCamelCase											=  f.read()

          if isinstance(lowercase_	,    lowercase_			):
              __UpperCamelCase											=  ffmpeg_read(lowercase_	,    self.feature_extractor.sampling_rate			)

          if isinstance(lowercase_	,    lowercase_			):
              # Accepting `"array"` which is the key defined in `datasets` for
              # better integration
              if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                  raise ValueError(
                      '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
                      '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
                      '''containing the sampling_rate associated with that array'''			)

              __UpperCamelCase											=  inputs.pop('''raw'''	,    lowercase_			)
              if _inputs is None:
                  # Remove path which will not be used from `datasets`.
                  inputs.pop('''path'''	,    lowercase_			)
                  __UpperCamelCase											=  inputs.pop('''array'''	,    lowercase_			)
              __UpperCamelCase											=  inputs.pop('''sampling_rate'''			)
              __UpperCamelCase											=  _inputs
              if in_sampling_rate != self.feature_extractor.sampling_rate:
                  import torch

                  if is_torchaudio_available():
                      from torchaudio import functional as F
                  else:
                      raise ImportError(
                          '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
                          '''The torchaudio package can be installed through: `pip install torchaudio`.'''			)

                  __UpperCamelCase											=  F.resample(
                      torch.from_numpy(lowercase_			)	,    lowercase_	,    self.feature_extractor.sampling_rate			).numpy()

          if not isinstance(lowercase_	,    np.ndarray			):
              raise ValueError('''We expect a numpy ndarray as input'''			)
          if len(inputs.shape			) != 1:
              raise ValueError('''We expect a single channel audio input for AudioClassificationPipeline'''			)

          __UpperCamelCase											=  self.feature_extractor(
              lowercase_	,    sampling_rate=self.feature_extractor.sampling_rate	,    return_tensors='''pt'''			)
          return processed







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model(**lowercase_			)
          return model_outputs







      def  snake_case		(       self   :	int	,    snake_case   :	Any	,    snake_case   :	Union[str, Any]=5			):
          __UpperCamelCase											=  model_outputs.logits[0].softmax(-1			)
          __UpperCamelCase						, __UpperCamelCase											=  probs.topk(lowercase_			)

          __UpperCamelCase											=  scores.tolist()
          __UpperCamelCase											=  ids.tolist()

          __UpperCamelCase											=  [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowercase_	,    lowercase_			)]

          return labels












import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   RePaintPipeline
      lowerCAmelCase__     :	Optional[int]      =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      lowerCAmelCase__     :	Tuple      =   PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      lowerCAmelCase__     :	List[str]      =   IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase__     :	Dict      =   False







      def  snake_case		(       self   :	Optional[Any]			):
          torch.manual_seed(0			)
          torch.manual_seed(0			)
          __UpperCamelCase											=  UNetaDModel(
              block_out_channels=(32, 64)	,    layers_per_block=2	,    sample_size=32	,    in_channels=3	,    out_channels=3	,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,    )
          __UpperCamelCase											=  RePaintScheduler()
          __UpperCamelCase											=  {'''unet''': unet, '''scheduler''': scheduler}
          return components







      def  snake_case		(       self   :	int	,    snake_case   :	str	,    snake_case   :	List[Any]=0			):
          if str(snake_case			).startswith('''mps'''			):
              __UpperCamelCase											=  torch.manual_seed(snake_case			)
          else:
              __UpperCamelCase											=  torch.Generator(device=snake_case			).manual_seed(snake_case			)
          __UpperCamelCase											=  np.random.RandomState(snake_case			).standard_normal((1, 3, 32, 32)			)
          __UpperCamelCase											=  torch.from_numpy(snake_case			).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  (image > 0).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  {
              '''image''': image,
              '''mask_image''': mask,
              '''generator''': generator,
              '''num_inference_steps''': 5,
              '''eta''': 0.0,
              '''jump_length''': 2,
              '''jump_n_sample''': 2,
              '''output_type''': '''numpy''',
          }
          return inputs







      def  snake_case		(       self   :	str			):
          __UpperCamelCase											=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCamelCase											=  self.get_dummy_components()
          __UpperCamelCase											=  RePaintPipeline(**snake_case			)
          __UpperCamelCase											=  sd_pipe.to(snake_case			)
          sd_pipe.set_progress_bar_config(disable=snake_case			)

          __UpperCamelCase											=  self.get_dummy_inputs(snake_case			)
          __UpperCamelCase											=  sd_pipe(**snake_case			).images
          __UpperCamelCase											=  image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]			)

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







      @skip_mps
      def  snake_case		(       self   :	str			):
          return super().test_save_load_local()







      @unittest.skip('''non-deterministic pipeline'''			)
      def  snake_case		(       self   :	Any			):
          return super().test_inference_batch_single_identical()







      @skip_mps
      def  snake_case		(       self   :	List[Any]			):
          return super().test_dict_tuple_outputs_equivalent()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_save_load_optional_components()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      def  snake_case		(       self   :	Dict			):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256.png'''			)
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
          __UpperCamelCase											=  load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256_result.npy'''			)

          __UpperCamelCase											=  '''google/ddpm-ema-celebahq-256'''
          __UpperCamelCase											=  UNetaDModel.from_pretrained(snake_case			)
          __UpperCamelCase											=  RePaintScheduler.from_pretrained(snake_case			)

          __UpperCamelCase											=  RePaintPipeline(unet=snake_case	,    scheduler=snake_case			).to(snake_case			)
          repaint.set_progress_bar_config(disable=snake_case			)
          repaint.enable_attention_slicing()

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  repaint(
              snake_case	,    snake_case	,    num_inference_steps=250	,    eta=0.0	,    jump_length=10	,    jump_n_sample=10	,    generator=snake_case	,    output_type='''np'''	,    )
          __UpperCamelCase											=  output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image			).mean() < 1E-2






