






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class        lowercase__       (     _UpperCAmelCase,   unittest.TestCase		):
		a_             =PaintByExamplePipeline
		a_             =IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		a_             =IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		a_             =frozenset([]		)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

		def 						UpperCAmelCase					(							self    )->   List[str]:







								'''simple docstring'''





								torch.manual_seed(0    )
								lowerCAmelCase__	 =  UNetaDConditionModel(
								    block_out_channels=(32, 64)						,      layers_per_block=2						,      sample_size=32						,      in_channels=9						,      out_channels=4						,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,      cross_attention_dim=32						,      )
								lowerCAmelCase__	 =  PNDMScheduler(skip_prk_steps=__UpperCAmelCase    )
								torch.manual_seed(0    )
								lowerCAmelCase__	 =  AutoencoderKL(
								    block_out_channels=[32, 64]						,      in_channels=3						,      out_channels=3						,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,      latent_channels=4						,      )
								torch.manual_seed(0    )
								lowerCAmelCase__	 =  CLIPVisionConfig(
								    hidden_size=32						,      projection_dim=32						,      intermediate_size=37						,      layer_norm_eps=1E-0_5						,      num_attention_heads=4						,      num_hidden_layers=5						,      image_size=32						,      patch_size=4						,      )
								lowerCAmelCase__	 =  PaintByExampleImageEncoder(__UpperCAmelCase						,      proj_size=32    )
								lowerCAmelCase__	 =  CLIPImageProcessor(crop_size=32						,      size=32    )

								lowerCAmelCase__	 =  {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "image_encoder": image_encoder,
								    "safety_checker": None,
								    "feature_extractor": feature_extractor,
								}
								return components

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Optional[Any]:







								'''simple docstring'''





								lowerCAmelCase__	 =  np.array(image.convert("RGB"    )    )
								lowerCAmelCase__	 =  image[None].transpose(0						,      3						,      1						,      2    )
								lowerCAmelCase__	 =  torch.from_numpy(__UpperCAmelCase    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
								return image

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase="cpu"						,      __UpperCAmelCase=0    )->   Union[str, Any]:







								'''simple docstring'''





								lowerCAmelCase__	 =  floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
								lowerCAmelCase__	 =  image.cpu().permute(0						,      2						,      3						,      1    )[0]
								lowerCAmelCase__	 =  Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert("RGB"    ).resize((64, 64)    )
								lowerCAmelCase__	 =  Image.fromarray(np.uinta(image + 4    )    ).convert("RGB"    ).resize((64, 64)    )
								lowerCAmelCase__	 =  Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert("RGB"    ).resize((32, 32)    )

								if str(__UpperCAmelCase    ).startswith("mps"    ):
														lowerCAmelCase__	 =  torch.manual_seed(__UpperCAmelCase    )
								else:
														lowerCAmelCase__	 =  torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
								lowerCAmelCase__	 =  {
								    "example_image": example_image,
								    "image": init_image,
								    "mask_image": mask_image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								}
								return inputs

		def 						UpperCAmelCase					(							self    )->   Dict:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.get_dummy_components()

								# make sure here that pndm scheduler skips prk
								lowerCAmelCase__	 =  PaintByExamplePipeline(**__UpperCAmelCase    )
								lowerCAmelCase__	 =  pipe.to("cpu"    )
								pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

								lowerCAmelCase__	 =  self.get_dummy_inputs()
								lowerCAmelCase__	 =  pipe(**__UpperCAmelCase    )
								lowerCAmelCase__	 =  output.images

								lowerCAmelCase__	 =  image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								lowerCAmelCase__	 =  np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2

		def 						UpperCAmelCase					(							self    )->   Tuple:







								'''simple docstring'''





								lowerCAmelCase__	 =  "cpu"
								lowerCAmelCase__	 =  self.get_dummy_inputs()
								inputs.pop("mask_image"    )
								lowerCAmelCase__	 =  self.convert_to_pt(inputs.pop("image"    )    )
								lowerCAmelCase__	 =  image.clamp(0						,      1    ) / 2

								# make sure here that pndm scheduler skips prk
								lowerCAmelCase__	 =  PaintByExamplePipeline(**self.get_dummy_components()    )
								lowerCAmelCase__	 =  pipe.to(__UpperCAmelCase    )
								pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

								lowerCAmelCase__	 =  pipe(image=__UpperCAmelCase						,      mask_image=mask_image[:, 0]						,      **__UpperCAmelCase    )
								lowerCAmelCase__	 =  output.images

								lowerCAmelCase__	 =  image.cpu().permute(0						,      2						,      3						,      1    )[0]
								lowerCAmelCase__	 =  mask_image.cpu().permute(0						,      2						,      3						,      1    )[0]

								lowerCAmelCase__	 =  Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert("RGB"    )
								lowerCAmelCase__	 =  Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert("RGB"    )

								lowerCAmelCase__	 =  pipe(**self.get_dummy_inputs()    )
								lowerCAmelCase__	 =  output.images

								assert out_a.shape == (1, 64, 64, 3)
								assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5E-2


		def 						UpperCAmelCase					(							self    )->   str:







								'''simple docstring'''





								super().test_inference_batch_single_identical(expected_max_diff=3E-3    )





@slow
@require_torch_gpu
class        lowercase__       (     unittest.TestCase		):

		def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







								'''simple docstring'''





								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


		def 						UpperCAmelCase					(							self    )->   int:







								'''simple docstring'''





								lowerCAmelCase__	 =  load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/dog_in_bucket.png"    )
								lowerCAmelCase__	 =  load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/mask.png"    )
								lowerCAmelCase__	 =  load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/paint_by_example/panda.jpg"    )

								lowerCAmelCase__	 =  PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"    )
								lowerCAmelCase__	 =  pipe.to(__UpperCAmelCase    )
								pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

								lowerCAmelCase__	 =  torch.manual_seed(321    )
								lowerCAmelCase__	 =  pipe(
								    image=__UpperCAmelCase						,      mask_image=__UpperCAmelCase						,      example_image=__UpperCAmelCase						,      generator=__UpperCAmelCase						,      guidance_scale=5.0						,      num_inference_steps=50						,      output_type="np"						,      )

								lowerCAmelCase__	 =  output.images

								lowerCAmelCase__	 =  image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCAmelCase__	 =  np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2














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     _a					(       UpperCamelCase_   :			bytes      ,				UpperCamelCase_   :			int				)	->				np.array:
						"""simple docstring"""







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

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

						lowerCAmelCase__	 =  np.frombuffer(UpperCamelCase_      ,				np.floataa				)
						if audio.shape[0] == 0:
												raise ValueError("Malformed soundfile"				)
						return audio





@add_end_docstrings(_UpperCAmelCase		)
class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  5
							super().__init__(*__UpperCAmelCase						,      **__UpperCAmelCase    )

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

							self.check_model_type(__UpperCAmelCase    )

	def __call__(							self						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   List[str]:







							'''simple docstring'''





							return super().__call__(__UpperCAmelCase						,      **__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  {}
							if top_k is not None:
													if top_k > self.model.config.num_labels:
																			lowerCAmelCase__	 =  self.model.config.num_labels
													lowerCAmelCase__	 =  top_k
							return {}, {}, postprocess_params

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													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
																			lowerCAmelCase__	 =  requests.get(__UpperCAmelCase    ).content
													else:
																			with open(__UpperCAmelCase						,      "rb"    ) as f:
																									lowerCAmelCase__	 =  f.read()

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													lowerCAmelCase__	 =  ffmpeg_read(__UpperCAmelCase						,      self.feature_extractor.sampling_rate    )

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													# 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"    )

													lowerCAmelCase__	 =  inputs.pop("raw"						,      __UpperCAmelCase    )
													if _inputs is None:
																			# Remove path which will not be used from `datasets`.
																			inputs.pop("path"						,      __UpperCAmelCase    )
																			lowerCAmelCase__	 =  inputs.pop("array"						,      __UpperCAmelCase    )
													lowerCAmelCase__	 =  inputs.pop("sampling_rate"    )
													lowerCAmelCase__	 =  _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`."    )

																			lowerCAmelCase__	 =  F.resample(
																			    torch.from_numpy(__UpperCAmelCase    )						,      __UpperCAmelCase						,      self.feature_extractor.sampling_rate    ).numpy()

							if not isinstance(__UpperCAmelCase						,      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"    )

							lowerCAmelCase__	 =  self.feature_extractor(
							    __UpperCAmelCase						,      sampling_rate=self.feature_extractor.sampling_rate						,      return_tensors="pt"    )
							return processed

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model(**__UpperCAmelCase    )
							return model_outputs


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=5    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  model_outputs.logits[0].softmax(-1    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  probs.topk(__UpperCAmelCase    )

							lowerCAmelCase__	 =  scores.tolist()
							lowerCAmelCase__	 =  ids.tolist()

							lowerCAmelCase__	 =  [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase						,      __UpperCAmelCase    )]

							return labels






