




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCamelCase__              =						logging.get_logger(__name__)


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = ["""input_values""", """attention_mask"""]

					def __init__(				self ,       __lowercase=1 ,       __lowercase=16_000 ,       __lowercase=128 ,       __lowercase=1_024 ,       __lowercase=0.0 ,       __lowercase=True ,       __lowercase=-4.2_67_73_93 ,       __lowercase=4.5_68_99_74 ,       __lowercase=False ,       **__lowercase ,       ):







						'''simple docstring'''




						super().__init__(feature_size=__lowercase ,       sampling_rate=__lowercase ,       padding_value=__lowercase ,       **__lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							num_mel_bins
						lowerCamelCase_							:		Optional[int]							   =							max_length
						lowerCamelCase_							:		List[str]							   =							do_normalize
						lowerCamelCase_							:		Any							   =							mean
						lowerCamelCase_							:		Tuple							   =							std
						lowerCamelCase_							:		Optional[int]							   =							return_attention_mask

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							torch.from_numpy(__lowercase					).unsqueeze(0					)
						lowerCamelCase_							:		Tuple							   =							ta_kaldi.fbank(
						    __lowercase ,       htk_compat=__lowercase ,       sample_frequency=self.sampling_rate ,       use_energy=__lowercase ,       window_type='hanning' ,       num_mel_bins=self.num_mel_bins ,       dither=0.0 ,       frame_shift=10 ,       )

						lowerCamelCase_							:		str							   =							fbank.shape[0]
						lowerCamelCase_							:		Union[str, Any]							   =							max_length - n_frames

						# pad or truncate, depending on difference
						if difference > 0:
							lowerCamelCase_							:		Any							   =							torch.nn.ZeroPadad((0, 0, 0, difference)					)
							lowerCamelCase_							:		Optional[int]							   =							pad_module(__lowercase					)
						elif difference < 0:
							lowerCamelCase_							:		Optional[Any]							   =							fbank[0:max_length, :]

						lowerCamelCase_							:		int							   =							fbank.numpy()

						return fbank

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return (input_values - (self.mean)) / (self.std * 2)





					def __call__(				self ,       __lowercase ,       __lowercase = None ,       __lowercase = None ,       **__lowercase ,       ):







						'''simple docstring'''




						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
								    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
								    f" {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_							:		Dict							   =							isinstance(__lowercase ,       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_							:		List[str]							   =							is_batched_numpy or (
						    isinstance(__lowercase ,       (list, tuple)					) and (isinstance(raw_speech[0] ,       (np.ndarray, tuple, list)					))
						)

						if is_batched:
							lowerCamelCase_							:		List[str]							   =							[np.asarray(__lowercase ,       dtype=np.floataa					) for speech in raw_speech]
						elif not is_batched and not isinstance(__lowercase ,       np.ndarray					):
							lowerCamelCase_							:		Any							   =							np.asarray(__lowercase ,       dtype=np.floataa					)
						elif isinstance(__lowercase ,       np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
							lowerCamelCase_							:		Tuple							   =							raw_speech.astype(np.floataa					)

						# always return batch
						if not is_batched:
							lowerCamelCase_							:		int							   =							[raw_speech]

						# extract fbank features and pad/truncate to max_length
						lowerCamelCase_							:		str							   =							[self._extract_fbank_features(__lowercase ,       max_length=self.max_length					) for waveform in raw_speech]

						# convert into BatchFeature
						lowerCamelCase_							:		Dict							   =							BatchFeature({'input_values': features}					)

						# make sure list is in array format
						lowerCamelCase_							:		List[str]							   =							padded_inputs.get('input_values'					)
						if isinstance(input_values[0] ,       __lowercase					):
							lowerCamelCase_							:		int							   =							[np.asarray(__lowercase ,       dtype=np.floataa					) for feature in input_values]

						# normalization
						if self.do_normalize:
							lowerCamelCase_							:		str							   =							[self.normalize(__lowercase					) for feature in input_values]

						if return_tensors is not None:
							lowerCamelCase_							:		Dict							   =							padded_inputs.convert_to_tensors(__lowercase					)

						return padded_inputs







"""simple docstring"""


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_ (   __lowerCAmelCase ,unittest.TestCase							):
       snake_case__ :							List[str] 							=   PaintByExamplePipeline
       snake_case__ :							Any 							=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__ :							Optional[int] 							=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__ :							int 							=   frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        torch.manual_seed(0   )
        snake_case__										= 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      ,    )
        snake_case__										= PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
        torch.manual_seed(0   )
        snake_case__										= 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   )
        snake_case__										= CLIPVisionConfig(
            hidden_size=32      ,    projection_dim=32      ,    intermediate_size=37      ,    layer_norm_eps=1e-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    image_size=32      ,    patch_size=4      ,    )
        snake_case__										= PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE      ,    proj_size=32   )
        snake_case__										= CLIPImageProcessor(crop_size=32      ,    size=32   )

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE="cpu"      ,    __SCREAMING_SNAKE_CASE=0   ):






        '''simple docstring'''

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

        if str(__SCREAMING_SNAKE_CASE   ).startswith("mps"   ):
         snake_case__										= torch.manual_seed(__SCREAMING_SNAKE_CASE   )
        else:
         snake_case__										= torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
        snake_case__										= {
            "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 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        snake_case__										= self.get_dummy_components()

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

        snake_case__										= self.get_dummy_inputs()
        snake_case__										= pipe(**__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

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

        assert image.shape == (1, 64, 64, 3)
        snake_case__										= np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]   )

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

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

        snake_case__										= pipe(image=__SCREAMING_SNAKE_CASE      ,    mask_image=mask_image[:, 0]      ,    **__SCREAMING_SNAKE_CASE   )
        snake_case__										= output.images

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

        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )
        snake_case__										= Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert("RGB"   )

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

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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



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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

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

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

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

        snake_case__										= output.images

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

        assert image.shape == (1, 512, 512, 3)
        snake_case__										= np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]   )

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

