






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A__      :     List[str]       =					logging.get_logger(__name__)  # pylint: disable=invalid-name






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				warnings.warn(
				    '''The preprocess method is deprecated and will be removed in a future version. Please'''
				    ''' use VaeImageProcessor.preprocess instead'''						,  lowerCamelCase_						,  )
				if isinstance(lowerCamelCase_						,  torch.Tensor							):
								return image
				elif isinstance(lowerCamelCase_						,  PIL.Image.Image							):
								lowercase__  					=     [image]

				if isinstance(image[0]						,  PIL.Image.Image							):
								lowercase__					,		lowercase__  					=     image[0].size
								lowercase__					,		lowercase__  					=     (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

								lowercase__  					=     [np.array(i.resize((w, h)						,  resample=PIL_INTERPOLATION['''lanczos''']							)							)[None, :] for i in image]
								lowercase__  					=     np.concatenate(lowerCamelCase_						,  axis=0							)
								lowercase__  					=     np.array(lowerCamelCase_							).astype(np.floataa							) / 2_55.0
								lowercase__  					=     image.transpose(0						,  3						,  1						,  2							)
								lowercase__  					=     2.0 * image - 1.0
								lowercase__  					=     torch.from_numpy(lowerCamelCase_							)
				elif isinstance(image[0]						,  torch.Tensor							):
								lowercase__  					=     torch.cat(lowerCamelCase_						,  dim=0							)
				return image






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if isinstance(lowerCamelCase_						,  torch.Tensor							):
								return mask
				elif isinstance(lowerCamelCase_						,  PIL.Image.Image							):
								lowercase__  					=     [mask]

				if isinstance(mask[0]						,  PIL.Image.Image							):
								lowercase__					,		lowercase__  					=     mask[0].size
								lowercase__					,		lowercase__  					=     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
								lowercase__  					=     [np.array(m.convert('''L'''							).resize((w, h)						,  resample=PIL_INTERPOLATION['''nearest''']							)							)[None, :] for m in mask]
								lowercase__  					=     np.concatenate(lowerCamelCase_						,  axis=0							)
								lowercase__  					=     mask.astype(np.floataa							) / 2_55.0
								lowercase__  					=     0
								lowercase__  					=     1
								lowercase__  					=     torch.from_numpy(lowerCamelCase_							)
				elif isinstance(mask[0]						,  torch.Tensor							):
								lowercase__  					=     torch.cat(lowerCamelCase_						,  dim=0							)
				return mask




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			42
				lowercase__											=			42




				def __init__(	self		:       Dict,    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       int    ):

								'''simple docstring'''
								super().__init__()
								self.register_modules(unet=lowerCamelCase,    scheduler=lowerCamelCase    )




				@torch.no_grad()
				def __call__(	self		:       Any,    lowerCamelCase		:       Union[torch.Tensor, PIL.Image.Image],    lowerCamelCase		:       Union[torch.Tensor, PIL.Image.Image],    lowerCamelCase		:       int = 250,    lowerCamelCase		:       float = 0.0,    lowerCamelCase		:       int = 10,    lowerCamelCase		:       int = 10,    lowerCamelCase		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,    lowerCamelCase		:       Optional[str] = "pil",    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								lowercase__  					=     image

								lowercase__  					=     _preprocess_image(lowerCamelCase    )
								lowercase__  					=     original_image.to(device=self.device,    dtype=self.unet.dtype    )
								lowercase__  					=     _preprocess_mask(lowerCamelCase    )
								lowercase__  					=     mask_image.to(device=self.device,    dtype=self.unet.dtype    )

								lowercase__  					=     original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(lowerCamelCase,    lowerCamelCase    ) and len(lowerCamelCase    ) != batch_size:
												raise ValueError(
												    F"""You have passed a list of generators of length {len(lowerCamelCase    )}, but requested an effective batch"""
												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

								lowercase__  					=     original_image.shape
								lowercase__  					=     randn_tensor(lowerCamelCase,    generator=lowerCamelCase,    device=self.device,    dtype=self.unet.dtype    )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    self.device    )
								lowercase__  					=     eta

								lowercase__  					=     self.scheduler.timesteps[0] + 1
								lowercase__  					=     generator[0] if isinstance(lowerCamelCase,    lowerCamelCase    ) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
												if t < t_last:
																# predict the noise residual
																lowercase__  					=     self.unet(lowerCamelCase,    lowerCamelCase    ).sample
																# compute previous image: x_t -> x_t-1
																lowercase__  					=     self.scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

												else:
																# compute the reverse: x_t-1 -> x_t
																lowercase__  					=     self.scheduler.undo_step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     t

								lowercase__  					=     (image / 2 + 0.5).clamp(0,    1    )
								lowercase__  					=     image.cpu().permute(0,    2,    3,    1    ).numpy()
								if output_type == "pil":
												lowercase__  					=     self.numpy_to_pil(lowerCamelCase    )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=lowerCamelCase    )












import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
				lowercase__											=			TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any,    lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     AudioClassificationPipeline(model=lowerCamelCase,    feature_extractor=lowerCamelCase    )

								# test with a raw waveform
								lowercase__  					=     np.zeros((34_000,)    )
								lowercase__  					=     np.zeros((14_000,)    )
								return audio_classifier, [audioa, audio]




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     examples
								lowercase__  					=     audio_classifier(lowerCamelCase    )
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=1    )
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )

								self.run_torchaudio(lowerCamelCase    )




				@require_torchaudio
				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								import datasets

								# test with a local file
								lowercase__  					=     datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy''',    '''clean''',    split='''validation'''    )
								lowercase__  					=     dataset[0]['''audio''']['''array''']
								lowercase__  					=     audio_classifier(lowerCamelCase    )
								self.assertEqual(
								    lowerCamelCase,    [
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								        {'''score''': ANY(lowerCamelCase    ), '''label''': ANY(lowerCamelCase    )},
								    ],    )




				@require_torch
				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     '''anton-l/wav2vec2-random-tiny-classifier'''

								lowercase__  					=     pipeline('''audio-classification''',    model=lowerCamelCase    )

								lowercase__  					=     np.ones((8_000,)    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )

								lowercase__  					=     [
								    {'''score''': 0.0842, '''label''': '''no'''},
								    {'''score''': 0.0838, '''label''': '''up'''},
								    {'''score''': 0.0837, '''label''': '''go'''},
								    {'''score''': 0.0834, '''label''': '''right'''},
								]
								lowercase__  					=     [
								    {'''score''': 0.0845, '''label''': '''stop'''},
								    {'''score''': 0.0844, '''label''': '''on'''},
								    {'''score''': 0.0841, '''label''': '''right'''},
								    {'''score''': 0.0834, '''label''': '''left'''},
								]
								self.assertIn(nested_simplify(lowerCamelCase,    decimals=4    ),    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]    )

								lowercase__  					=     {'''array''': np.ones((8_000,)    ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )
								self.assertIn(nested_simplify(lowerCamelCase,    decimals=4    ),    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]    )




				@require_torch
				@slow
				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								import datasets

								lowercase__  					=     '''superb/wav2vec2-base-superb-ks'''

								lowercase__  					=     pipeline('''audio-classification''',    model=lowerCamelCase    )
								lowercase__  					=     datasets.load_dataset('''anton-l/superb_dummy''',    '''ks''',    split='''test'''    )

								lowercase__  					=     np.array(dataset[3]['''speech'''],    dtype=np.floataa    )
								lowercase__  					=     audio_classifier(lowerCamelCase,    top_k=4    )
								self.assertEqual(
								    nested_simplify(lowerCamelCase,    decimals=3    ),    [
								        {'''score''': 0.981, '''label''': '''go'''},
								        {'''score''': 0.007, '''label''': '''up'''},
								        {'''score''': 0.006, '''label''': '''_unknown_'''},
								        {'''score''': 0.001, '''label''': '''down'''},
								    ],    )




				@require_tf
				@unittest.skip('''Audio classification is not implemented for TF'''    )
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								pass




