




'''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   _snake_case( UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       Any									=				PaintByExamplePipeline
 __snake_case:       int									=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case:       Union[str, Any]									=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case:       List[str]									=				frozenset([]			)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






 def 	_UpperCamelCase   (self					:     int						)					->      str:






    """simple docstring"""


    torch.manual_seed(0						)
    A__         =    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						,		)
    A__         =    PNDMScheduler(skip_prk_steps=a						)
    torch.manual_seed(0						)
    A__         =    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						)
    A__         =    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						,		)
    A__         =    PaintByExampleImageEncoder(a						,		proj_size=32						)
    A__         =    CLIPImageProcessor(crop_size=32						,		size=32						)

    A__         =    {
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
        'image_encoder': image_encoder,
        'safety_checker': None,
        'feature_extractor': feature_extractor,
    }
    return components






 def 	_UpperCamelCase   (self					:     List[Any]						,		a					:     Tuple						)					->      Optional[Any]:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     Any						,		a					:     Dict="cpu"						,		a					:     Optional[Any]=0						)					->      Any:






    """simple docstring"""


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

    if str(a						).startswith('mps'						):
       A__         =    torch.manual_seed(a						)
    else:
       A__         =    torch.Generator(device=a						).manual_seed(a						)
    A__         =    {
        '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					:     Tuple						)					->      int:






    """simple docstring"""


    A__         =    self.get_dummy_components()

    # make sure here that pndm scheduler skips prk
    A__         =    PaintByExamplePipeline(**a						)
    A__         =    pipe.to('cpu'						)
    pipe.set_progress_bar_config(disable=a						)

    A__         =    self.get_dummy_inputs()
    A__         =    pipe(**a						)
    A__         =    output.images

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

    assert image.shape == (1, 64, 64, 3)
    A__         =    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 	_UpperCamelCase   (self					:     List[Any]						)					->      Dict:






    """simple docstring"""


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

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

    A__         =    pipe(image=a						,		mask_image=mask_image[:, 0]						,		**a						)
    A__         =    output.images

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

    A__         =    Image.fromarray(np.uinta(a						)						).convert('RGB'						)
    A__         =    Image.fromarray(np.uinta(a						)						).convert('RGB'						)

    A__         =    pipe(**self.get_dummy_inputs()						)
    A__         =    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					:     Optional[int]						)					->      Optional[int]:






    """simple docstring"""


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





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






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      Tuple:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Optional[int]:






    """simple docstring"""


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

    A__         =    PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'						)
    A__         =    pipe.to(a						)
    pipe.set_progress_bar_config(disable=a						)

    A__         =    torch.manual_seed(3_21						)
    A__         =    pipe(
        image=a						,		mask_image=a						,		example_image=a						,		generator=a						,		guidance_scale=5.0						,		num_inference_steps=50						,		output_type='np'						,		)

    A__         =    output.images

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

    assert image.shape == (1, 5_12, 5_12, 3)
    A__         =    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








'''simple docstring'''







import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


lowerCAmelCase_    	=							random.Random()







def 	_A			(   UpperCAmelCase			,UpperCAmelCase=1.0			,UpperCAmelCase=None			,UpperCAmelCase=None	):






   '''simple docstring'''







   if rng is None:
      A__         =    global_rng

   A__         =    []
   for batch_idx in range(shape[0]	):
      values.append([]	)
      for _ in range(shape[1]	):
         values[-1].append(rng.random() * scale	)

   return values

@require_torch
class   _snake_case( unittest.TestCase			):






 def __init__(self					:     int						,		a					:     str						,		a					:     Optional[int]=7						,		a					:     Optional[Any]=4_00						,		a					:     Any=20_00						,		a					:     Optional[Any]=1						,		a					:     Optional[int]=0.0						,		a					:     Dict=2_40_00						,		a					:     Tuple=True						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    parent
    A__         =    batch_size
    A__         =    min_seq_length
    A__         =    max_seq_length
    A__         =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    A__         =    feature_size
    A__         =    padding_value
    A__         =    sampling_rate
    A__         =    return_attention_mask






 def 	_UpperCamelCase   (self					:     int						)					->      List[str]:






    """simple docstring"""


    return {
        "feature_size": self.feature_size,
        "padding_value": self.padding_value,
        "sampling_rate": self.sampling_rate,
        "return_attention_mask": self.return_attention_mask,
    }






 def 	_UpperCamelCase   (self					:     List[Any]						,		a					:     Any=False						,		a					:     Optional[Any]=False						)					->      Tuple:






    """simple docstring"""


    def _flatten(a					:     Optional[Any]						):
       return list(itertools.chain(*a						)						)

    if equal_length:
       A__         =    floats_list((self.batch_size, self.max_seq_length)						)
    else:
       # make sure that inputs increase in size
       A__         =    [
           _flatten(floats_list((x, self.feature_size)						)						)
           for x in range(self.min_seq_length						,		self.max_seq_length						,		self.seq_length_diff						)
       ]

    if numpify:
       A__         =    [np.asarray(a						) for x in audio_inputs]

    return audio_inputs




@require_torch
class   _snake_case( UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       Union[str, Any]									=				EncodecFeatureExtractor






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






    """simple docstring"""


    A__         =    EnCodecFeatureExtractionTester(self						)






 def 	_UpperCamelCase   (self					:     Dict						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
    # create three inputs of length 800, 1000, and 1200
    A__         =    [floats_list((1, x)						)[0] for x in range(8_00						,		14_00						,		2_00						)]
    A__         =    [np.asarray(a						) for audio_input in audio_inputs]

    # Test not batched input
    A__         =    feat_extract(audio_inputs[0]						,		return_tensors='np'						).input_values
    A__         =    feat_extract(np_audio_inputs[0]						,		return_tensors='np'						).input_values
    self.assertTrue(np.allclose(a						,		a						,		atol=1e-3						)						)

    # Test batched
    A__         =    feat_extract(a						,		padding=a						,		return_tensors='np'						).input_values
    A__         =    feat_extract(a						,		padding=a						,		return_tensors='np'						).input_values
    for enc_seq_a, enc_seq_a in zip(a						,		a						):
       self.assertTrue(np.allclose(a						,		a						,		atol=1e-3						)						)






 def 	_UpperCamelCase   (self					:     Tuple						)					->      Any:






    """simple docstring"""


    A__         =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
    A__         =    np.random.rand(1_00						).astype(np.floataa						)
    A__         =    np_audio_inputs.tolist()

    for inputs in [py_audio_inputs, np_audio_inputs]:
       A__         =    feature_extractor.pad([{'input_values': inputs}]						,		return_tensors='np'						)
       self.assertTrue(np_processed.input_values.dtype == np.floataa						)
       A__         =    feature_extractor.pad([{'input_values': inputs}]						,		return_tensors='pt'						)
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)






 def 	_UpperCamelCase   (self					:     Optional[int]						,		a					:     Optional[Any]						)					->      Union[str, Any]:






    """simple docstring"""


    from datasets import load_dataset

    A__         =    load_dataset('hf-internal-testing/librispeech_asr_dummy'						,		'clean'						,		split='validation'						)
    # automatic decoding with librispeech
    A__         =    ds.sort('id'						).select(range(a						)						)[:num_samples]['audio']

    return [x["array"] for x in audio_samples]






 def 	_UpperCamelCase   (self					:     Tuple						)					->      Tuple:






    """simple docstring"""


    A__         =    torch.tensor(
        [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
         3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
         2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
         4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
         7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
         4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]						)
    # fmt: on
    A__         =    self._load_datasamples(1						)
    A__         =    EncodecFeatureExtractor()
    A__         =    feature_extractor(a						,		return_tensors='pt'						).input_values
    self.assertEquals(input_values.shape						,		(1, 1, 9_36_80)						)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]						,		a						,		atol=1e-6						)						)






 def 	_UpperCamelCase   (self					:     int						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    torch.tensor(
        [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
         3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
         2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
         4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
         7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
         4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]						)
    # fmt: on
    A__         =    self._load_datasamples(1						)
    A__         =    [np.tile(input_audio[0][None]						,		reps=(2, 1)						)]
    input_audio[0][1] *= 0.5
    A__         =    EncodecFeatureExtractor(feature_size=2						)
    A__         =    feature_extractor(a						,		return_tensors='pt'						).input_values
    self.assertEquals(input_values.shape						,		(1, 2, 9_36_80)						)
    self.assertTrue(torch.allclose(input_values[0, 0, :30]						,		a						,		atol=1e-6						)						)
    self.assertTrue(torch.allclose(input_values[0, 1, :30]						,		EXPECTED_INPUT_VALUES * 0.5						,		atol=1e-6						)						)






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






    """simple docstring"""


    A__         =    self._load_datasamples(2						)
    # would be easier if the stride was like
    A__         =    EncodecFeatureExtractor(feature_size=1						,		chunk_length_s=1						,		overlap=0.01						)

    # pad and trunc raise an error ?
    with self.assertRaisesRegex(
        a						,		'^Both padding and truncation were set. Make sure you only set one.$'						,		):
       A__         =    feature_extractor(
           a						,		padding='max_length'						,		truncation=a						,		return_tensors='pt'						).input_values

    # truncate to chunk
    A__         =    feature_extractor(a						,		truncation=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 7_15_20)						)  # 2 chunks

    # force truncate to max_length
    A__         =    feature_extractor(
        a						,		truncation=a						,		max_length=4_80_00						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 4_80_00)						)

    # pad to chunk
    A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(padded_outputs.shape						,		(2, 1, 9_52_80)						)

    # pad to chunk
    A__         =    feature_extractor(a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 9_52_80)						)

    # force pad to max length
    A__         =    feature_extractor(
        a						,		padding='max_length'						,		max_length=10_00_00						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(2, 1, 10_00_00)						)

    # force no pad
    with self.assertRaisesRegex(
        a						,		'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'						,		):
       A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values

    A__         =    feature_extractor(input_audio[0]						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(1, 1, 9_36_80)						)

    # no pad if no chunk_length_s
    A__         =    None
    with self.assertRaisesRegex(
        a						,		'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'						,		):
       A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values

    A__         =    feature_extractor(input_audio[0]						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(1, 1, 9_36_80)						)

    # no pad if no overlap
    A__         =    2
    A__         =    None
    with self.assertRaisesRegex(
        a						,		'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'						,		):
       A__         =    feature_extractor(a						,		padding=a						,		return_tensors='pt'						).input_values

    A__         =    feature_extractor(input_audio[0]						,		padding=a						,		return_tensors='pt'						).input_values
    self.assertEquals(truncated_outputs.shape						,		(1, 1, 9_36_80)						)


