



"""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  A_     (	__UpperCAmelCase      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				PaintByExamplePipeline
						__UpperCamelCase													=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
						__UpperCamelCase													=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						__UpperCamelCase													=				frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[int]:
												torch.manual_seed(0   )
												UpperCAmelCase			 =					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  ,    )
												UpperCAmelCase			 =					PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE   )
												torch.manual_seed(0   )
												UpperCAmelCase			 =					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   )
												UpperCAmelCase			 =					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  ,    )
												UpperCAmelCase			 =					PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE  ,    proj_size=32   )
												UpperCAmelCase			 =					CLIPImageProcessor(crop_size=32  ,    size=32   )

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






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:Optional[Any]   )							->     Tuple:
												UpperCAmelCase			 =					np.array(image.convert('RGB'   )   )
												UpperCAmelCase			 =					image[None].transpose(0  ,    3  ,    1  ,    2   )
												UpperCAmelCase			 =					torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(dtype=torch.floataa   ) / 1_27.5 - 1.0
												return image






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:Dict="cpu"  ,    lowercase_					:List[str]=0   )							->     str:
												UpperCAmelCase			 =					floats_tensor((1, 3, 32, 32)  ,    rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   )[0]
												UpperCAmelCase			 =					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((64, 64)   )
												UpperCAmelCase			 =					Image.fromarray(np.uinta(image + 4   )   ).convert('RGB'   ).resize((64, 64)   )
												UpperCAmelCase			 =					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((32, 32)   )

												if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
																		UpperCAmelCase			 =					torch.manual_seed(__SCREAMING_SNAKE_CASE   )
												else:
																		UpperCAmelCase			 =					torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
												UpperCAmelCase			 =					{
												    '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					:Union[str, Any]   )							->     int:
												UpperCAmelCase			 =					self.get_dummy_components()

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

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

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

												assert image.shape == (1, 64, 64, 3)
												UpperCAmelCase			 =					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					:Any   )							->     Tuple:
												UpperCAmelCase			 =					'cpu'
												UpperCAmelCase			 =					self.get_dummy_inputs()
												inputs.pop('mask_image'   )
												UpperCAmelCase			 =					self.convert_to_pt(inputs.pop('image'   )   )
												UpperCAmelCase			 =					image.clamp(0  ,    1   ) / 2

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

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

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

												UpperCAmelCase			 =					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   )
												UpperCAmelCase			 =					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   )

												UpperCAmelCase			 =					pipe(**self.get_dummy_inputs()   )
												UpperCAmelCase			 =					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					:List[Any]   )							->     str:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3   )







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



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[str]:
												UpperCAmelCase			 =					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/paint_by_example/dog_in_bucket.png'   )
												UpperCAmelCase			 =					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/paint_by_example/mask.png'   )
												UpperCAmelCase			 =					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/paint_by_example/panda.jpg'   )

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

												UpperCAmelCase			 =					torch.manual_seed(3_21   )
												UpperCAmelCase			 =					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'  ,    )

												UpperCAmelCase			 =					output.images

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

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










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


__snake_case		:Any 						=	random.Random()





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=1.0  ,					_UpperCAmelCase=None  ,					_UpperCAmelCase=None							):
    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        _A			(      unittest.TestCase       ):



       def __init__(						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Tuple=7	,						__SCREAMING_SNAKE_CASE				:	Any=400	,						__SCREAMING_SNAKE_CASE				:	Any=2_000	,						__SCREAMING_SNAKE_CASE				:	int=1	,						__SCREAMING_SNAKE_CASE				:	Dict=0.0	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=24_000	,						__SCREAMING_SNAKE_CASE				:	Dict=True	,						):


           '''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 						_lowerCamelCase     (						self				:	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 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=False	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=False):


           '''simple docstring'''

           def _flatten(__SCREAMING_SNAKE_CASE				:	List[str]):
               return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

           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(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

           return audio_inputs


@require_torch
class        _A			(      __UpperCAmelCase ,unittest.TestCase       ):
       UpperCamelCase__    :       str		   =		EncodecFeatureExtractor



       def 						_lowerCamelCase     (						self				:	Union[str, Any]):


           '''simple docstring'''

           __a					=							EnCodecFeatureExtractionTester(self)



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''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(800	,						1_400	,						200)]
           __a					=							[np.asarray(__SCREAMING_SNAKE_CASE) 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(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						atol=1E-3))

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



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a					=							np.random.rand(100).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 						_lowerCamelCase     (						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	str):


           '''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(__SCREAMING_SNAKE_CASE))[:num_samples]['''audio''']

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



       def 						_lowerCamelCase     (						self				:	int):


           '''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(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(input_values.shape	,						(1, 1, 93_680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]	,						__SCREAMING_SNAKE_CASE	,						atol=1E-6))



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''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(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(input_values.shape	,						(1, 2, 93_680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]	,						__SCREAMING_SNAKE_CASE	,						atol=1E-6))
           self.assertTrue(torch.allclose(input_values[0, 1, :30]	,						EXPECTED_INPUT_VALUES * 0.5	,						atol=1E-6))





       def 						_lowerCamelCase     (						self				:	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(
               __SCREAMING_SNAKE_CASE	,						'''^Both padding and truncation were set. Make sure you only set one.$'''	,						):
               __a					=							feature_extractor(
                   __SCREAMING_SNAKE_CASE	,						padding='''max_length'''	,						truncation=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           # truncate to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						truncation=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 71_520))  # 2 chunks

           # force truncate to max_length
           __a					=							feature_extractor(
               __SCREAMING_SNAKE_CASE	,						truncation=__SCREAMING_SNAKE_CASE	,						max_length=48_000	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 48_000))

           # pad to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(padded_outputs.shape	,						(2, 1, 95_280))

           # pad to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 95_280))

           # force pad to max length
           __a					=							feature_extractor(
               __SCREAMING_SNAKE_CASE	,						padding='''max_length'''	,						max_length=100_000	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 100_000))

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

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))

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

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))

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

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))





