
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 	UpperCAmelCase_      (			_a  ,	unittest.TestCase					):


  '''simple docstring'''







  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 							_lowercase							(			self      :			Union[str, Any]   )      ->     Tuple:







    """simple docstring"""






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

    __magic_name__    					=	{
        """unet""": unet,
        """scheduler""": scheduler,
        """vae""": vae,
        """image_encoder""": image_encoder,
        """safety_checker""": None,
        """feature_extractor""": feature_extractor,
    }
    return components
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			List[str]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	np.array(image.convert("""RGB"""   )   )
    __magic_name__    					=	image[None].transpose(0      ,							3      ,							1      ,							2   )
    __magic_name__    					=	torch.from_numpy(snake_case_   ).to(dtype=torch.floataa   ) / 127.5 - 1.0
    return image
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Tuple="cpu"      ,							UpperCamelCase__      :			List[str]=0   )      ->     Union[str, Any]:







    """simple docstring"""






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

    if str(snake_case_   ).startswith("""mps"""   ):
      __magic_name__    					=	torch.manual_seed(snake_case_   )
    else:
      __magic_name__    					=	torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )
    __magic_name__    					=	{
        """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 							_lowercase							(			self      :			int   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	self.get_dummy_components()

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

    __magic_name__    					=	self.get_dummy_inputs()
    __magic_name__    					=	pipe(**snake_case_   )
    __magic_name__    					=	output.images

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

    assert image.shape == (1, 64, 64, 3)
    __magic_name__    					=	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 							_lowercase							(			self      :			Dict   )      ->     str:







    """simple docstring"""






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

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

    __magic_name__    					=	pipe(image=snake_case_      ,							mask_image=mask_image[:, 0]      ,							**snake_case_   )
    __magic_name__    					=	output.images

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

    __magic_name__    					=	Image.fromarray(np.uinta(snake_case_   )   ).convert("""RGB"""   )
    __magic_name__    					=	Image.fromarray(np.uinta(snake_case_   )   ).convert("""RGB"""   )

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

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





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







    """simple docstring"""






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







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


  '''simple docstring'''







  def 							_lowercase							(			self      :			Any   )      ->     Optional[Any]:







    """simple docstring"""






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





  def 							_lowercase							(			self      :			List[Any]   )      ->     Tuple:







    """simple docstring"""






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

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

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

    __magic_name__    					=	output.images

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

    assert image.shape == (1, 512, 512, 3)
    __magic_name__    					=	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 random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
    pass

if is_speech_available():
    from transformers import EncodecFeatureExtractor, MusicgenProcessor


__lowerCAmelCase      :					Dict   	=       random.Random()

def a__   (	A_,					A_=1.0,					A_=None,					A_=None ):






  '''simple docstring'''





  if rng is None:
    __magic_name__    					=	global_rng

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

  return values





@require_torch
@require_sentencepiece
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







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







    """simple docstring"""






    __magic_name__    					=	"""facebook/musicgen-small"""
    __magic_name__    					=	tempfile.mkdtemp()
  def 							_lowercase							(			self      :			Tuple      ,							**UpperCamelCase__      :			str   )      ->     Any:







    """simple docstring"""






    return TaTokenizer.from_pretrained(self.checkpoint      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[int]      ,							**UpperCamelCase__      :			List[str]   )      ->     List[str]:







    """simple docstring"""






    return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     List[Any]:







    """simple docstring"""






    shutil.rmtree(self.tmpdirname   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	self.get_tokenizer()
    __magic_name__    					=	self.get_feature_extractor()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    processor.save_pretrained(self.tmpdirname   )
    __magic_name__    					=	MusicgenProcessor.from_pretrained(self.tmpdirname   )

    self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer.get_vocab()   )
    self.assertIsInstance(processor.tokenizer      ,							UpperCamelCase__   )

    self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor.to_json_string()   )
    self.assertIsInstance(processor.feature_extractor      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	MusicgenProcessor(tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()   )
    processor.save_pretrained(self.tmpdirname   )

    __magic_name__    					=	self.get_tokenizer(bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""   )
    __magic_name__    					=	self.get_feature_extractor(do_normalize=UpperCamelCase__      ,							padding_value=1.0   )

    __magic_name__    					=	MusicgenProcessor.from_pretrained(
        self.tmpdirname      ,							bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""      ,							do_normalize=UpperCamelCase__      ,							padding_value=1.0   )

    self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()   )
    self.assertIsInstance(processor.tokenizer      ,							UpperCamelCase__   )

    self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor_add_kwargs.to_json_string()   )
    self.assertIsInstance(processor.feature_extractor      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	floats_list((3, 1000)   )

    __magic_name__    					=	feature_extractor(UpperCamelCase__      ,							return_tensors="""np"""   )
    __magic_name__    					=	processor(UpperCamelCase__      ,							return_tensors="""np"""   )

    for key in input_feat_extract.keys():
      self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1E-2   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	"""This is a test string"""

    __magic_name__    					=	processor(text=UpperCamelCase__   )

    __magic_name__    					=	tokenizer(UpperCamelCase__   )

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]   )
  def 							_lowercase							(			self      :			Dict   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    __magic_name__    					=	processor.batch_decode(sequences=UpperCamelCase__   )
    __magic_name__    					=	tokenizer.batch_decode(UpperCamelCase__   )

    self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    self.assertListEqual(
        processor.model_input_names      ,							feature_extractor.model_input_names      ,							msg="""`processor` and `feature_extractor` model input names do not match"""      ,							)





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







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor(padding_side="""left"""   )
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	[floats_list((1, x)   )[0] for x in range(5      ,							20      ,							5   )]
    __magic_name__    					=	processor(UpperCamelCase__   ).padding_mask

    __magic_name__    					=	np.asarray(floats_list((3, 20)   )   )[:, None, :]
    __magic_name__    					=	processor.batch_decode(UpperCamelCase__      ,							padding_mask=UpperCamelCase__   )

    self.assertIsInstance(UpperCamelCase__      ,							UpperCamelCase__   )

    for audio in decoded_audios:
      self.assertIsInstance(UpperCamelCase__      ,							np.ndarray   )

    self.assertTrue(decoded_audios[0].shape == (1, 10)   )
    self.assertTrue(decoded_audios[1].shape == (1, 15)   )
    self.assertTrue(decoded_audios[2].shape == (1, 20)   )

