

from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    				=     '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


__UpperCAmelCase    				=     '\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


__UpperCAmelCase    				=     '\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,		_KWARGS_DESCRIPTION )
class A__    ( datasets.Metric ):



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      return datasets.MetricInfo(
          description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(
              {
                  "predictions": datasets.Value("float"       ),
                  "references": datasets.Value("float"       ),
              }       )     ,						reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"]     ,						)


 def    __magic_name__       (		self :	str     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	Dict=False       ):






      '''simple docstring'''




      if return_pvalue:
           _lowerCAmelCase   :					Optional[int]        =    pearsonr(A_     ,						A_       )
           return {"pearsonr": results[0], "p-value": results[1]}
      else:
           return {"pearsonr": float(pearsonr(A_     ,						A_       )[0]       )}







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					List[str]      =						KandinskyVaaControlnetImgaImgPipeline
 _lowercase     :					Tuple      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					str      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					Optional[Any]      =						[
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowercase     :					Tuple      =						False

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      return self.time_input_dim

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.time_input_dim * 4

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 1_0_0

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      torch.manual_seed(0       )

      _lowerCAmelCase   :					Tuple        =    {
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      _lowerCAmelCase   :					Optional[int]        =    UNetaDConditionModel(**A_       )
      return model

 @property
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      return {
          "block_out_channels": [3_2, 3_2, 6_4, 6_4],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 1_2,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }

 @property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					List[str]        =    VQModel(**self.dummy_movq_kwargs       )
      return model

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.dummy_unet
      _lowerCAmelCase   :					List[Any]        =    self.dummy_movq

      _lowerCAmelCase   :					Tuple        =    {
          "num_train_timesteps": 1_0_0_0,
          "beta_schedule": "linear",
          "beta_start": 0.00085,
          "beta_end": 0.012,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      _lowerCAmelCase   :					int        =    DDIMScheduler(**A_       )

      _lowerCAmelCase   :					Tuple        =    {
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Optional[int]=0       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					str        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(seed + 1       )       ).to(
          A_       )
      # create init_image
      _lowerCAmelCase   :					Dict        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					Dict        =    image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((2_5_6, 2_5_6)       )
      # create hint
      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )

      if str(A_       ).startswith("mps"       ):
           _lowerCAmelCase   :					Tuple        =    torch.manual_seed(A_       )
      else:
           _lowerCAmelCase   :					Any        =    torch.Generator(device=A_       ).manual_seed(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    {
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 1_0,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "cpu"

      _lowerCAmelCase   :					Tuple        =    self.get_dummy_components()

      _lowerCAmelCase   :					Optional[int]        =    self.pipeline_class(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    pipe.to(A_       )

      pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    pipe(**self.get_dummy_inputs(A_       )       )
      _lowerCAmelCase   :					List[Any]        =    output.images

      _lowerCAmelCase   :					str        =    pipe(
          **self.get_dummy_inputs(A_       )     ,						return_dict=A_     ,						)[0]

      _lowerCAmelCase   :					int        =    image[0, -3:, -3:, -1]
      _lowerCAmelCase   :					List[str]        =    image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      _lowerCAmelCase   :					List[Any]        =    np.array(
          [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]       )
      assert (
          np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"       )

      _lowerCAmelCase   :					str        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
      _lowerCAmelCase   :					Union[str, Any]        =    init_image.resize((5_1_2, 5_1_2)       )

      _lowerCAmelCase   :					Dict        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png"       )
      _lowerCAmelCase   :					Tuple        =    torch.from_numpy(np.array(A_       )       ).float() / 255.0
      _lowerCAmelCase   :					Optional[int]        =    hint.permute(2     ,						0     ,						1       ).unsqueeze(0       )

      _lowerCAmelCase   :					List[str]        =    "A robot, 4k photo"

      _lowerCAmelCase   :					Dict        =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"     ,						torch_dtype=torch.floataa       )
      pipe_prior.to(A_       )

      _lowerCAmelCase   :					str        =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,						torch_dtype=torch.floataa       )
      _lowerCAmelCase   :					Optional[int]        =    pipeline.to(A_       )

      pipeline.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					Dict        =    torch.Generator(device="cpu"       ).manual_seed(0       )

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    pipe_prior(
          A_     ,						image=A_     ,						strength=0.85     ,						generator=A_     ,						negative_prompt=""     ,						).to_tuple()

      _lowerCAmelCase   :					List[Any]        =    pipeline(
          image=A_     ,						image_embeds=A_     ,						negative_image_embeds=A_     ,						hint=A_     ,						generator=A_     ,						num_inference_steps=1_0_0     ,						height=5_1_2     ,						width=5_1_2     ,						strength=0.5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					str        =    output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)

      assert_mean_pixel_difference(A_     ,						A_       )






