




from scipy.stats import spearmanr

import datasets


_snake_case            = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"

_snake_case            = "\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {'spearmanr': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results['spearmanr'])\n        -0.7\n        >>> print(round(results['spearmanr_pvalue'], 2))\n        0.19\n"

_snake_case            = r"\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {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, {\.I}lhan 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, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n"







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

     def       a__			(						self       )		->							str:
           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.spearmanr.html"""]	,  )



     def       a__			(						self	,  _a	,  _a	,  _a=False       )		->							Dict:
           _A							:  int    =		spearmanr(_a	,  _a       )
           if return_pvalue:
                 return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
           else:
                 return {"spearmanr": results[0]}







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    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 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		KandinskyVaaImgaImgPipeline
     _a											=		["image_embeds", "negative_image_embeds", "image"]
     _a											=		[
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _a											=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _a											=		False

     @property
     def       a__			(						self       )		->							int:
           return 32

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return 32

     @property
     def       a__			(						self       )		->							List[str]:
           return self.time_input_dim

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           return self.time_input_dim * 4

     @property
     def       a__			(						self       )		->							str:
           return 100

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )

           _A							:  str    =		{
               """in_channels""": 4,
               # Out channels is double in channels because predicts mean and variance
               """out_channels""": 8,
               """addition_embed_type""": """image""",
               """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,
           }

           _A							:  Union[str, Any]    =		UNetaDConditionModel(**_a       )
           return model

     @property
     def       a__			(						self       )		->							int:
           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }

     @property
     def       a__			(						self       )		->							Tuple:
           torch.manual_seed(0       )
           _A							:  Dict    =		VQModel(**self.dummy_movq_kwargs       )
           return model

     def       a__			(						self       )		->							int:
           _A							:  Any    =		self.dummy_unet
           _A							:  List[Any]    =		self.dummy_movq

           _A							:  str    =		{
               """num_train_timesteps""": 1000,
               """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,
           }

           _A							:  int    =		DDIMScheduler(**_a       )

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

           return components

     def       a__			(						self	,  _a	,  _a=0       )		->							str:
           _A							:  Optional[int]    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Dict    =		floats_tensor((1, self.text_embedder_hidden_size)	,  rng=random.Random(seed + 1       )       ).to(
               _a       )
           # create init_image
           _A							:  Optional[Any]    =		floats_tensor((1, 3, 64, 64)	,  rng=random.Random(_a       )       ).to(_a       )
           _A							:  Optional[Any]    =		image.cpu().permute(0	,  2	,  3	,  1       )[0]
           _A							:  Optional[Any]    =		Image.fromarray(np.uinta(_a       )       ).convert("""RGB"""       ).resize((256, 256)       )

           if str(_a       ).startswith("""mps"""       ):
                 _A							:  Tuple    =		torch.manual_seed(_a       )
           else:
                 _A							:  str    =		torch.Generator(device=_a       ).manual_seed(_a       )
           _A							:  Optional[Any]    =		{
               """image""": init_image,
               """image_embeds""": image_embeds,
               """negative_image_embeds""": negative_image_embeds,
               """generator""": generator,
               """height""": 64,
               """width""": 64,
               """num_inference_steps""": 10,
               """guidance_scale""": 7.0,
               """strength""": 0.2,
               """output_type""": """np""",
           }
           return inputs



     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Dict    =		"""cpu"""

           _A							:  int    =		self.get_dummy_components()

           _A							:  Optional[int]    =		self.pipeline_class(**_a       )
           _A							:  Any    =		pipe.to(_a       )

           pipe.set_progress_bar_config(disable=_a       )

           _A							:  List[Any]    =		pipe(**self.get_dummy_inputs(_a       )       )
           _A							:  Dict    =		output.images

           _A							:  List[str]    =		pipe(
               **self.get_dummy_inputs(_a       )	,  return_dict=_a	,  )[0]

           _A							:  Dict    =		image[0, -3:, -3:, -1]
           _A							:  Union[str, Any]    =		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           _A							:  Optional[int]    =		np.array(
               [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]       )
           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 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							List[Any]:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     def       a__			(						self       )		->							List[str]:
           _A							:  Any    =		load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_img2img_frog.npy"""       )

           _A							:  Union[str, Any]    =		load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
           _A							:  Dict    =		"""A red cartoon frog, 4k"""

           _A							:  Dict    =		KandinskyVaaPriorPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""	,  torch_dtype=torch.floataa       )
           pipe_prior.to(_a       )

           _A							:  int    =		KandinskyVaaImgaImgPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-decoder"""	,  torch_dtype=torch.floataa       )
           _A							:  Dict    =		pipeline.to(_a       )

           pipeline.set_progress_bar_config(disable=_a       )

           _A							:  Tuple    =		torch.Generator(device="""cpu"""       ).manual_seed(0       )
           _A    ,     _A							:  List[str]    =		pipe_prior(
               _a	,  generator=_a	,  num_inference_steps=5	,  negative_prompt=""""""	,  ).to_tuple()

           _A							:  int    =		pipeline(
               image=_a	,  image_embeds=_a	,  negative_image_embeds=_a	,  generator=_a	,  num_inference_steps=100	,  height=768	,  width=768	,  strength=0.2	,  output_type="""np"""	,  )

           _A							:  Optional[int]    =		output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(_a	,  _a       )

