

from scipy.stats import spearmanr

import datasets


_SCREAMING_SNAKE_CASE  :						Any         =    '''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

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

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

_SCREAMING_SNAKE_CASE  :						str         =    r'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''






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






  """simple docstring"""


  def    lowercase_	(							self		:	Dict      )	->							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    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	str=False      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					spearmanr(__lowerCamelCase			,     __lowerCamelCase      )
       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 transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      KandinskyImgaImgPipeline
  a     =      ["prompt", "image_embeds", "negative_image_embeds", "image"]
  a     =      [
      "prompt",
      "negative_prompt",
      "image_embeds",
      "negative_image_embeds",
      "image",
  ]
  a     =      [
      "generator",
      "height",
      "width",
      "strength",
      "guidance_scale",
      "negative_prompt",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  a     =      False






  @property
  def    lowercase_	(							self		:	str      )	->							List[str]:
       return 32






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							int:
       return 32






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       return self.time_input_dim






  @property
  def    lowercase_	(							self		:	List[str]      )	->							int:
       return self.time_input_dim * 4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							Any:
       return 100






  @property
  def    lowercase_	(							self		:	Any      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
       return tokenizer






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					MCLIPConfig(
           numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=37			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1005			,     )

       SCREAMING_SNAKE_CASE__					=					MultilingualCLIP(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder.eval()

       return text_encoder






  @property
  def    lowercase_	(							self		:	str      )	->							Dict:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					{
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''text_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''': '''text_image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(**__lowerCamelCase      )
       return model






  @property
  def    lowercase_	(							self		:	Dict      )	->							Optional[Any]:
       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    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					VQModel(**self.dummy_movq_kwargs      )
       return model






  def    lowercase_	(							self		:	int      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__					=					self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__					=					self.dummy_unet
       SCREAMING_SNAKE_CASE__					=					self.dummy_movq

       SCREAMING_SNAKE_CASE__					=					{
           '''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,
       }

       SCREAMING_SNAKE_CASE__					=					DDIMScheduler(**__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					{
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]=0      )	->							str:
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(__lowerCamelCase      )
       # create init_image
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 64, 64)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					image.cpu().permute(0			,     2			,     3			,     1      )[0]
       SCREAMING_SNAKE_CASE__					=					Image.fromarray(np.uinta(__lowerCamelCase      )      ).convert('''RGB'''      ).resize((256, 256)      )

       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''horse''',
           '''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    lowercase_	(							self		:	int      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()

       SCREAMING_SNAKE_CASE__					=					self.pipeline_class(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )

       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					pipe(**self.get_dummy_inputs(__lowerCamelCase      )      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					pipe(
           **self.get_dummy_inputs(__lowerCamelCase      )			,     return_dict=__lowerCamelCase			,     )[0]

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__					=					image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
       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  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def    lowercase_	(							self		:	Tuple      )	->							Dict:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def    lowercase_	(							self		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinsky/kandinsky_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__					=					'''A red cartoon frog, 4k'''

       SCREAMING_SNAKE_CASE__					=					KandinskyPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1-prior'''			,     torch_dtype=torch.floataa      )
       pipe_prior.to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					KandinskyImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1'''			,     torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__					=					pipeline.to(__lowerCamelCase      )

       pipeline.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					pipe_prior(
           __lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=5			,     negative_prompt=''''''			,     ).to_tuple()

       SCREAMING_SNAKE_CASE__					=					pipeline(
           __lowerCamelCase			,     image=__lowerCamelCase			,     image_embeds=__lowerCamelCase			,     negative_image_embeds=__lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=100			,     height=768			,     width=768			,     strength=0.2			,     output_type='''np'''			,     )

       SCREAMING_SNAKE_CASE__					=					output.images[0]

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

       assert_mean_pixel_difference(__lowerCamelCase			,     __lowerCamelCase      )


