




"""simple docstring"""







from scipy.stats import pearsonr

import datasets


SCREAMING_SNAKE_CASE_	     =       '\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'


SCREAMING_SNAKE_CASE_	     =       '\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'


SCREAMING_SNAKE_CASE_	     =       '\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 				snake_case_			(					datasets.Metric			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   Tuple:
       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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_=False)				->   Any:
       if return_pvalue:
           UpperCamelCase              =	pearsonr(lowerCamelCase_       ,					lowerCamelCase_)
           return {"pearsonr": results[0], "p-value": results[1]}
       else:
           return {"pearsonr": float(pearsonr(lowerCamelCase_       ,					lowerCamelCase_)[0])}




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class     __a			(  SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							UnCLIPImageVariationPipeline
	SCREAMING_SNAKE_CASE  	=							IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
	SCREAMING_SNAKE_CASE  	=							IMAGE_VARIATION_BATCH_PARAMS

	SCREAMING_SNAKE_CASE  	=							[
	    "generator",
	    "return_dict",
	    "decoder_num_inference_steps",
	    "super_res_num_inference_steps",
	]
	SCREAMING_SNAKE_CASE  	=							False



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



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



	@property
	def 		UpperCamelCase				(    self				:     Dict)->				Dict:
						return self.time_input_dim



	@property
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[int]:
						return self.time_input_dim * 4



	@property
	def 		UpperCamelCase				(    self				:     str)->				str:
						return 1_00



	@property
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Union[str, Any]:
						__lowerCAmelCase						 =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
						return tokenizer



	@property
	def 		UpperCamelCase				(    self				:     List[str])->				List[Any]:
						torch.manual_seed(0)
						__lowerCAmelCase						 =CLIPTextConfig(
						    bos_token_id=0					,						eos_token_id=2					,						hidden_size=self.text_embedder_hidden_size					,						projection_dim=self.text_embedder_hidden_size					,						intermediate_size=37					,						layer_norm_eps=1e-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=10_00					,						)
						return CLIPTextModelWithProjection(snake_case_)



	@property
	def 		UpperCamelCase				(    self				:     List[str])->				Dict:
						torch.manual_seed(0)
						__lowerCAmelCase						 =CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size					,						projection_dim=self.text_embedder_hidden_size					,						num_hidden_layers=5					,						num_attention_heads=4					,						image_size=32					,						intermediate_size=37					,						patch_size=1					,						)
						return CLIPVisionModelWithProjection(snake_case_)



	@property
	def 		UpperCamelCase				(    self				:     str)->				Union[str, Any]:
						torch.manual_seed(0)

						__lowerCAmelCase						 ={
						    """clip_embeddings_dim""": self.text_embedder_hidden_size,
						    """time_embed_dim""": self.time_embed_dim,
						    """cross_attention_dim""": self.cross_attention_dim,
						}

						__lowerCAmelCase						 =UnCLIPTextProjModel(**snake_case_)
						return model



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				List[str]:
						torch.manual_seed(0)

						__lowerCAmelCase						 ={
						    """sample_size""": 32,
						    # RGB in channels
						    """in_channels""": 3,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 6,
						    """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,
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": """identity""",
						}

						__lowerCAmelCase						 =UNetaDConditionModel(**snake_case_)
						return model



	@property
	def 		UpperCamelCase				(    self				:     int)->				str:
						return {
						    "sample_size": 64,
						    "layers_per_block": 1,
						    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
						    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "in_channels": 6,
						    "out_channels": 3,
						}



	@property
	def 		UpperCamelCase				(    self				:     Optional[int])->				List[Any]:
						torch.manual_seed(0)

						__lowerCAmelCase						 =UNetaDModel(**self.dummy_super_res_kwargs)
						return model



	@property
	def 		UpperCamelCase				(    self				:     Tuple)->				int:
						# seeded differently to get different unet than `self.dummy_super_res_first`
						torch.manual_seed(1)

						__lowerCAmelCase						 =UNetaDModel(**self.dummy_super_res_kwargs)
						return model



	def 		UpperCamelCase				(    self				:     List[Any])->				Tuple:
						__lowerCAmelCase						 =self.dummy_decoder
						__lowerCAmelCase						 =self.dummy_text_proj
						__lowerCAmelCase						 =self.dummy_text_encoder
						__lowerCAmelCase						 =self.dummy_tokenizer
						__lowerCAmelCase						 =self.dummy_super_res_first
						__lowerCAmelCase						 =self.dummy_super_res_last

						__lowerCAmelCase						 =UnCLIPScheduler(
						    variance_type="""learned_range"""					,						prediction_type="""epsilon"""					,						num_train_timesteps=10_00					,						)

						__lowerCAmelCase						 =UnCLIPScheduler(
						    variance_type="""fixed_small_log"""					,						prediction_type="""epsilon"""					,						num_train_timesteps=10_00					,						)

						__lowerCAmelCase						 =CLIPImageProcessor(crop_size=32					,						size=32)

						__lowerCAmelCase						 =self.dummy_image_encoder

						return {
						    "decoder": decoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "text_proj": text_proj,
						    "feature_extractor": feature_extractor,
						    "image_encoder": image_encoder,
						    "super_res_first": super_res_first,
						    "super_res_last": super_res_last,
						    "decoder_scheduler": decoder_scheduler,
						    "super_res_scheduler": super_res_scheduler,
						}



	def 		UpperCamelCase				(    self				:     Optional[int]					,						snake_case_				:     Optional[int]					,						snake_case_				:     Optional[int]=0					,						snake_case_				:     List[str]=True)->				List[str]:
						__lowerCAmelCase						 =floats_tensor((1, 3, 32, 32)					,						rng=random.Random(snake_case_)).to(snake_case_)
						if str(snake_case_).startswith("""mps"""):
											__lowerCAmelCase						 =torch.manual_seed(snake_case_)
						else:
											__lowerCAmelCase						 =torch.Generator(device=snake_case_).manual_seed(snake_case_)

						if pil_image:
											__lowerCAmelCase						 =input_image * 0.5 + 0.5
											__lowerCAmelCase						 =input_image.clamp(0					,						1)
											__lowerCAmelCase						 =input_image.cpu().permute(0					,						2					,						3					,						1).float().numpy()
											__lowerCAmelCase						 =DiffusionPipeline.numpy_to_pil(snake_case_)[0]

						return {
						    "image": input_image,
						    "generator": generator,
						    "decoder_num_inference_steps": 2,
						    "super_res_num_inference_steps": 2,
						    "output_type": "np",
						}



	def 		UpperCamelCase				(    self				:     Optional[Any])->				str:
						__lowerCAmelCase						 ="""cpu"""

						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(**snake_case_)
						__lowerCAmelCase						 =output.images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						return_dict=snake_case_					,						)[0]

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

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

						__lowerCAmelCase						 =np.array(
						    [
						        0.9_9_9_7,
						        0.0_0_0_2,
						        0.9_9_9_7,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.0_0_2_3,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.9_9_7_0,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2



	def 		UpperCamelCase				(    self				:     Optional[Any])->				List[Any]:
						__lowerCAmelCase						 ="""cpu"""

						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(**snake_case_)
						__lowerCAmelCase						 =output.images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						return_dict=snake_case_					,						)[0]

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

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

						__lowerCAmelCase						 =np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2



	def 		UpperCamelCase				(    self				:     int)->				Optional[Any]:
						__lowerCAmelCase						 ="""cpu"""

						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)
						__lowerCAmelCase						 =[
						    pipeline_inputs["""image"""],
						    pipeline_inputs["""image"""],
						]

						__lowerCAmelCase						 =pipe(**snake_case_)
						__lowerCAmelCase						 =output.images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)
						__lowerCAmelCase						 =[
						    tuple_pipeline_inputs["""image"""],
						    tuple_pipeline_inputs["""image"""],
						]

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						return_dict=snake_case_					,						)[0]

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

						assert image.shape == (2, 64, 64, 3)

						__lowerCAmelCase						 =np.array(
						    [
						        0.9_9_9_7,
						        0.9_9_8_9,
						        0.0_0_0_8,
						        0.0_0_2_1,
						        0.9_9_6_0,
						        0.0_0_1_8,
						        0.0_0_1_4,
						        0.0_0_0_2,
						        0.9_9_3_3,
						    ])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2



	def 		UpperCamelCase				(    self				:     Tuple)->				Optional[int]:
						__lowerCAmelCase						 =torch.device("""cpu""")






						class     __a			:
							SCREAMING_SNAKE_CASE  	=							1


						__lowerCAmelCase						 =self.get_dummy_components()

						__lowerCAmelCase						 =self.pipeline_class(**snake_case_)
						__lowerCAmelCase						 =pipe.to(snake_case_)

						pipe.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =torch.Generator(device=snake_case_).manual_seed(0)
						__lowerCAmelCase						 =pipe.decoder.dtype
						__lowerCAmelCase						 =1

						__lowerCAmelCase						 =(
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						__lowerCAmelCase						 =pipe.prepare_latents(
						    snake_case_					,						dtype=snake_case_					,						device=snake_case_					,						generator=snake_case_					,						latents=snake_case_					,						scheduler=DummyScheduler())

						__lowerCAmelCase						 =(
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						__lowerCAmelCase						 =pipe.prepare_latents(
						    snake_case_					,						dtype=snake_case_					,						device=snake_case_					,						generator=snake_case_					,						latents=snake_case_					,						scheduler=DummyScheduler())

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						decoder_latents=snake_case_					,						super_res_latents=snake_case_).images

						__lowerCAmelCase						 =self.get_dummy_inputs(snake_case_					,						pil_image=snake_case_)
						# Don't pass image, instead pass embedding
						__lowerCAmelCase						 =pipeline_inputs.pop("""image""")
						__lowerCAmelCase						 =pipe.image_encoder(snake_case_).image_embeds

						__lowerCAmelCase						 =pipe(
						    **snake_case_					,						decoder_latents=snake_case_					,						super_res_latents=snake_case_					,						image_embeddings=snake_case_					,						).images

						# make sure passing text embeddings manually is identical
						assert np.abs(img_out_a - img_out_a).max() < 1e-4



	@skip_mps
	def 		UpperCamelCase				(    self				:     Optional[int])->				Optional[int]:
						__lowerCAmelCase						 =torch_device == """cpu"""

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						__lowerCAmelCase						 =1e-2

						self._test_attention_slicing_forward_pass(
						    test_max_difference=snake_case_					,						expected_max_diff=snake_case_)



	@skip_mps
	def 		UpperCamelCase				(    self				:     int)->				int:
						__lowerCAmelCase						 =torch_device == """cpu"""
						__lowerCAmelCase						 =True
						__lowerCAmelCase						 =[
						    """decoder_num_inference_steps""",
						    """super_res_num_inference_steps""",
						]

						self._test_inference_batch_single_identical(
						    test_max_difference=snake_case_					,						relax_max_difference=snake_case_					,						additional_params_copy_to_batched_inputs=snake_case_					,						)



	def 		UpperCamelCase				(    self				:     List[Any])->				int:
						__lowerCAmelCase						 =[
						    """decoder_num_inference_steps""",
						    """super_res_num_inference_steps""",
						]

						if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											__lowerCAmelCase						 =[2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=snake_case_					,						additional_params_copy_to_batched_inputs=snake_case_					,						)
						else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=snake_case_)



	@skip_mps
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Tuple:
						return super().test_dict_tuple_outputs_equivalent()



	@skip_mps
	def 		UpperCamelCase				(    self				:     Optional[int])->				Tuple:
						return super().test_save_load_local()



	@skip_mps
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Any:
						return super().test_save_load_optional_components()






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



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



	def 		UpperCamelCase				(    self				:     Optional[Any])->				List[Any]:
						__lowerCAmelCase						 =load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png""")
						__lowerCAmelCase						 =load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/unclip/karlo_v1_alpha_cat_variation_fp16.npy""")

						__lowerCAmelCase						 =UnCLIPImageVariationPipeline.from_pretrained(
						    """kakaobrain/karlo-v1-alpha-image-variations"""					,						torch_dtype=torch.floataa)
						__lowerCAmelCase						 =pipeline.to(snake_case_)
						pipeline.set_progress_bar_config(disable=snake_case_)

						__lowerCAmelCase						 =torch.Generator(device="""cpu""").manual_seed(0)
						__lowerCAmelCase						 =pipeline(
						    snake_case_					,						generator=snake_case_					,						output_type="""np"""					,						)

						__lowerCAmelCase						 =output.images[0]

						assert image.shape == (2_56, 2_56, 3)

						assert_mean_pixel_difference(snake_case_					,						snake_case_					,						15)




