
'''simple docstring'''




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class  A				(							__snake_case    ,			__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		IFPipeline
     __magic_name__						=		TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     __magic_name__						=		TEXT_TO_IMAGE_BATCH_PARAMS
     __magic_name__						=		PipelineTesterMixin.required_optional_params - {'''latents'''}





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         return self._get_dummy_components()





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=0	) ->				Optional[int]:






         """simple docstring"""






         if str(SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
             A       :   str					=						torch.manual_seed(SCREAMING_SNAKE_CASE	)
         else:
             A       :   List[Any]					=						torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)

         A       :   Any					=						{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != '''cuda'''						,			reason='''float16 requires CUDA'''	)
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         super().test_save_load_floataa(expected_max_diff=1e-1	)





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         self._test_save_load_local()





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2						,			)





     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()						,			reason='''XFormers attention is only available with CUDA and `xformers` installed'''						,			)
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)





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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Optional[int]					=						IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''						,			variant='''fp16'''						,			torch_dtype=torch.floataa	)

         A       :   str					=						IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0'''						,			variant='''fp16'''						,			torch_dtype=torch.floataa						,			text_encoder=SCREAMING_SNAKE_CASE						,			tokenizer=SCREAMING_SNAKE_CASE	)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda'''	)

         A,  A       :   Optional[Any]					=						pipe_a.encode_prompt('''anime turtle'''						,			device='''cuda'''	)

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         A       :   List[Any]					=						None
         A       :   List[Any]					=						None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)

         self._test_if(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         A       :   Optional[Any]					=						IFImgaImgPipeline(**pipe_a.components	)
         A       :   Union[str, Any]					=						IFImgaImgSuperResolutionPipeline(**pipe_a.components	)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)

         self._test_if_imgaimg(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         A       :   str					=						IFInpaintingPipeline(**pipe_a.components	)
         A       :   Any					=						IFInpaintingSuperResolutionPipeline(**pipe_a.components	)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)

         self._test_if_inpainting(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         _start_torch_memory_measurement()

         A       :   Any					=						torch.Generator(device='''cpu'''	).manual_seed(0	)
         A       :   Dict					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			generator=SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)

         A       :   Any					=						output.images[0]

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

         A       :   Union[str, Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         A       :   Union[str, Any]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # pipeline 2

         _start_torch_memory_measurement()

         A       :   str					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Tuple					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''np'''						,			)

         A       :   List[str]					=						output.images[0]

         assert image.shape == (256, 256, 3)

         A       :   Tuple					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         A       :   int					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         _start_torch_memory_measurement()

         A       :   Optional[Any]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Any					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			generator=SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)

         A       :   List[str]					=						output.images[0]

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

         A       :   Any					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         A       :   Union[str, Any]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # pipeline 2

         _start_torch_memory_measurement()

         A       :   Any					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Union[str, Any]					=						floats_tensor((1, 3, 256, 256)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   str					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			original_image=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''np'''						,			)

         A       :   Any					=						output.images[0]

         assert image.shape == (256, 256, 3)

         A       :   Union[str, Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         A       :   List[str]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         _start_torch_memory_measurement()

         A       :   List[str]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(1	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						torch.Generator(device='''cpu'''	).manual_seed(0	)
         A       :   List[Any]					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			mask_image=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			generator=SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)

         A       :   Any					=						output.images[0]

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

         A       :   Optional[Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         A       :   int					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # pipeline 2

         _start_torch_memory_measurement()

         A       :   int					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Union[str, Any]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Any					=						floats_tensor((1, 3, 256, 256)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						floats_tensor((1, 3, 256, 256)						,			rng=random.Random(1	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			mask_image=SCREAMING_SNAKE_CASE						,			original_image=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''np'''						,			)

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

         assert image.shape == (256, 256, 3)

         A       :   List[Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         A       :   Any					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()






'''simple docstring'''




from scipy.stats import pearsonr

import datasets


lowercase			:				Optional[int]    		=			'\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'


lowercase			:				Optional[Any]    		=			'\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'


lowercase			:				str    		=			'\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						):





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False	) ->				Union[str, Any]:






         """simple docstring"""






         if return_pvalue:
             A       :   Union[str, Any]					=						pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
             return {"pearsonr": results[0], "p-value": results[1]}
         else:
             return {"pearsonr": float(pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)[0]	)}




