from scipy.stats import spearmanr

import datasets


lowerCAmelCase			:							Dict     ="\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"

lowerCAmelCase			:							Optional[Any]     ="\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"

lowerCAmelCase			:							List[str]     =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 	__snake_case	( datasets.Metric		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->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.spearmanr.html"""]    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Tuple=False)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						spearmanr(_UpperCamelCase    ,  _UpperCamelCase)
          if return_pvalue:
              return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
          else:
              return {"spearmanr": results[0]}





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 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       IFPipeline
      _snake_case    		=       TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
      _snake_case    		=       TEXT_TO_IMAGE_BATCH_PARAMS
      _snake_case    		=       PipelineTesterMixin.required_optional_params - {'latents'}



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Optional[Any]:


          """simple docstring"""


          return self._get_dummy_components()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[Any]=0)				->Optional[Any]:


          """simple docstring"""


          if str(_UpperCamelCase).startswith("""mps"""):
              _lowerCamelCase     :		int 						=						torch.manual_seed(_UpperCamelCase)
          else:
              _lowerCamelCase     :		List[Any] 						=						torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """output_type""": """numpy""",
          }

          return inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Union[str, Any]:


          """simple docstring"""


          self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != """cuda"""    ,  reason="""float16 requires CUDA""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Any:


          """simple docstring"""


          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Union[str, Any]:


          """simple docstring"""


          self._test_save_load_local()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Dict:


          """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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->int:


          """simple docstring"""


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)





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





      '''simple docstring'''






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


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


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

          _lowerCamelCase     :		Tuple 						=						IFSuperResolutionPipeline.from_pretrained(
              """DeepFloyd/IF-II-L-v1.0"""    ,  variant="""fp16"""    ,  torch_dtype=torch.floataa    ,  text_encoder=_UpperCamelCase    ,  tokenizer=_UpperCamelCase)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to("""cuda""")

          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						pipe_a.encode_prompt("""anime turtle"""    ,  device="""cuda""")

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

          _lowerCamelCase     :		str 						=						None
          _lowerCamelCase     :		str 						=						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(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          _lowerCamelCase     :		Optional[Any] 						=						IFImgaImgPipeline(**pipe_a.components)
          _lowerCamelCase     :		Optional[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(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          _lowerCamelCase     :		Any 						=						IFInpaintingPipeline(**pipe_a.components)
          _lowerCamelCase     :		Dict 						=						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(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				str)				->Tuple:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		Optional[int] 						=						torch.Generator(device="""cpu""").manual_seed(0)
          _lowerCamelCase     :		Optional[Any] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

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

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

          _lowerCamelCase     :		Dict 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          _lowerCamelCase     :		Dict 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		str 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Any 						=						output.images[0]

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

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

          _lowerCamelCase     :		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(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase     :		Dict 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Union[str, Any] 						=						output.images[0]

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

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

          _lowerCamelCase     :		List[Any] 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase     :		List[str] 						=						floats_tensor((1, 3, 256, 256)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  original_image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		List[Any] 						=						output.images[0]

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

          _lowerCamelCase     :		str 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          _lowerCamelCase     :		int 						=						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(_UpperCamelCase    ,  _UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Tuple)				->Optional[int]:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(1)).to(_UpperCamelCase)

          _lowerCamelCase     :		int 						=						torch.Generator(device="""cpu""").manual_seed(0)
          _lowerCamelCase     :		Any 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  mask_image=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Any 						=						output.images[0]

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

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

          _lowerCamelCase     :		str 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						torch.Generator(device="""cpu""").manual_seed(0)

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

          _lowerCamelCase     :		List[str] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  mask_image=_UpperCamelCase    ,  original_image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Optional[Any] 						=						output.images[0]

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

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

          _lowerCamelCase     :		int 						=						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(_UpperCamelCase    ,  _UpperCamelCase)







def 						A__  (					):







    '''simple docstring'''
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()




