
"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class     _UpperCAmelCase(    lowerCamelCase      ,     lowerCamelCase      ,     unittest.TestCase	):
		lowercase__						=      StableDiffusionPanoramaPipeline
		lowercase__						=      TEXT_TO_IMAGE_PARAMS
		lowercase__						=      TEXT_TO_IMAGE_BATCH_PARAMS
		lowercase__						=      TEXT_TO_IMAGE_IMAGE_PARAMS
		lowercase__						=      TEXT_TO_IMAGE_IMAGE_PARAMS



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							torch.manual_seed(0)
							_UpperCamelCase							 =  UNetaDConditionModel(
							    block_out_channels=(32, 64)				,		layers_per_block=1				,		sample_size=32				,		in_channels=4				,		out_channels=4				,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,		cross_attention_dim=32				,		)
							_UpperCamelCase							 =  DDIMScheduler()
							torch.manual_seed(0)
							_UpperCamelCase							 =  AutoencoderKL(
							    block_out_channels=[32, 64]				,		in_channels=3				,		out_channels=3				,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,		latent_channels=4				,		)
							torch.manual_seed(0)
							_UpperCamelCase							 =  CLIPTextConfig(
							    bos_token_id=0				,		eos_token_id=2				,		hidden_size=32				,		intermediate_size=37				,		layer_norm_eps=1e-05				,		num_attention_heads=4				,		num_hidden_layers=5				,		pad_token_id=1				,		vocab_size=10_00				,		)
							_UpperCamelCase							 =  CLIPTextModel(__a)
							_UpperCamelCase							 =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							_UpperCamelCase							 =  {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components



		def 						UpperCAmelCase							(					self				,		__a				,		__a=0)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  torch.manual_seed(__a)
							_UpperCamelCase							 =  {
							    '''prompt''': '''a photo of the dolomites''',
							    '''generator''': generator,
							    # Setting height and width to None to prevent OOMs on CPU.
							    '''height''': None,
							    '''width''': None,
							    '''num_inference_steps''': 1,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline(**__a)
							_UpperCamelCase							 =  sd_pipe.to(__a)
							sd_pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase							 =  self.get_dummy_inputs(__a)
							_UpperCamelCase							 =  sd_pipe(**__a).images
							_UpperCamelCase							 =  image[0, -3:, -3:, -1]
							assert image.shape == (1, 64, 64, 3)

							_UpperCamelCase							 =  np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])

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



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






							'''simple docstring'''
							super().test_inference_batch_consistent(batch_sizes=[1, 2])



		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							super().test_inference_batch_single_identical(batch_size=2				,		expected_max_diff=3.25e-3)



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






							'''simple docstring'''
							_UpperCamelCase							 =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline(**__a)
							_UpperCamelCase							 =  sd_pipe.to(__a)
							sd_pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase							 =  self.get_dummy_inputs(__a)
							_UpperCamelCase							 =  '''french fries'''
							_UpperCamelCase							 =  sd_pipe(**__a				,		negative_prompt=__a)
							_UpperCamelCase							 =  output.images
							_UpperCamelCase							 =  image[0, -3:, -3:, -1]

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

							_UpperCamelCase							 =  np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])

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



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline(**__a)
							_UpperCamelCase							 =  sd_pipe.to(__a)
							sd_pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase							 =  self.get_dummy_inputs(__a)
							_UpperCamelCase							 =  sd_pipe(**__a				,		view_batch_size=2)
							_UpperCamelCase							 =  output.images
							_UpperCamelCase							 =  image[0, -3:, -3:, -1]

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

							_UpperCamelCase							 =  np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])

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



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0085				,		beta_end=0.012				,		beta_schedule='''scaled_linear''')
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline(**__a)
							_UpperCamelCase							 =  sd_pipe.to(__a)
							sd_pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase							 =  self.get_dummy_inputs(__a)
							_UpperCamelCase							 =  sd_pipe(**__a).images
							_UpperCamelCase							 =  image[0, -3:, -3:, -1]

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

							_UpperCamelCase							 =  np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952])

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




		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  PNDMScheduler(
							    beta_start=0.0_0085				,		beta_end=0.012				,		beta_schedule='''scaled_linear'''				,		skip_prk_steps=__a)
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline(**__a)
							_UpperCamelCase							 =  sd_pipe.to(__a)
							sd_pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase							 =  self.get_dummy_inputs(__a)
							_UpperCamelCase							 =  sd_pipe(**__a).images
							_UpperCamelCase							 =  image[0, -3:, -3:, -1]

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

							_UpperCamelCase							 =  np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539])

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






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



		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 						UpperCAmelCase							(					self				,		__a=0)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  torch.manual_seed(__a)
							_UpperCamelCase							 =  {
							    '''prompt''': '''a photo of the dolomites''',
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  '''stabilityai/stable-diffusion-2-base'''
							_UpperCamelCase							 =  DDIMScheduler.from_pretrained(__a				,		subfolder='''scheduler''')
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline.from_pretrained(__a				,		scheduler=__a				,		safety_checker=__a)
							pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing()

							_UpperCamelCase							 =  self.get_inputs()
							_UpperCamelCase							 =  pipe(**__a).images
							_UpperCamelCase							 =  image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 5_12, 20_48, 3)

							_UpperCamelCase							 =  np.array(
							    [
							        0.3696_8392,
							        0.2702_5372,
							        0.3244_6766,
							        0.2837_9387,
							        0.3636_3274,
							        0.3073_3347,
							        0.2710_0027,
							        0.2705_4125,
							        0.2553_6096,
							    ])

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



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline.from_pretrained(
							    '''stabilityai/stable-diffusion-2-base'''				,		safety_checker=__a)
							_UpperCamelCase							 =  LMSDiscreteScheduler.from_config(pipe.scheduler.config)
							pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing()

							_UpperCamelCase							 =  self.get_inputs()
							_UpperCamelCase							 =  pipe(**__a).images
							_UpperCamelCase							 =  image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 5_12, 20_48, 3)

							_UpperCamelCase							 =  np.array(
							    [
							        [
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							            0.0,
							        ]
							    ])

							assert np.abs(expected_slice - image_slice).max() < 1e-3



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  0

							def callback_fn(__a				,		__a				,		__a) -> None:
												_UpperCamelCase							 =  True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	_UpperCamelCase							 =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 2_56)
																	_UpperCamelCase							 =  latents[0, -3:, -3:, -1]

																	_UpperCamelCase							 =  np.array(
																	    [
																	        0.1868_1869,
																	        0.3390_7816,
																	        0.536_1276,
																	        0.1443_2865,
																	        -0.0285_6611,
																	        -0.7394_1123,
																	        0.2339_7987,
																	        0.4732_2682,
																	        -0.3782_3164,
																	    ])
																	assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
												elif step == 2:
																	_UpperCamelCase							 =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 2_56)
																	_UpperCamelCase							 =  latents[0, -3:, -3:, -1]

																	_UpperCamelCase							 =  np.array(
																	    [
																	        0.1853_9645,
																	        0.3398_7248,
																	        0.537_8559,
																	        0.1443_7142,
																	        -0.0245_5261,
																	        -0.733_8317,
																	        0.2399_0755,
																	        0.4735_6272,
																	        -0.378_6505,
																	    ])

																	assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2

							_UpperCamelCase							 =  False

							_UpperCamelCase							 =  '''stabilityai/stable-diffusion-2-base'''
							_UpperCamelCase							 =  DDIMScheduler.from_pretrained(__a				,		subfolder='''scheduler''')
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline.from_pretrained(__a				,		scheduler=__a				,		safety_checker=__a)
							_UpperCamelCase							 =  pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing()

							_UpperCamelCase							 =  self.get_inputs()
							pipe(**__a				,		callback=__a				,		callback_steps=1)
							assert callback_fn.has_been_called
							assert number_of_steps == 3




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






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

							_UpperCamelCase							 =  '''stabilityai/stable-diffusion-2-base'''
							_UpperCamelCase							 =  DDIMScheduler.from_pretrained(__a				,		subfolder='''scheduler''')
							_UpperCamelCase							 =  StableDiffusionPanoramaPipeline.from_pretrained(__a				,		scheduler=__a				,		safety_checker=__a)
							_UpperCamelCase							 =  pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing(1)
							pipe.enable_sequential_cpu_offload()

							_UpperCamelCase							 =  self.get_inputs()
							_UpperCamelCase							 =  pipe(**__a)

							_UpperCamelCase							 =  torch.cuda.max_memory_allocated()
							# make sure that less than 5.2 GB is allocated
							assert mem_bytes < 5.5 * 10**9








"""simple docstring"""





from sklearn.metrics import matthews_corrcoef

import datasets


_a														=		"""
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

_a														=		"""
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

_a														=		"""\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""






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



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int32'''),
							            '''references''': datasets.Value('''int32'''),
							        })				,		reference_urls=[
							        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
							    ]				,		)




		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a=None)       ->       Dict:






							'''simple docstring'''
							return {
							    "matthews_correlation": float(matthews_corrcoef(__a				,		__a				,		sample_weight=__a)),
							}






