
"""simple docstring"""





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class     _UpperCAmelCase(    lowerCamelCase      ,     lowerCamelCase      ,     lowerCamelCase      ,     unittest.TestCase	):
		lowercase__						=      StableUnCLIPPipeline
		lowercase__						=      TEXT_TO_IMAGE_PARAMS
		lowercase__						=      TEXT_TO_IMAGE_BATCH_PARAMS
		lowercase__						=      TEXT_TO_IMAGE_IMAGE_PARAMS
		lowercase__						=      TEXT_TO_IMAGE_IMAGE_PARAMS

		# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
		lowercase__						=      False



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






							'''simple docstring'''
							_UpperCamelCase							 =  32
							_UpperCamelCase							 =  embedder_hidden_size

							# prior components

							torch.manual_seed(0)
							_UpperCamelCase							 =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							torch.manual_seed(0)
							_UpperCamelCase							 =  CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0				,		eos_token_id=2				,		hidden_size=__a				,		projection_dim=__a				,		intermediate_size=37				,		layer_norm_eps=1e-05				,		num_attention_heads=4				,		num_hidden_layers=5				,		pad_token_id=1				,		vocab_size=10_00				,		))

							torch.manual_seed(0)
							_UpperCamelCase							 =  PriorTransformer(
							    num_attention_heads=2				,		attention_head_dim=12				,		embedding_dim=__a				,		num_layers=1				,		)

							torch.manual_seed(0)
							_UpperCamelCase							 =  DDPMScheduler(
							    variance_type='''fixed_small_log'''				,		prediction_type='''sample'''				,		num_train_timesteps=10_00				,		clip_sample=__a				,		clip_sample_range=5.0				,		beta_schedule='''squaredcos_cap_v2'''				,		)

							# regular denoising components

							torch.manual_seed(0)
							_UpperCamelCase							 =  StableUnCLIPImageNormalizer(embedding_dim=__a)
							_UpperCamelCase							 =  DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

							torch.manual_seed(0)
							_UpperCamelCase							 =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							torch.manual_seed(0)
							_UpperCamelCase							 =  CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0				,		eos_token_id=2				,		hidden_size=__a				,		projection_dim=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				,		))

							torch.manual_seed(0)
							_UpperCamelCase							 =  UNetaDConditionModel(
							    sample_size=32				,		in_channels=4				,		out_channels=4				,		down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')				,		up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')				,		block_out_channels=(32, 64)				,		attention_head_dim=(2, 4)				,		class_embed_type='''projection'''				,		projection_class_embeddings_input_dim=embedder_projection_dim * 2				,		cross_attention_dim=__a				,		layers_per_block=1				,		upcast_attention=__a				,		use_linear_projection=__a				,		)

							torch.manual_seed(0)
							_UpperCamelCase							 =  DDIMScheduler(
							    beta_schedule='''scaled_linear'''				,		beta_start=0.0_0085				,		beta_end=0.012				,		prediction_type='''v_prediction'''				,		set_alpha_to_one=__a				,		steps_offset=1				,		)

							torch.manual_seed(0)
							_UpperCamelCase							 =  AutoencoderKL()

							_UpperCamelCase							 =  {
							    # prior components
							    '''prior_tokenizer''': prior_tokenizer,
							    '''prior_text_encoder''': prior_text_encoder,
							    '''prior''': prior,
							    '''prior_scheduler''': prior_scheduler,
							    # image noising components
							    '''image_normalizer''': image_normalizer,
							    '''image_noising_scheduler''': image_noising_scheduler,
							    # regular denoising components
							    '''tokenizer''': tokenizer,
							    '''text_encoder''': text_encoder,
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							}

							return components



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






							'''simple docstring'''
							if str(__a).startswith('''mps'''):
												_UpperCamelCase							 =  torch.manual_seed(__a)
							else:
												_UpperCamelCase							 =  torch.Generator(device=__a).manual_seed(__a)
							_UpperCamelCase							 =  {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''prior_num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  torch_device == '''cpu'''

							self._test_attention_slicing_forward_pass(test_max_difference=__a)




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






							'''simple docstring'''
							_UpperCamelCase							 =  torch_device in ['''cpu''', '''mps''']

							self._test_inference_batch_single_identical(test_max_difference=__a)






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



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






							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



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






							'''simple docstring'''
							_UpperCamelCase							 =  load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy''')

							_UpperCamelCase							 =  StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''				,		torch_dtype=torch.floataa)
							pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							_UpperCamelCase							 =  torch.Generator(device='''cpu''').manual_seed(0)
							_UpperCamelCase							 =  pipe('''anime turle'''				,		generator=__a				,		output_type='''np''')

							_UpperCamelCase							 =  output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(__a				,		__a)




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






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

							_UpperCamelCase							 =  StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''				,		torch_dtype=torch.floataa)
							_UpperCamelCase							 =  pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							_UpperCamelCase							 =  pipe(
							    '''anime turtle'''				,		prior_num_inference_steps=2				,		num_inference_steps=2				,		output_type='''np'''				,		)

							_UpperCamelCase							 =  torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 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)),
							}






