





'''simple docstring'''






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()







class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    AltDiffusionPipeline
					_SCREAMING_SNAKE_CASE							   =    TEXT_TO_IMAGE_PARAMS
					_SCREAMING_SNAKE_CASE							   =    TEXT_TO_IMAGE_BATCH_PARAMS
					_SCREAMING_SNAKE_CASE							   =    TEXT_TO_IMAGE_IMAGE_PARAMS
					_SCREAMING_SNAKE_CASE							   =    TEXT_TO_IMAGE_IMAGE_PARAMS


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											torch.manual_seed(0       )
											_snake_case:   Optional[int]  					=		UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,     cross_attention_dim=32       ,     )
											_snake_case:   Union[str, Any]  					=		DDIMScheduler(
											    beta_start=0.00_085       ,     beta_end=0.012       ,     beta_schedule='scaled_linear'       ,     clip_sample=__snake_case       ,     set_alpha_to_one=__snake_case       ,     )
											torch.manual_seed(0       )
											_snake_case:   List[Any]  					=		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       ,     )

											# TODO: address the non-deterministic text encoder (fails for save-load tests)
											# torch.manual_seed(0)
											# text_encoder_config = RobertaSeriesConfig(
											#     hidden_size=32,
											#     project_dim=32,
											#     intermediate_size=37,
											#     layer_norm_eps=1e-05,
											#     num_attention_heads=4,
											#     num_hidden_layers=5,
											#     vocab_size=5002,
											# )
											# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

											torch.manual_seed(0       )
											_snake_case:   Tuple  					=		CLIPTextConfig(
											    bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     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=50_02       ,     )
											_snake_case:   int  					=		CLIPTextModel(__snake_case       )

											_snake_case:   str  					=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
											_snake_case:   str  					=		77

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Dict=0       ):





											'''simple docstring'''





											if str(__snake_case       ).startswith('mps'       ):
																	_snake_case:   Dict  					=		torch.manual_seed(__snake_case       )
											else:
																	_snake_case:   Dict  					=		torch.Generator(device=__snake_case       ).manual_seed(__snake_case       )
											_snake_case:   List[Any]  					=		{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											super().test_inference_batch_single_identical(expected_max_diff=3e-3       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		'cpu'  # ensure determinism for the device-dependent torch.Generator

											_snake_case:   Optional[Any]  					=		self.get_dummy_components()
											torch.manual_seed(0       )
											_snake_case:   Optional[int]  					=		RobertaSeriesConfig(
											    hidden_size=32       ,     project_dim=32       ,     intermediate_size=37       ,     layer_norm_eps=1e-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     vocab_size=50_02       ,     )
											# TODO: remove after fixing the non-deterministic text encoder
											_snake_case:   List[str]  					=		RobertaSeriesModelWithTransformation(__snake_case       )
											_snake_case:   Optional[Any]  					=		text_encoder

											_snake_case:   Union[str, Any]  					=		AltDiffusionPipeline(**__snake_case       )
											_snake_case:   Optional[int]  					=		alt_pipe.to(__snake_case       )
											alt_pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   Tuple  					=		self.get_dummy_inputs(__snake_case       )
											_snake_case:   Optional[Any]  					=		'A photo of an astronaut'
											_snake_case:   List[Any]  					=		alt_pipe(**__snake_case       )
											_snake_case:   Tuple  					=		output.images
											_snake_case:   List[str]  					=		image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											_snake_case:   int  					=		np.array(
											    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]       )

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




					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		'cpu'  # ensure determinism for the device-dependent torch.Generator

											_snake_case:   Dict  					=		self.get_dummy_components()
											_snake_case:   Dict  					=		PNDMScheduler(skip_prk_steps=__snake_case       )
											torch.manual_seed(0       )
											_snake_case:   str  					=		RobertaSeriesConfig(
											    hidden_size=32       ,     project_dim=32       ,     intermediate_size=37       ,     layer_norm_eps=1e-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     vocab_size=50_02       ,     )
											# TODO: remove after fixing the non-deterministic text encoder
											_snake_case:   Tuple  					=		RobertaSeriesModelWithTransformation(__snake_case       )
											_snake_case:   str  					=		text_encoder
											_snake_case:   str  					=		AltDiffusionPipeline(**__snake_case       )
											_snake_case:   List[str]  					=		alt_pipe.to(__snake_case       )
											alt_pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   Optional[Any]  					=		self.get_dummy_inputs(__snake_case       )
											_snake_case:   Tuple  					=		alt_pipe(**__snake_case       )
											_snake_case:   str  					=		output.images
											_snake_case:   int  					=		image[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											_snake_case:   Optional[Any]  					=		np.array(
											    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]       )

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







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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'       ,     safety_checker=__snake_case       )
											_snake_case:   Union[str, Any]  					=		alt_pipe.to(__snake_case       )
											alt_pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   List[Any]  					=		'A painting of a squirrel eating a burger'
											_snake_case:   Dict  					=		torch.manual_seed(0       )
											_snake_case:   Optional[Any]  					=		alt_pipe([prompt]       ,     generator=__snake_case       ,     guidance_scale=6.0       ,     num_inference_steps=20       ,     output_type='np'       )

											_snake_case:   str  					=		output.images

											_snake_case:   Dict  					=		image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											_snake_case:   Any  					=		np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]       )

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




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		DDIMScheduler.from_pretrained('BAAI/AltDiffusion'       ,     subfolder='scheduler'       )

											_snake_case:   Tuple  					=		AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'       ,     scheduler=__snake_case       ,     safety_checker=__snake_case       )
											_snake_case:   int  					=		alt_pipe.to(__snake_case       )
											alt_pipe.set_progress_bar_config(disable=__snake_case       )

											_snake_case:   Dict  					=		'A painting of a squirrel eating a burger'
											_snake_case:   str  					=		torch.manual_seed(0       )

											_snake_case:   int  					=		alt_pipe([prompt]       ,     generator=__snake_case       ,     num_inference_steps=2       ,     output_type='numpy'       )
											_snake_case:   str  					=		output.images

											_snake_case:   Any  					=		image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											_snake_case:   Any  					=		np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]       )

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












'''simple docstring'''






from sklearn.metrics import matthews_corrcoef

import datasets


A   : Dict             =			'\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'

A   : int             =			'\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric("matthews_correlation")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n'

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







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


					def    SCREAMING_SNAKE_CASE_		(					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    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  int       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]=None       ):





											'''simple docstring'''





											return {
											    "matthews_correlation": float(matthews_corrcoef(__snake_case       ,     __snake_case       ,     sample_weight=__snake_case       )       ),
											}





