




"""simple docstring"""







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'ClapFeatureExtractor'
						_a    						=						('RobertaTokenizer', 'RobertaTokenizerFast')


						def __init__(							self				:			List[str]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			int					):
										super().__init__(lowerCAmelCase						,						lowerCAmelCase					)


						def __call__(							self				:			str						,						lowerCAmelCase				:			int=None						,						lowerCAmelCase				:			str=None						,						lowerCAmelCase				:			Tuple=None						,						**lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				kwargs.pop("""sampling_rate"""						,						lowerCAmelCase					)

										if text is None and audios is None:
														raise ValueError("""You have to specify either text or audios. Both cannot be none."""					)

										if text is not None:
														lowerCAmelCase  =				self.tokenizer(lowerCAmelCase						,						return_tensors=lowerCAmelCase						,						**lowerCAmelCase					)

										if audios is not None:
														lowerCAmelCase  =				self.feature_extractor(
														    lowerCAmelCase						,						sampling_rate=lowerCAmelCase						,						return_tensors=lowerCAmelCase						,						**lowerCAmelCase					)

										if text is not None and audios is not None:
														lowerCAmelCase  =				audio_features.input_features
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**lowerCAmelCase					)						,						tensor_type=lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]						,						*lowerCAmelCase				:			Union[str, Any]						,						**lowerCAmelCase				:			Optional[Any]					):
										return self.tokenizer.batch_decode(*lowerCAmelCase						,						**lowerCAmelCase					)


						def   __lowercase (							self				:			Tuple						,						*lowerCAmelCase				:			str						,						**lowerCAmelCase				:			str					):
										return self.tokenizer.decode(*lowerCAmelCase						,						**lowerCAmelCase					)




						@property
						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.tokenizer.model_input_names
										lowerCAmelCase  =				self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names					)					)











"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=3						,						out_channels=3						,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,						)
										return model




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""						,						return_dict=lowerCAmelCase					)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

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






@slow
@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

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





