



"""simple docstring"""





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	'''ClapFeatureExtractor'''
       UpperCamelCase		=	('''RobertaTokenizer''', '''RobertaTokenizerFast''')



       def __init__(				self     :    str						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    int       )					->		str:


             '''simple docstring'''




             super().__init__(__lowercase						,							__lowercase       )



       def __call__(				self     :    Optional[int]						,							_UpperCAmelCase     :    int=None						,							_UpperCAmelCase     :    List[str]=None						,							_UpperCAmelCase     :    str=None						,							**_UpperCAmelCase     :    List[Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				kwargs.pop("sampling_rate"						,							__lowercase       )

             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:
                   UpperCAmelCase_     					=				self.tokenizer(__lowercase						,							return_tensors=__lowercase						,							**__lowercase       )

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

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



       def 	lowercase__							(				self     :    int						,							*_UpperCAmelCase     :    List[Any]						,							**_UpperCAmelCase     :    Tuple       )					->		Optional[Any]:


             '''simple docstring'''




             return self.tokenizer.batch_decode(*__lowercase						,							**__lowercase       )



       def 	lowercase__							(				self     :    str						,							*_UpperCAmelCase     :    Optional[Any]						,							**_UpperCAmelCase     :    str       )					->		Optional[int]:


             '''simple docstring'''




             return self.tokenizer.decode(*__lowercase						,							**__lowercase       )







       @property
       def 	lowercase__							(				self     :    Any       )					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				self.tokenizer.model_input_names
             UpperCAmelCase_     					=				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 PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 	lowerCAmelCase  (   unittest.TestCase ):







       @property
       def       snake_case (				self      :			str   ):







            """simple docstring"""







            torch.manual_seed(0   )
            __lowercase      		=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       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.dummy_uncond_unet
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'   ).images

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   num_inference_steps=20    ,   output_type='numpy'    ,   return_dict=__lowercase   )[0]

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

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([1.0, 1.0, 0.0, 1.0, 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 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		='google/ddpm-cifar10-32'

            __lowercase      		=UNetaDModel.from_pretrained(__lowercase   )
            __lowercase      		=PNDMScheduler()

            __lowercase      		=PNDMPipeline(unet=__lowercase    ,   scheduler=__lowercase   )
            pndm.to(__lowercase   )
            pndm.set_progress_bar_config(disable=__lowercase   )
            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pndm(generator=__lowercase    ,   output_type='numpy'   ).images

            __lowercase      		=image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __lowercase      		=np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]   )

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


