


'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      @property
      def       UpperCamelCase__		(self    :    Any ):

             '''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       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= self.dummy_uncond_unet
             lowercase__      			= ScoreSdeVeScheduler()

             lowercase__      			= ScoreSdeVePipeline(unet=UpperCamelCase   ,						scheduler=UpperCamelCase )
             sde_ve.to(UpperCamelCase )
             sde_ve.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.manual_seed(0 )
             lowercase__      			= sde_ve(num_inference_steps=2   ,						output_type='''numpy'''   ,						generator=UpperCamelCase ).images

             lowercase__      			= torch.manual_seed(0 )
             lowercase__      			= sde_ve(num_inference_steps=2   ,						output_type='''numpy'''   ,						generator=UpperCamelCase   ,						return_dict=UpperCamelCase )[
                 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([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   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             lowercase__      			= '''google/ncsnpp-church-256'''
             lowercase__      			= UNetaDModel.from_pretrained(UpperCamelCase )

             lowercase__      			= ScoreSdeVeScheduler.from_pretrained(UpperCamelCase )

             lowercase__      			= ScoreSdeVePipeline(unet=UpperCamelCase   ,						scheduler=UpperCamelCase )
             sde_ve.to(UpperCamelCase )
             sde_ve.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.manual_seed(0 )
             lowercase__      			= sde_ve(num_inference_steps=10   ,						output_type='''numpy'''   ,						generator=UpperCamelCase ).images

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

             assert image.shape == (1, 256, 256, 3)

             lowercase__      			= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )

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







import warnings

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Optional[int]	    =    "CLIPImageProcessor"
							UpperCAmelCase__       :						Tuple	    =    ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    int         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    int         =		kwargs.pop('''feature_extractor'''       )

												_a	:    int         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    List[Any]:

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

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

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    List[Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Optional[int]:
												return self.tokenizer.decode(*_a       ,							**_a       )




							@property
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



