




from ...processing_utils import ProcessorMixin


class 							__UpperCamelCase			( _A  ):





				'''simple docstring'''



				__a							: Optional[int]										="""WhisperFeatureExtractor"""
				__a							: Dict										="""WhisperTokenizer"""



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											super().__init__(UpperCamelCase__       , UpperCamelCase__						)
											lowerCAmelCase   =			self.feature_extractor
											lowerCAmelCase   =			False



				def       __snake_case		( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , UpperCAmelCase_=True						):
											return self.tokenizer.get_decoder_prompt_ids(task=UpperCamelCase__       , language=UpperCamelCase__       , no_timestamps=UpperCamelCase__						)



				def __call__( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):






											if self._in_target_context_manager:
																		return self.current_processor(*UpperCamelCase__       , **UpperCamelCase__						)

											lowerCAmelCase   =			kwargs.pop('''audio'''       , UpperCamelCase__						)
											lowerCAmelCase   =			kwargs.pop('''sampling_rate'''       , UpperCamelCase__						)
											lowerCAmelCase   =			kwargs.pop('''text'''       , UpperCamelCase__						)
											if len(UpperCamelCase__						) > 0:
																		lowerCAmelCase   =			args[0]
																		lowerCAmelCase   =			args[1:]

											if audio is None and text is None:
																		raise ValueError('''You need to specify either an `audio` or `text` input to process.'''						)

											if audio is not None:
																		lowerCAmelCase   =			self.feature_extractor(UpperCamelCase__       , *UpperCamelCase__       , sampling_rate=UpperCamelCase__       , **UpperCamelCase__						)
											if text is not None:
																		lowerCAmelCase   =			self.tokenizer(UpperCamelCase__       , **UpperCamelCase__						)

											if text is None:
																		return inputs

											elif audio is None:
																		return encodings
											else:
																		lowerCAmelCase   =			encodings['''input_ids''']
																		return inputs



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.batch_decode(*UpperCamelCase__       , **UpperCamelCase__						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.decode(*UpperCamelCase__       , **UpperCamelCase__						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_="np"						):
											return self.tokenizer.get_prompt_ids(UpperCamelCase__       , return_tensors=UpperCamelCase__						)









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Tuple										=IFInpaintingSuperResolutionPipeline
				__a							: Dict										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__a							: int										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}  )
				__a							: Union[str, Any]										=PipelineTesterMixin.required_optional_params - {"""latents"""}



				def       __snake_case		( self						):
											return self._get_superresolution_dummy_components()



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
											if str(UpperCAmelCase_						).startswith('''mps'''						):
																		lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											else:
																		lowerCAmelCase   =		torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)

											lowerCAmelCase   =		floats_tensor((1, 3, 16, 16)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

											lowerCAmelCase   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': image,
											    '''original_image''': original_image,
											    '''mask_image''': mask_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''output_type''': '''numpy''',
											}

											return inputs



				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available()       , reason='''XFormers attention is only available with CUDA and `xformers` installed'''       , )
				def       __snake_case		( self						):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



				def       __snake_case		( self						):
											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != '''cuda'''       , reason='''float16 requires CUDA'''						)
				def       __snake_case		( self						):
											# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
											super().test_save_load_floataa(expected_max_diff=1E-1						)



				def       __snake_case		( self						):
											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



				def       __snake_case		( self						):
											self._test_save_load_local()






				def       __snake_case		( self						):
											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2       , )



