



import warnings

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



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			['''image_processor''', '''tokenizer''']
							_a      				=			'''AutoImageProcessor'''
							_a      				=			'''AutoTokenizer'''




							def __init__(	self					:	Tuple    ,    A_					:	Tuple=None    ,    A_					:	Dict=None    ,    **A_					:	Optional[int]):
									lowerCAmelCase_      :					Any     =			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_    ,    )
											lowerCAmelCase_      :					Any     =			kwargs.pop('''feature_extractor''')

									lowerCAmelCase_      :					List[Any]     =			image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError('''You have to specify an image_processor.''')
									if tokenizer is None:
											raise ValueError('''You have to specify a tokenizer.''')

									super().__init__(A_    ,    A_)
									lowerCAmelCase_      :					Any     =			self.image_processor




							def __call__(	self					:	str    ,    A_					:	List[str]=None    ,    A_					:	Optional[Any]=None    ,    A_					:	Dict=None    ,    **A_					:	str):

									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:
											lowerCAmelCase_      :					int     =			self.tokenizer(A_    ,    return_tensors=A_    ,    **A_)

									if images is not None:
											lowerCAmelCase_      :					List[str]     =			self.image_processor(A_    ,    return_tensors=A_    ,    **A_)

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




							def        UpperCAmelCase__      (	self					:	Optional[Any]    ,    *A_					:	Optional[Any]    ,    **A_					:	Tuple):
									return self.tokenizer.batch_decode(*A_    ,    **A_)




							def        UpperCAmelCase__      (	self					:	Union[str, Any]    ,    *A_					:	Union[str, Any]    ,    **A_					:	Dict):
									return self.tokenizer.decode(*A_    ,    **A_)




							@property
							def        UpperCAmelCase__      (	self					:	Any):
									lowerCAmelCase_      :					str     =			self.tokenizer.model_input_names
									lowerCAmelCase_      :					Union[str, Any]     =			self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




							@property
							def        UpperCAmelCase__      (	self					:	Any):
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,    A_    ,    )
									return self.image_processor_class







							@property
							def        UpperCAmelCase__      (	self					:	Dict):
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,    A_    ,    )
									return self.image_processor










import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class     __snake_case						(					UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a      				=			IFImgaImgPipeline
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
							_a      				=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_a      				=			PipelineTesterMixin.required_optional_params - {'''latents'''}




							def        UpperCAmelCase__      (	self					:	Any):
									return self._get_dummy_components()




							def        UpperCAmelCase__      (	self					:	str    ,    A_					:	Optional[int]    ,    A_					:	List[Any]=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_      :					Dict     =			torch.manual_seed(A_)
									else:
											lowerCAmelCase_      :					List[Any]     =			torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_      :					List[Any]     =			floats_tensor((1, 3, 3_2, 3_2)    ,    rng=random.Random(A_)).to(A_)

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

									return inputs




							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_save_load_optional_components()




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




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Dict):
									# 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)




							@unittest.skipIf(torch_device != '''cuda'''    ,    reason='''float16 requires CUDA''')
							def        UpperCAmelCase__      (	self					:	Tuple):
									super().test_floataa_inference(expected_max_diff=1e-1)




							def        UpperCAmelCase__      (	self					:	str):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def        UpperCAmelCase__      (	self					:	int):
									self._test_save_load_local()







							def        UpperCAmelCase__      (	self					:	Union[str, Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2    ,    )





