

import warnings

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







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	Tuple    =   ['image_processor', 'tokenizer']
   lowerCamelCase__ :	str    =   'CLIPImageProcessor'
   lowerCamelCase__ :	str    =   ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')



   def __init__(				self,    lowercase_=None,    lowercase_=None,    **lowercase_       )							->    int:






     """simple docstring"""






     a__  =None
     if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.''',    lowercase_,    )
       a__  =kwargs.pop('''feature_extractor'''       )

     a__  =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__(lowercase_,    lowercase_       )



   def __call__(				self,    lowercase_=None,    lowercase_=None,    lowercase_=None,    **lowercase_       )							->    int:






     """simple docstring"""






     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__  =self.tokenizer(lowercase_,    return_tensors=lowercase_,    **lowercase_       )

     if images is not None:
       a__  =self.image_processor(lowercase_,    return_tensors=lowercase_,    **lowercase_       )

     if text is not None and images is not None:
       a__  =image_features.pixel_values
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**lowercase_       ),    tensor_type=lowercase_       )



   def  _UpperCAmelCase (				self,    *lowercase_,    **lowercase_       )							->    Dict:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    *lowercase_,    **lowercase_       )							->    Union[str, Any]:






     """simple docstring"""






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



   @property
   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =self.tokenizer.model_input_names
     a__  =self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )



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 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	List[Any]    =   IFInpaintingSuperResolutionPipeline
   lowerCamelCase__ :	str    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
   lowerCamelCase__ :	List[Any]    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
   lowerCamelCase__ :	str    =   PipelineTesterMixin.required_optional_params - {'latents'}



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     return self._get_superresolution_dummy_components()



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0       )							->    Tuple:






     """simple docstring"""






     if str(lowercase_       ).startswith('''mps'''       ):
       a__  =torch.manual_seed(lowercase_       )
     else:
       a__  =torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

     a__  =floats_tensor((1, 3, 16, 16),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )

     a__  ={
         '''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  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda''',    reason='''float16 requires CUDA'''       )
   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     super().test_save_load_floataa(expected_max_diff=1E-1       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     self._test_save_load_local()



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






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