

'''simple docstring'''
import warnings

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


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					int     	=							'CLIPImageProcessor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							('CLIPTokenizer', 'CLIPTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				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."      ,    _UpperCamelCase      ,    )
         _lowercase      :				int      					=       kwargs.pop("feature_extractor"  )

      _lowercase      :				Any      					=       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__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """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:
         _lowercase      :				int      					=       self.tokenizer(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if images is not None:
         _lowercase      :				Dict      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

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


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       self.tokenizer.model_input_names
      _lowercase      :				Tuple      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor_class






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor









'''simple docstring'''
import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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        a__  (							lowerCamelCase_  ,				lowerCamelCase_  ,				unittest.TestCase  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							IFImgaImgSuperResolutionPipeline
   _SCREAMING_SNAKE_CASE   :					int     	=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}  )
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							PipelineTesterMixin.required_optional_params - {'latents'}


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return self._get_superresolution_dummy_components()


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=0  ):





      """simple docstring"""





      if str(_UpperCamelCase  ).startswith("mps"  ):
         _lowercase      :				Tuple      					=       torch.manual_seed(_UpperCamelCase  )
      else:
         _lowercase      :				int      					=       torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )

      _lowercase      :				List[Any]      					=       floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       floats_tensor((1, 3, 16, 16)      ,    rng=random.Random(_UpperCamelCase  )  ).to(_UpperCamelCase  )

      _lowercase      :				List[str]      					=       {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "original_image": original_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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != "cuda"      ,    reason="float16 requires CUDA"  )
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self._test_save_load_local()






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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






