




'''simple docstring'''







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					['''image_processor''', '''tokenizer''']
     _UpperCAmelCase										=					'''ViltImageProcessor'''
     _UpperCAmelCase										=					('''BertTokenizer''', '''BertTokenizerFast''')







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	Union[str, Any]:
      snake_case				=			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__    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      snake_case				=			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__    )
      snake_case				=			self.image_processor







     def __call__( self    ,   A__    ,   A__ = None    ,   A__ = True    ,   A__ = False    ,   A__ = None    ,   A__ = None    ,   A__ = 0    ,   A__ = None    ,   A__ = None    ,   A__ = None    ,   A__ = False    ,   A__ = False    ,   A__ = False    ,   A__ = False    ,   A__ = True    ,   A__ = None    ,   **A__    ,   ) ->	BatchEncoding:
      snake_case				=			self.tokenizer(
          text=A__    ,   add_special_tokens=A__    ,   padding=A__    ,   truncation=A__    ,   max_length=A__    ,   stride=A__    ,   pad_to_multiple_of=A__    ,   return_token_type_ids=A__    ,   return_attention_mask=A__    ,   return_overflowing_tokens=A__    ,   return_special_tokens_mask=A__    ,   return_offsets_mapping=A__    ,   return_length=A__    ,   verbose=A__    ,   return_tensors=A__    ,   **A__    ,   )
      # add pixel_values + pixel_mask
      snake_case				=			self.image_processor(A__    ,   return_tensors=A__    )
      encoding.update(A__    )

      return encoding







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Optional[int]:
      return self.tokenizer.batch_decode(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	Union[str, Any]:
      return self.tokenizer.decode(*A__    ,   **A__    )







     @property
     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.tokenizer.model_input_names
      snake_case				=			self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







     @property
     def     UpperCamelCase			( self    ) ->	Tuple:
      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    ) ->	Optional[int]:
      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,   A__    ,   )
      return self.image_processor








'''simple docstring'''







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 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					IFInpaintingSuperResolutionPipeline
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
     _UpperCAmelCase										=					PipelineTesterMixin.required_optional_params - {'''latents'''}







     def     UpperCamelCase			( self    ) ->	int:
      return self._get_superresolution_dummy_components()







     def     UpperCamelCase			( self    ,   A__    ,   A__=0    ) ->	Union[str, Any]:
      if str(A__    ).startswith('''mps'''    ):
       snake_case				=			torch.manual_seed(A__    )
      else:
       snake_case				=			torch.Generator(device=A__    ).manual_seed(A__    )

      snake_case				=			floats_tensor((1, 3, 16, 16)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )

      snake_case				=			{
          '''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[Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_optional_components()







     @unittest.skipIf(torch_device != '''cuda'''    ,   reason='''float16 requires CUDA'''    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      # 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     UpperCamelCase			( self    ) ->	int:
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_local()







     def     UpperCamelCase			( self    ) ->	Dict:
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2    ,   )

