



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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,					_snake_case					:       bool = True				,					_snake_case					:       Union[bool, str, PaddingStrategy] = False				,					_snake_case					:       Union[bool, str, TruncationStrategy] = None				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       int = 0				,					_snake_case					:       Optional[int] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       Optional[bool] = None				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = False				,					_snake_case					:       bool = True				,					_snake_case					:       Optional[Union[str, TensorType]] = None				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    self.tokenizer(
								    text=_snake_case				,					add_special_tokens=_snake_case				,					padding=_snake_case				,					truncation=_snake_case				,					max_length=_snake_case				,					stride=_snake_case				,					pad_to_multiple_of=_snake_case				,					return_token_type_ids=_snake_case				,					return_attention_mask=_snake_case				,					return_overflowing_tokens=_snake_case				,					return_special_tokens_mask=_snake_case				,					return_offsets_mapping=_snake_case				,					return_length=_snake_case				,					verbose=_snake_case				,					return_tensors=_snake_case				,					**_snake_case				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


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



					@property
					def     _a							(     self					:       Tuple					):






								"""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 IFInpaintingPipeline
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      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Any			       =    IFInpaintingPipeline
					A__				:		Dict			       =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
					A__				:		List[Any]			       =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				:		Dict			       =    PipelineTesterMixin.required_optional_params - {"latents"}



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._get_dummy_components()



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Any				,					_snake_case					:       str=0					):






								"""simple docstring"""


								if str(_snake_case					).startswith('mps'					):
											A__       =    torch.manual_seed(_snake_case					)
								else:
											A__       =    torch.Generator(device=_snake_case					).manual_seed(_snake_case					)

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

								A__       =    {
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': 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     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self._test_save_load_optional_components()



					@unittest.skipIf(torch_device != 'cuda'				,					reason='float16 requires CUDA'					)
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								self._test_save_load_local()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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




