


'''simple docstring'''






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  __magic_name__							(		lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=IFInpaintingPipeline
       UpperCAmelCase										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
       UpperCAmelCase										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCAmelCase										=PipelineTesterMixin.required_optional_params - {"latents"}






       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          return self._get_dummy_components()






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=0)						->  Any:




          '''simple docstring'''
          if str(snake_case).startswith('mps'):
             _UpperCAmelCase       :       str							   =torch.manual_seed(snake_case)
          else:
             _UpperCAmelCase       :       List[str]							   =torch.Generator(device=snake_case).manual_seed(snake_case)

          _UpperCAmelCase       :       int							   =floats_tensor((1, 3, 3_2, 3_2)  ,							rng=random.Random(snake_case)).to(snake_case)
          _UpperCAmelCase       :       List[str]							   =floats_tensor((1, 3, 3_2, 3_2)  ,							rng=random.Random(snake_case)).to(snake_case)

          _UpperCAmelCase       :       Dict							   ={
              '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       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''simple docstring'''
          self._test_save_load_optional_components()






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




          '''simple docstring'''
          # 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       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          self._test_save_load_local()






       def       lowerCAmelCase		(      self)						->  List[str]:




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





'''simple docstring'''






from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

lowercase							=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase    )
class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							**snake_case)						->  Optional[int]:




          '''simple docstring'''
          super().__init__(**snake_case)

          requires_backends(self  ,							'vision')
          self.check_model_type(
              TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)






       def __call__(      self  ,							snake_case  ,							**snake_case)						->  str:




          '''simple docstring'''
          return super().__call__(snake_case  ,							**snake_case)






       def       lowerCAmelCase		(      self  ,							**snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   ={}
          if "candidate_labels" in kwargs:
             _UpperCAmelCase       :       Union[str, Any]							   =kwargs['candidate_labels']
          if "hypothesis_template" in kwargs:
             _UpperCAmelCase       :       List[Any]							   =kwargs['hypothesis_template']

          return preprocess_params, {}, {}






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=None  ,							snake_case="This is a photo of {}.")						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =load_image(snake_case)
          _UpperCAmelCase       :       Union[str, Any]							   =self.image_processor(images=[image]  ,							return_tensors=self.framework)
          _UpperCAmelCase       :       Union[str, Any]							   =candidate_labels
          _UpperCAmelCase       :       List[Any]							   =[hypothesis_template.format(snake_case) for x in candidate_labels]
          _UpperCAmelCase       :       str							   =self.tokenizer(snake_case  ,							return_tensors=self.framework  ,							padding=snake_case)
          _UpperCAmelCase       :       Any							   =[text_inputs]
          return inputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =model_inputs.pop('candidate_labels')
          _UpperCAmelCase       :       Tuple							   =model_inputs.pop('text_inputs')
          if isinstance(text_inputs[0]  ,							snake_case):
             _UpperCAmelCase       :       Any							   =text_inputs[0]
          else:
             # Batching case.
             _UpperCAmelCase       :       str							   =text_inputs[0][0]

          _UpperCAmelCase       :       Any							   =self.model(**snake_case  ,							**snake_case)

          _UpperCAmelCase       :       List[str]							   ={
              'candidate_labels': candidate_labels,
              'logits': outputs.logits_per_image,
          }
          return model_outputs






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =model_outputs.pop('candidate_labels')
          _UpperCAmelCase       :       Union[str, Any]							   =model_outputs['logits'][0]
          if self.framework == "pt":
             _UpperCAmelCase       :       Dict							   =logits.softmax(dim=-1).squeeze(-1)
             _UpperCAmelCase       :       Union[str, Any]							   =probs.tolist()
             if not isinstance(snake_case  ,							snake_case):
                _UpperCAmelCase       :       Union[str, Any]							   =[scores]
          elif self.framework == "tf":
             _UpperCAmelCase       :       Dict							   =stable_softmax(snake_case  ,							axis=-1)
             _UpperCAmelCase       :       str							   =probs.numpy().tolist()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

          _UpperCAmelCase       :       List[str]							   =[
              {'score': score, 'label': candidate_label}
              for score, candidate_label in sorted(zip(snake_case  ,							snake_case)  ,							key=lambda snake_case: -x[0])
          ]
          return result

