






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   (   lowercase__						,				lowercase__						,				unittest.TestCase						):
      _a							:						Tuple										=							IFInpaintingSuperResolutionPipeline
      _a							:						List[str]										=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
      _a							:						List[str]										=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}						)
      _a							:						List[Any]										=							PipelineTesterMixin.required_optional_params - {'latents'}
      def 						lowercase__					(     self   ):
       return self._get_superresolution_dummy_components()
      def 						lowercase__					(     self				,			a				,			a=0   ):



       if str(__lowerCamelCase   ).startswith("""mps"""   ):
        snake_case__  :    str							       =torch.manual_seed(__lowerCamelCase   )
       else:
        snake_case__  :    Dict							       =torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )

       snake_case__  :    Optional[int]							       =floats_tensor((1, 3, 1_6, 1_6)				,			rng=random.Random(__lowerCamelCase   )   ).to(__lowerCamelCase   )
       snake_case__  :    int							       =floats_tensor((1, 3, 3_2, 3_2)				,			rng=random.Random(__lowerCamelCase   )   ).to(__lowerCamelCase   )
       snake_case__  :    Tuple							       =floats_tensor((1, 3, 3_2, 3_2)				,			rng=random.Random(__lowerCamelCase   )   ).to(__lowerCamelCase   )

       snake_case__  :    Any							       ={
           "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 						lowercase__					(     self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )
      def 						lowercase__					(     self   ):
       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != """cuda"""				,			reason="""float16 requires CUDA"""   )
      def 						lowercase__					(     self   ):
       super().test_save_load_floataa(expected_max_diff=1e-1   )
      def 						lowercase__					(     self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )
      def 						lowercase__					(     self   ):
       self._test_save_load_local()



      def 						lowercase__					(     self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2				,			)













from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)







@add_end_docstrings(_A						)
class  _lowercase   (   _A						):
      def __init__(     self				,			**a   ):
       super().__init__(**a   )

       if self.framework == "tf":
        raise ValueError(F"The {self.__class__} is only available in PyTorch."   )

       requires_backends(self				,			"""vision"""   )
       self.check_model_type(a   )
      def __call__(     self				,			a				,			a = None				,			**a				,			):
       if "text_queries" in kwargs:
        snake_case__  :    Any							       =kwargs.pop("""text_queries"""   )

       if isinstance(a				,			(str, Image.Image)   ):
        snake_case__  :    Union[str, Any]							       ={"""image""": image, """candidate_labels""": candidate_labels}
       else:
        snake_case__  :    str							       =image
       snake_case__  :    Union[str, Any]							       =super().__call__(a				,			**a   )
       return results
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Union[str, Any]							       ={}
       if "threshold" in kwargs:
        snake_case__  :    Optional[Any]							       =kwargs["""threshold"""]
       if "top_k" in kwargs:
        snake_case__  :    Dict							       =kwargs["""top_k"""]
       return {}, {}, postprocess_params
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    List[Any]							       =load_image(inputs["""image"""]   )
       snake_case__  :    Dict							       =inputs["""candidate_labels"""]
       if isinstance(a				,			a   ):
        snake_case__  :    str							       =candidate_labels.split(""","""   )

       snake_case__  :    Optional[Any]							       =torch.tensor([[image.height, image.width]]				,			dtype=torch.intaa   )
       for i, candidate_label in enumerate(a   ):
        snake_case__  :    Tuple							       =self.tokenizer(a				,			return_tensors=self.framework   )
        snake_case__  :    int							       =self.image_processor(a				,			return_tensors=self.framework   )
        yield {
            "is_last": i == len(a   ) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =model_inputs.pop("""target_size"""   )
       snake_case__  :    Tuple							       =model_inputs.pop("""candidate_label"""   )
       snake_case__  :    Dict							       =model_inputs.pop("""is_last"""   )

       snake_case__  :    Any							       =self.model(**a   )

       snake_case__  :    List[Any]							       ={"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
       return model_outputs
      def 						lowercase__					(     self				,			a				,			a=0.1				,			a=None   ):
       snake_case__  :    Optional[int]							       =[]
       for model_output in model_outputs:
        snake_case__  :    Tuple							       =model_output["""candidate_label"""]
        snake_case__  :    Optional[int]							       =BaseModelOutput(a   )
        snake_case__  :    int							       =self.image_processor.post_process_object_detection(
            outputs=a				,			threshold=a				,			target_sizes=model_output["""target_size"""]   )[0]

        for index in outputs["scores"].nonzero():
         snake_case__  :    int							       =outputs["""scores"""][index].item()
         snake_case__  :    Tuple							       =self._get_bounding_box(outputs["""boxes"""][index][0]   )

         snake_case__  :    Tuple							       ={"""score""": score, """label""": label, """box""": box}
         results.append(a   )

       snake_case__  :    Tuple							       =sorted(a				,			key=lambda a   : x["score"]				,			reverse=a   )
       if top_k:
        snake_case__  :    Union[str, Any]							       =results[:top_k]

       return results



      def 						lowercase__					(     self				,			a   ):
       if self.framework != "pt":
        raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""   )
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Dict							       =box.int().tolist()
       snake_case__  :    int							       ={
           """xmin""": xmin,
           """ymin""": ymin,
           """xmax""": xmax,
           """ymax""": ymax,
       }
       return bbox





