"""simple docstring"""







from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, 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():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)







@add_end_docstrings(lowercase_						)
class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   str:


        '''simple docstring'''


        super().__init__(*__UpperCamelCase  ,**__UpperCamelCase    )
        requires_backends(self  ,'vision'    )
        self.check_model_type(__UpperCamelCase    )
   def __call__(    self  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        return super().__call__(__UpperCamelCase  ,**__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,**__UpperCamelCase    )			->   Optional[int]:


        '''simple docstring'''


        return {}, {}, {}
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        lowercase_			:       str									=			load_image(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			image.size
        lowercase_			:       str									=			self.image_processor(images=__UpperCamelCase  ,return_tensors=self.framework    )
        return model_inputs
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			self.model(**__UpperCamelCase    )
        return model_outputs







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       int									=			model_outputs.predicted_depth
        lowercase_			:       Union[str, Any]									=			torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1    )  ,size=self.image_size[::-1]  ,mode='bicubic'  ,align_corners=__UpperCamelCase    )
        lowercase_			:       Any									=			prediction.squeeze().cpu().numpy()
        lowercase_			:       List[str]									=			(output * 255 / np.max(__UpperCamelCase    )).astype('uint8'    )
        lowercase_			:       Dict									=			Image.fromarray(__UpperCamelCase    )
        lowercase_			:       List[Any]									=			{}
        lowercase_			:       str									=			predicted_depth
        lowercase_			:       Optional[int]									=			depth
        return output_dict


"""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  UpperCamelCase	(    lowercase_		,   lowercase_		,   unittest.TestCase						):
   lowercase							=						IFImgaImgSuperResolutionPipeline
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}						)
   lowercase							=						PipelineTesterMixin.required_optional_params - {'latents'}
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self._get_superresolution_dummy_components()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase=0    )			->   List[str]:


        '''simple docstring'''


        if str(__UpperCamelCase    ).startswith('mps'    ):
             lowercase_			:       Any									=			torch.manual_seed(__UpperCamelCase    )
        else:
             lowercase_			:       int									=			torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
        lowercase_			:       List[str]									=			floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			{
            '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     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


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


        '''simple docstring'''


        super().test_save_load_floataa(expected_max_diff=1e-1    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self._test_save_load_local()







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


        '''simple docstring'''


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

