


'''simple docstring'''
import numpy as np

from transformers import Pipeline

def 					lowerCamelCase_ ( __UpperCamelCase					:       Union[str, Any]		)					->	Tuple:







     """simple docstring"""
     _A				=							np.max(__UpperCamelCase							,    axis=-1							,    keepdims=__UpperCamelCase		)
     _A				=							np.exp(outputs - maxes		)
     return shifted_exp / shifted_exp.sum(axis=-1							,    keepdims=__UpperCamelCase		)






class        lowerCAmelCase_  (     UpperCAmelCase	):






       def   UpperCamelCase						( self			,     **_UpperCamelCase   )->      List[str]:
            _A				=							{}
            if "second_text" in kwargs:
                 _A				=							kwargs['second_text']
            return preprocess_kwargs, {}, {}






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=None   )->      List[str]:
            return self.tokenizer(_UpperCamelCase			,     text_pair=_UpperCamelCase			,     return_tensors=self.framework   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      List[Any]:
            return self.model(**_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      str:
            _A				=							model_outputs.logits[0].numpy()
            _A				=							softmax(_UpperCamelCase   )

            _A				=							np.argmax(_UpperCamelCase   )
            _A				=							self.model.config.idalabel[best_class]
            _A				=							probabilities[best_class].item()
            _A				=							logits.tolist()
            return {"label": label, "score": score, "logits": logits}









'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_dpmpp_2m'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe(
                [prompt]			,     generator=_UpperCamelCase			,     guidance_scale=7.5			,     num_inference_steps=15			,     output_type='np'			,     use_karras_sigmas=_UpperCamelCase			,     )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2





