






'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort




class     A    (      SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						List[Any]      =						'''ssube/stable-diffusion-x4-upscaler-onnx'''
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=0 ):
       UpperCamelCase_				:   Union[str, Any]					    =							floats_tensor((1, 3, 1_28, 1_28)			,     rng=random.Random(__lowerCAmelCase ) )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.manual_seed(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							{
           """prompt""": """A painting of a squirrel eating a burger""",
           """image""": image,
           """generator""": generator,
           """num_inference_steps""": 3,
           """guidance_scale""": 7.5,
           """output_type""": """numpy""",
       }
       return inputs
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="""CPUExecutionProvider""" )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							self.get_dummy_inputs()
       UpperCamelCase_				:   Optional[Any]					    =							pipe(**__lowerCAmelCase ).images
       UpperCamelCase_				:   Optional[int]					    =							image[0, -3:, -3:, -1].flatten()

       # started as 128, should now be 512
       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   Union[str, Any]					    =							np.array(
           [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23] )
       assert np.abs(image_slice - expected_slice ).max() < 1E-1
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="""CPUExecutionProvider""" )
       UpperCamelCase_				:   Any					    =							PNDMScheduler.from_config(pipe.scheduler.config			,     skip_prk_steps=__lowerCAmelCase )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   Optional[Any]					    =							self.get_dummy_inputs()
       UpperCamelCase_				:   int					    =							pipe(**__lowerCAmelCase ).images
       UpperCamelCase_				:   str					    =							image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   List[Any]					    =							np.array(
           [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64] )
       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="""CPUExecutionProvider""" )
       UpperCamelCase_				:   str					    =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   str					    =							self.get_dummy_inputs()
       UpperCamelCase_				:   str					    =							pipe(**__lowerCAmelCase ).images
       UpperCamelCase_				:   List[Any]					    =							image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   List[Any]					    =							np.array(
           [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="""CPUExecutionProvider""" )
       UpperCamelCase_				:   Optional[int]					    =							EulerDiscreteScheduler.from_config(pipe.scheduler.config )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   List[Any]					    =							self.get_dummy_inputs()
       UpperCamelCase_				:   Union[str, Any]					    =							pipe(**__lowerCAmelCase ).images
       UpperCamelCase_				:   Dict					    =							image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   Optional[int]					    =							np.array(
           [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23] )
       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,     provider="""CPUExecutionProvider""" )
       UpperCamelCase_				:   Optional[int]					    =							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							self.get_dummy_inputs()
       UpperCamelCase_				:   Optional[int]					    =							pipe(**__lowerCAmelCase ).images
       UpperCamelCase_				:   Union[str, Any]					    =							image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   Tuple					    =							np.array(
           [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43] )

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






@nightly
@require_onnxruntime
@require_torch_gpu
class     A    (      unittest.TestCase						):






 """simple docstring"""





 @property
 def   _UpperCAmelCase       (							self ):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )
 @property
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							ort.SessionOptions()
       UpperCamelCase_				:   Tuple					    =							False
       return options
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[Any]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/img2img/sketch-mountains-input.jpg""" )
       UpperCamelCase_				:   Any					    =							init_image.resize((1_28, 1_28) )
       # using the PNDM scheduler by default
       UpperCamelCase_				:   Tuple					    =							OnnxStableDiffusionUpscalePipeline.from_pretrained(
           """ssube/stable-diffusion-x4-upscaler-onnx"""			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							"""A fantasy landscape, trending on artstation"""

       UpperCamelCase_				:   Union[str, Any]					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   int					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )
       UpperCamelCase_				:   Any					    =							output.images
       UpperCamelCase_				:   Tuple					    =							images[0, 2_55:2_58, 3_83:3_86, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   List[Any]					    =							np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72] )
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/img2img/sketch-mountains-input.jpg""" )
       UpperCamelCase_				:   Tuple					    =							init_image.resize((1_28, 1_28) )
       UpperCamelCase_				:   List[str]					    =							LMSDiscreteScheduler.from_pretrained(
           """ssube/stable-diffusion-x4-upscaler-onnx"""			,     subfolder="""scheduler""" )
       UpperCamelCase_				:   Optional[Any]					    =							OnnxStableDiffusionUpscalePipeline.from_pretrained(
           """ssube/stable-diffusion-x4-upscaler-onnx"""			,     scheduler=__lowerCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   List[str]					    =							"""A fantasy landscape, trending on artstation"""

       UpperCamelCase_				:   Tuple					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   str					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )
       UpperCamelCase_				:   Optional[int]					    =							output.images
       UpperCamelCase_				:   Tuple					    =							images[0, 2_55:2_58, 3_83:3_86, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       UpperCamelCase_				:   Dict					    =							np.array(
           [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66] )
       # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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














'''simple docstring'''

import warnings

from .generation import TFGenerationMixin




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 warnings.warn(
     '''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''',   SCREAMING_SNAKE_CASE__,   )






