



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 				snake_case__(_lowercase    ,      unittest.TestCase      ):

     """simple docstring"""






     lowercase_								=		"""ssube/stable-diffusion-x4-upscaler-onnx"""







     def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     Optional[Any]=0					):
         lowercase__					:   List[str]													=					floats_tensor((1, 3, 128, 128)				,       rng=random.Random(__UpperCamelCase					)					)
         lowercase__					:   Tuple													=					torch.manual_seed(__UpperCamelCase					)
         lowercase__					:   int													=					{
             "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 			snake_case					(							self :     List[str]					):
         lowercase__					:   int													=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"					)
         pipe.set_progress_bar_config(disable=__UpperCamelCase					)

         lowercase__					:   str													=					self.get_dummy_inputs()
         lowercase__					:   Union[str, Any]													=					pipe(**__UpperCamelCase					).images
         lowercase__					:   Optional[int]													=					image[0, -3:, -3:, -1].flatten()

         # started as 128, should now be 512
         assert image.shape == (1, 512, 512, 3)
         lowercase__					:   Optional[Any]													=					np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]					)
         assert np.abs(image_slice - expected_slice					).max() < 1E-1







     def 			snake_case					(							self :     int					):
         lowercase__					:   List[Any]													=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"					)
         lowercase__					:   Any													=					PNDMScheduler.from_config(pipe.scheduler.config				,       skip_prk_steps=__UpperCamelCase					)
         pipe.set_progress_bar_config(disable=__UpperCamelCase					)

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

         assert image.shape == (1, 512, 512, 3)
         lowercase__					:   Tuple													=					np.array(
             [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]					)
         assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-1







     def 			snake_case					(							self :     Any					):
         lowercase__					:   str													=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"					)
         lowercase__					:   Union[str, Any]													=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
         pipe.set_progress_bar_config(disable=__UpperCamelCase					)

         lowercase__					:   List[str]													=					self.get_dummy_inputs()
         lowercase__					:   Dict													=					pipe(**__UpperCamelCase					).images
         lowercase__					:   Optional[Any]													=					image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         lowercase__					:   Any													=					np.array(
             [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]					)

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







     def 			snake_case					(							self :     Dict					):
         lowercase__					:   Dict													=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"					)
         lowercase__					:   Any													=					EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
         pipe.set_progress_bar_config(disable=__UpperCamelCase					)

         lowercase__					:   List[Any]													=					self.get_dummy_inputs()
         lowercase__					:   Dict													=					pipe(**__UpperCamelCase					).images
         lowercase__					:   Any													=					image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         lowercase__					:   Dict													=					np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]					)
         assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-1







     def 			snake_case					(							self :     Tuple					):
         lowercase__					:   str													=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"					)
         lowercase__					:   List[Any]													=					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
         pipe.set_progress_bar_config(disable=__UpperCamelCase					)

         lowercase__					:   Dict													=					self.get_dummy_inputs()
         lowercase__					:   Dict													=					pipe(**__UpperCamelCase					).images
         lowercase__					:   int													=					image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         lowercase__					:   Optional[int]													=					np.array(
             [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]					)

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






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

     """simple docstring"""






     @property
     def 			snake_case					(							self :     Any					):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )







     @property
     def 			snake_case					(							self :     Union[str, Any]					):
         lowercase__					:   List[str]													=					ort.SessionOptions()
         lowercase__					:   Union[str, Any]													=					False
         return options







     def 			snake_case					(							self :     Any					):
         lowercase__					:   Tuple													=					load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"					)
         lowercase__					:   int													=					init_image.resize((128, 128)					)
         # using the PNDM scheduler by default
         lowercase__					:   Union[str, Any]													=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
             "ssube/stable-diffusion-x4-upscaler-onnx"				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
         pipe.set_progress_bar_config(disable=__UpperCamelCase					)

         lowercase__					:   Any													=					"A fantasy landscape, trending on artstation"

         lowercase__					:   Any													=					torch.manual_seed(0					)
         lowercase__					:   Any													=					pipe(
             prompt=__UpperCamelCase				,       image=__UpperCamelCase				,       guidance_scale=7.5				,       num_inference_steps=10				,       generator=__UpperCamelCase				,       output_type="np"				,       )
         lowercase__					:   Optional[Any]													=					output.images
         lowercase__					:   Union[str, Any]													=					images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 512, 3)
         lowercase__					:   Tuple													=					np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]					)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







     def 			snake_case					(							self :     Tuple					):
         lowercase__					:   Any													=					load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"					)
         lowercase__					:   int													=					init_image.resize((128, 128)					)
         lowercase__					:   List[Any]													=					LMSDiscreteScheduler.from_pretrained(
             "ssube/stable-diffusion-x4-upscaler-onnx"				,       subfolder="scheduler"					)
         lowercase__					:   Tuple													=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
             "ssube/stable-diffusion-x4-upscaler-onnx"				,       scheduler=__UpperCamelCase				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
         pipe.set_progress_bar_config(disable=__UpperCamelCase					)

         lowercase__					:   Any													=					"A fantasy landscape, trending on artstation"

         lowercase__					:   List[Any]													=					torch.manual_seed(0					)
         lowercase__					:   Union[str, Any]													=					pipe(
             prompt=__UpperCamelCase				,       image=__UpperCamelCase				,       guidance_scale=7.5				,       num_inference_steps=20				,       generator=__UpperCamelCase				,       output_type="np"				,       )
         lowercase__					:   Optional[int]													=					output.images
         lowercase__					:   Tuple													=					images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 512, 3)
         lowercase__					:   Any													=					np.array(
             [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]					)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb import NllbTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb_fast import NllbTokenizerFast

else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




