


"""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   _UpperCamelCase  (   lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[str]				      ="""ssube/stable-diffusion-x4-upscaler-onnx"""

							def 			snake_case				(     self    ,   __a=0	):
										__lowerCAmelCase				    =			floats_tensor((1, 3, 1_28, 1_28)    ,   rng=random.Random(__a	)	)
										__lowerCAmelCase				    =			torch.manual_seed(__a	)
										__lowerCAmelCase				    =			{
										    "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	):
										__lowerCAmelCase				    =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"	)
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			self.get_dummy_inputs()
										__lowerCAmelCase				    =			pipe(**__a	).images
										__lowerCAmelCase				    =			image[0, -3:, -3:, -1].flatten()

										# started as 128, should now be 512
										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase				    =			np.array(
										    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]	)
										assert np.abs(image_slice - expected_slice	).max() < 1e-1

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"	)
										__lowerCAmelCase				    =			PNDMScheduler.from_config(pipe.scheduler.config    ,   skip_prk_steps=__a	)
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			self.get_dummy_inputs()
										__lowerCAmelCase				    =			pipe(**__a	).images
										__lowerCAmelCase				    =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase				    =			np.array(
										    [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]	)
										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"	)
										__lowerCAmelCase				    =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			self.get_dummy_inputs()
										__lowerCAmelCase				    =			pipe(**__a	).images
										__lowerCAmelCase				    =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase				    =			np.array(
										    [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"	)
										__lowerCAmelCase				    =			EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			self.get_dummy_inputs()
										__lowerCAmelCase				    =			pipe(**__a	).images
										__lowerCAmelCase				    =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase				    =			np.array(
										    [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]	)
										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"	)
										__lowerCAmelCase				    =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			self.get_dummy_inputs()
										__lowerCAmelCase				    =			pipe(**__a	).images
										__lowerCAmelCase				    =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase				    =			np.array(
										    [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







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

							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ort.SessionOptions()
										__lowerCAmelCase				    =			False
										return options

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"	)
										__lowerCAmelCase				    =			init_image.resize((1_28, 1_28)	)
										# using the PNDM scheduler by default
										__lowerCAmelCase				    =			OnnxStableDiffusionUpscalePipeline.from_pretrained(
										    "ssube/stable-diffusion-x4-upscaler-onnx"    ,   provider=self.gpu_provider    ,   sess_options=self.gpu_options    ,   )
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			"A fantasy landscape, trending on artstation"

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

										assert images.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase				    =			np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]	)
										# 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	):
										__lowerCAmelCase				    =			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"	)
										__lowerCAmelCase				    =			init_image.resize((1_28, 1_28)	)
										__lowerCAmelCase				    =			LMSDiscreteScheduler.from_pretrained(
										    "ssube/stable-diffusion-x4-upscaler-onnx"    ,   subfolder="scheduler"	)
										__lowerCAmelCase				    =			OnnxStableDiffusionUpscalePipeline.from_pretrained(
										    "ssube/stable-diffusion-x4-upscaler-onnx"    ,   scheduler=__a    ,   provider=self.gpu_provider    ,   sess_options=self.gpu_options    ,   )
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			"A fantasy landscape, trending on artstation"

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

										assert images.shape == (1, 5_12, 5_12, 3)
										__lowerCAmelCase				    =			np.array(
										    [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]	)
										# 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 logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=0.0       ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Whether to SortishSamler or not."""}		)
							__UpperCAmelCase						:    bool				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """whether to use adafactor"""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[str]				      =field(
							    default="""linear"""       ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'''}       ,)




