




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				(		__lowercase       ,     unittest.TestCase     ):
		# TODO: is there an appropriate internal test set?
		__a					    =       """ssube/stable-diffusion-x4-upscaler-onnx"""






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=0			):
								_UpperCAmelCase      =			floats_tensor((1, 3, 128, 128)						,		rng=random.Random(_SCREAMING_SNAKE_CASE			)			)
								_UpperCAmelCase      =			torch.manual_seed(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			{
								    """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      =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,		provider="""CPUExecutionProvider"""			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

								# started as 128, should now be 512
								assert image.shape == (1, 512, 512, 3)
								_UpperCAmelCase      =			np.array(
								    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]			)
								assert np.abs(image_slice - expected_slice			).max() < 1e-1






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,		provider="""CPUExecutionProvider"""			)
								_UpperCAmelCase      =			PNDMScheduler.from_config(pipe.scheduler.config						,		skip_prk_steps=_SCREAMING_SNAKE_CASE			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

								assert image.shape == (1, 512, 512, 3)
								_UpperCAmelCase      =			np.array(
								    [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]			)
								assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-1






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,		provider="""CPUExecutionProvider"""			)
								_UpperCAmelCase      =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

								assert image.shape == (1, 512, 512, 3)
								_UpperCAmelCase      =			np.array(
								    [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]			)

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,		provider="""CPUExecutionProvider"""			)
								_UpperCAmelCase      =			EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

								assert image.shape == (1, 512, 512, 3)
								_UpperCAmelCase      =			np.array(
								    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]			)
								assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-1






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint						,		provider="""CPUExecutionProvider"""			)
								_UpperCAmelCase      =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

								assert image.shape == (1, 512, 512, 3)
								_UpperCAmelCase      =			np.array(
								    [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]			)

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




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






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






		@property
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			ort.SessionOptions()
								_UpperCAmelCase      =			False
								return options






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

								_UpperCAmelCase      =			"""A fantasy landscape, trending on artstation"""

								_UpperCAmelCase      =			torch.manual_seed(0			)
								_UpperCAmelCase      =			pipe(
								    prompt=_SCREAMING_SNAKE_CASE						,		image=_SCREAMING_SNAKE_CASE						,		guidance_scale=7.5						,		num_inference_steps=10						,		generator=_SCREAMING_SNAKE_CASE						,		output_type="""np"""						,		)
								_UpperCAmelCase      =			output.images
								_UpperCAmelCase      =			images[0, 255:258, 383:386, -1]

								assert images.shape == (1, 512, 512, 3)
								_UpperCAmelCase      =			np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]			)
								# 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      =			load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/img2img/sketch-mountains-input.jpg"""			)
								_UpperCAmelCase      =			init_image.resize((128, 128)			)
								_UpperCAmelCase      =			LMSDiscreteScheduler.from_pretrained(
								    """ssube/stable-diffusion-x4-upscaler-onnx"""						,		subfolder="""scheduler"""			)
								_UpperCAmelCase      =			OnnxStableDiffusionUpscalePipeline.from_pretrained(
								    """ssube/stable-diffusion-x4-upscaler-onnx"""						,		scheduler=_SCREAMING_SNAKE_CASE						,		provider=self.gpu_provider						,		sess_options=self.gpu_options						,		)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			"""A fantasy landscape, trending on artstation"""

								_UpperCAmelCase      =			torch.manual_seed(0			)
								_UpperCAmelCase      =			pipe(
								    prompt=_SCREAMING_SNAKE_CASE						,		image=_SCREAMING_SNAKE_CASE						,		guidance_scale=7.5						,		num_inference_steps=20						,		generator=_SCREAMING_SNAKE_CASE						,		output_type="""np"""						,		)
								_UpperCAmelCase      =			output.images
								_UpperCAmelCase      =			images[0, 255:258, 383:386, -1]

								assert images.shape == (1, 512, 512, 3)
								_UpperCAmelCase      =			np.array(
								    [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]			)
								# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





import logging

from transformers.configuration_utils import PretrainedConfig


a													=			logging.getLogger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       """masked_bert"""






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3_0522						,		_SCREAMING_SNAKE_CASE=768						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=3072						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-12						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE="topK"						,		_SCREAMING_SNAKE_CASE="constant"						,		_SCREAMING_SNAKE_CASE=0.0						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			hidden_size
								_UpperCAmelCase      =			num_hidden_layers
								_UpperCAmelCase      =			num_attention_heads
								_UpperCAmelCase      =			hidden_act
								_UpperCAmelCase      =			intermediate_size
								_UpperCAmelCase      =			hidden_dropout_prob
								_UpperCAmelCase      =			attention_probs_dropout_prob
								_UpperCAmelCase      =			max_position_embeddings
								_UpperCAmelCase      =			type_vocab_size
								_UpperCAmelCase      =			initializer_range
								_UpperCAmelCase      =			layer_norm_eps
								_UpperCAmelCase      =			pruning_method
								_UpperCAmelCase      =			mask_init
								_UpperCAmelCase      =			mask_scale