


"""simple docstring"""


import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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[Any]				      ="""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

							def 			snake_case				(     self    ,   __a=0	):
										__lowerCAmelCase				    =			floats_tensor((1, 3, 1_28, 1_28)    ,   rng=random.Random(__a	)	)
										__lowerCAmelCase				    =			np.random.RandomState(__a	)
										__lowerCAmelCase				    =			{
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "strength": 0.7_5,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionImgaImgPipeline.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()

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase				    =			np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]	)
										assert np.abs(image_slice - expected_slice	).max() < 1e-1

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionImgaImgPipeline.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, 1_28, 1_28, 3)
										__lowerCAmelCase				    =			np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]	)

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

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

										# warmup pass to apply optimizations
										__lowerCAmelCase				    =			pipe(**self.get_dummy_inputs()	)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase				    =			np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionImgaImgPipeline.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, 1_28, 1_28, 3)
										__lowerCAmelCase				    =			np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]	)

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionImgaImgPipeline.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, 1_28, 1_28, 3)
										__lowerCAmelCase				    =			np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]	)

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



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OnnxStableDiffusionImgaImgPipeline.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, 1_28, 1_28, 3)
										__lowerCAmelCase				    =			np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]	)

										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((7_68, 5_12)	)
										# using the PNDM scheduler by default
										__lowerCAmelCase				    =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "CompVis/stable-diffusion-v1-4"    ,   revision="onnx"    ,   safety_checker=__a    ,   feature_extractor=__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				    =			np.random.RandomState(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=__a    ,   image=__a    ,   strength=0.7_5    ,   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, 7_68, 3)
										__lowerCAmelCase				    =			np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]	)
										# 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((7_68, 5_12)	)
										__lowerCAmelCase				    =			LMSDiscreteScheduler.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"    ,   subfolder="scheduler"    ,   revision="onnx"	)
										__lowerCAmelCase				    =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"    ,   revision="onnx"    ,   scheduler=__a    ,   safety_checker=__a    ,   feature_extractor=__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				    =			np.random.RandomState(0	)
										__lowerCAmelCase				    =			pipe(
										    prompt=__a    ,   image=__a    ,   strength=0.7_5    ,   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, 7_68, 3)
										__lowerCAmelCase				    =			np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]	)
										# 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 transformers.configuration_utils import PretrainedConfig


a_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """masked_bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="topK"					,						__lowerCamelCase="constant"					,						__lowerCamelCase=0.0					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Union[str, Any]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						Tuple						 =     num_attention_heads
     __A    :						Optional[Any]						 =     hidden_act
     __A    :						List[str]						 =     intermediate_size
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Any						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     layer_norm_eps
     __A    :						Optional[int]						 =     pruning_method
     __A    :						str						 =     mask_init
     __A    :						Any						 =     mask_scale




