

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 				a  (	_UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"


						def        _UpperCAmelCase				(       self						,					A_=0	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    floats_tensor((1, 3, 128, 128)						,					rng=random.Random(lowercase__	)	)
										_UpperCAmelCase       :		int 		=    np.random.RandomState(lowercase__	)
										_UpperCAmelCase       :		Union[str, Any] 		=    {
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "strength": 0.75,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										pipe.set_progress_bar_config(disable=lowercase__	)

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

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		Optional[int] 		=    np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]	)
										assert np.abs(image_slice - expected_slice	).max() < 1e-1


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		List[Any] 		=    PNDMScheduler.from_config(pipe.scheduler.config						,					skip_prk_steps=lowercase__	)
										pipe.set_progress_bar_config(disable=lowercase__	)

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

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		List[str] 		=    np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		Any 		=    LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowercase__	)

										# warmup pass to apply optimizations
										_UpperCAmelCase       :		List[str] 		=    pipe(**self.get_dummy_inputs()	)

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

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		Optional[Any] 		=    np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		List[Any] 		=    EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowercase__	)

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

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		Tuple 		=    np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]	)

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		str 		=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowercase__	)

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

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		int 		=    np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]	)

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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,					provider="CPUExecutionProvider"	)
										_UpperCAmelCase       :		List[str] 		=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
										pipe.set_progress_bar_config(disable=lowercase__	)

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

										assert image.shape == (1, 128, 128, 3)
										_UpperCAmelCase       :		Optional[Any] 		=    np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]	)

										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	):







										'''simple docstring'''


										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    ort.SessionOptions()
										_UpperCAmelCase       :		Tuple 		=    False
										return options


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"	)
										_UpperCAmelCase       :		Optional[Any] 		=    init_image.resize((768, 512)	)
										# using the PNDM scheduler by default
										_UpperCAmelCase       :		Dict 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "CompVis/stable-diffusion-v1-4"						,					revision="onnx"						,					safety_checker=lowercase__						,					feature_extractor=lowercase__						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
										pipe.set_progress_bar_config(disable=lowercase__	)

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

										_UpperCAmelCase       :		int 		=    np.random.RandomState(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe(
										    prompt=lowercase__						,					image=lowercase__						,					strength=0.75						,					guidance_scale=7.5						,					num_inference_steps=10						,					generator=lowercase__						,					output_type="np"						,					)
										_UpperCAmelCase       :		Optional[int] 		=    output.images
										_UpperCAmelCase       :		Any 		=    images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 768, 3)
										_UpperCAmelCase       :		List[Any] 		=    np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]	)
										# 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	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"	)
										_UpperCAmelCase       :		Any 		=    init_image.resize((768, 512)	)
										_UpperCAmelCase       :		Optional[Any] 		=    LMSDiscreteScheduler.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					subfolder="scheduler"						,					revision="onnx"	)
										_UpperCAmelCase       :		List[str] 		=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"						,					revision="onnx"						,					scheduler=lowercase__						,					safety_checker=lowercase__						,					feature_extractor=lowercase__						,					provider=self.gpu_provider						,					sess_options=self.gpu_options						,					)
										pipe.set_progress_bar_config(disable=lowercase__	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "A fantasy landscape, trending on artstation"

										_UpperCAmelCase       :		Union[str, Any] 		=    np.random.RandomState(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    pipe(
										    prompt=lowercase__						,					image=lowercase__						,					strength=0.75						,					guidance_scale=7.5						,					num_inference_steps=20						,					generator=lowercase__						,					output_type="np"						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images
										_UpperCAmelCase       :		Optional[Any] 		=    images[0, 255:258, 383:386, -1]

										assert images.shape == (1, 512, 768, 3)
										_UpperCAmelCase       :		str 		=    np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]	)
										# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"openai-gpt"
						_lowercase    						=			{
						    "max_position_embeddings": "n_positions",
						    "hidden_size": "n_embd",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}


						def __init__(       self						,					A_=40478						,					A_=512						,					A_=768						,					A_=12						,					A_=12						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=0.1						,					A_=1e-5						,					A_=0.02						,					A_="cls_index"						,					A_=True						,					A_=None						,					A_=True						,					A_=0.1						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    vocab_size
										_UpperCAmelCase       :		str 		=    n_positions
										_UpperCAmelCase       :		List[Any] 		=    n_embd
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Any 		=    n_head
										_UpperCAmelCase       :		int 		=    afn
										_UpperCAmelCase       :		int 		=    resid_pdrop
										_UpperCAmelCase       :		Tuple 		=    embd_pdrop
										_UpperCAmelCase       :		Optional[Any] 		=    attn_pdrop
										_UpperCAmelCase       :		Any 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[str] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    summary_type
										_UpperCAmelCase       :		List[Any] 		=    summary_use_proj
										_UpperCAmelCase       :		Optional[Any] 		=    summary_activation
										_UpperCAmelCase       :		int 		=    summary_first_dropout
										_UpperCAmelCase       :		List[str] 		=    summary_proj_to_labels
										super().__init__(**A_	)


