
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				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"






	def     UpperCamelCase_  (	self :			List[str]							,A :			Dict=0			):
					__A             =			floats_tensor((1, 3, 1_28, 1_28)							,rng=random.Random(A			)			)
					__A             =			np.random.RandomState(A			)
					__A             =			{
					    "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 :			int			):
					__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					pipe.set_progress_bar_config(disable=A			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			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 :			Optional[Any]			):
					__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			PNDMScheduler.from_config(pipe.scheduler.config							,skip_prk_steps=A			)
					pipe.set_progress_bar_config(disable=A			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			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 :			Union[str, Any]			):
					__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=A			)

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

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			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 :			Optional[Any]			):
					__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=A			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			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 :			str			):
					__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=A			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			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 :			Optional[int]			):
					__A             =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,provider="CPUExecutionProvider"			)
					__A             =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
					pipe.set_progress_bar_config(disable=A			)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A             =			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 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





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






	@property
	def     UpperCamelCase_  (	self :			str			):
					__A             =			ort.SessionOptions()
					__A             =			False
					return options






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/img2img/sketch-mountains-input.jpg"			)
					__A             =			init_image.resize((7_68, 5_12)			)
					# using the PNDM scheduler by default
					__A             =			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			)

					__A             =			"A fantasy landscape, trending on artstation"

					__A             =			np.random.RandomState(0			)
					__A             =			pipe(
					    prompt=A							,image=A							,strength=0.75							,guidance_scale=7.5							,num_inference_steps=10							,generator=A							,output_type="np"							,)
					__A             =			output.images
					__A             =			images[0, 2_55:2_58, 3_83:3_86, -1]

					assert images.shape == (1, 5_12, 7_68, 3)
					__A             =			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 :			List[Any]			):
					__A             =			load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/img2img/sketch-mountains-input.jpg"			)
					__A             =			init_image.resize((7_68, 5_12)			)
					__A             =			LMSDiscreteScheduler.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"							,subfolder="scheduler"							,revision="onnx"			)
					__A             =			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			)

					__A             =			"A fantasy landscape, trending on artstation"

					__A             =			np.random.RandomState(0			)
					__A             =			pipe(
					    prompt=A							,image=A							,strength=0.75							,guidance_scale=7.5							,num_inference_steps=20							,generator=A							,output_type="np"							,)
					__A             =			output.images
					__A             =			images[0, 2_55:2_58, 3_83:3_86, -1]

					assert images.shape == (1, 5_12, 7_68, 3)
					__A             =			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


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="automatic-speech-recognition"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"audio": Audio()}  )
	snake_case_    =					Features({"transcription": Value("string"  )}  )
	snake_case_    =					"audio"
	snake_case_    =					"transcription"






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]			):
					if self.audio_column not in features:
									raise ValueError(f'''Column {self.audio_column} is not present in features.'''			)
					if not isinstance(features[self.audio_column]							,A			):
									raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.input_schema.copy()
					__A             =			features[self.audio_column]
					__A             =			input_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			int			):
					return {self.audio_column: "audio", self.transcription_column: "transcription"}
