





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							(						__lowercase		,   unittest.TestCase    ):
      _snake_case				  ='''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



      def 						lowerCAmelCase__     (						self:    Union[str, Any]		,	_lowerCAmelCase:    Dict=0							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =floats_tensor((1, 3, 128, 128)		,	rng=random.Random(_lowerCAmelCase							)							)
         UpperCAmelCase_           =np.random.RandomState(_lowerCAmelCase							)
         UpperCAmelCase_           ={
             "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 						lowerCAmelCase__     (						self:    Any							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

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

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    Dict							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =PNDMScheduler.from_config(pipe.scheduler.config		,	skip_prk_steps=_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

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

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    int							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

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

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

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    List[str]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

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

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    str							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

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

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

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

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    List[str]							)    ->     Dict:
         '''simple docstring'''

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



      @property
      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =ort.SessionOptions()
         UpperCAmelCase_           =False
         return options



      def 						lowerCAmelCase__     (						self:    Any							)    ->     List[Any]:
         '''simple docstring'''

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

         UpperCAmelCase_           ="A fantasy landscape, trending on artstation"

         UpperCAmelCase_           =np.random.RandomState(0							)
         UpperCAmelCase_           =pipe(
             prompt=_lowerCAmelCase		,	image=_lowerCAmelCase		,	strength=0.75		,	guidance_scale=7.5		,	num_inference_steps=10		,	generator=_lowerCAmelCase		,	output_type="np"		,	)
         UpperCAmelCase_           =output.images
         UpperCAmelCase_           =images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    str							)    ->     Union[str, Any]:
         '''simple docstring'''

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

         UpperCAmelCase_           ="A fantasy landscape, trending on artstation"

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

         assert images.shape == (1, 512, 768, 3)
         UpperCAmelCase_           =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 os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
    import torch

if is_soundfile_availble():
    import soundfile as sf

if is_vision_available():
    from PIL import Image






def 				_UpperCAmelCase			(    A=""     ):




   '''simple docstring'''

   UpperCAmelCase__						=tempfile.mkdtemp()
   return os.path.join(A				,							str(uuid.uuida()     ) + suffix     )

@require_soundfile
@require_torch
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=AgentAudio(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

    # Ensure that the tensor and the agent_type's tensor are the same
    self.assertTrue(torch.allclose(A_,		agent_type.to_raw(),		atol=1E-4		)		)

    del agent_type

    # Ensure the path remains even after the object deletion
    self.assertTrue(os.path.exists(A_		)		)

    # Ensure that the file contains the same value as the original tensor
    UpperCAmelCase__			,							UpperCAmelCase__						=sf.read(A_		)
    self.assertTrue(torch.allclose(A_,		torch.tensor(A_		),		atol=1E-4		)		)






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=torch.rand(12,		dtype=torch.floataa		) - 0.5
    UpperCAmelCase__						=get_new_path(suffix=".wav"		)
    sf.write(A_,		A_,		1_6000		)

    UpperCAmelCase__						=AgentAudio(A_		)

    self.assertTrue(torch.allclose(A_,		agent_type.to_raw(),		atol=1E-4		)		)
    self.assertEqual(agent_type.to_string(),		A_		)

@require_vision
@require_torch
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=torch.randint(0,		256,		(64, 64, 3)		)
    UpperCAmelCase__						=AgentImage(A_		)
    UpperCAmelCase__						=str(agent_type.to_string()		)

    # Ensure that the tensor and the agent_type's tensor are the same
    self.assertTrue(torch.allclose(A_,		agent_type._tensor,		atol=1E-4		)		)

    self.assertIsInstance(agent_type.to_raw(),		Image.Image		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    self.assertTrue(path.samefile(agent_type.to_string()		)		)
    self.assertTrue(image == agent_type.to_raw()		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)






 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"
    UpperCAmelCase__						=Image.open(A_		)
    UpperCAmelCase__						=AgentImage(A_		)

    self.assertFalse(path.samefile(agent_type.to_string()		)		)
    self.assertTrue(image == agent_type.to_raw()		)

    # Ensure the path remains even after the object deletion
    del agent_type
    self.assertTrue(os.path.exists(A_		)		)

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						="Hey!"
    UpperCAmelCase__						=AgentText(A_		)

    self.assertEqual(A_,		agent_type.to_string()		)
    self.assertEqual(A_,		agent_type.to_raw()		)
    self.assertEqual(A_,		A_		)



