




import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, 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				):
     a		:      int		 =     """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







     def       lowerCAmelCase_    (     self  ,		A=0			)  ->   List[Any]:



          '''simple docstring'''



          a     =				np.random.RandomState(A			)
          a     =				{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.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]

          assert image.shape == (1, 128, 128, 3)
          a     =				np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.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, 128, 128, 3)
          a     =				np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          a     =				LMSDiscreteScheduler.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, 128, 128, 3)
          a     =				np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.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, 128, 128, 3)
          a     =				np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.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, 128, 128, 3)
          a     =				np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.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, 128, 128, 3)
          a     =				np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          pipe.set_progress_bar_config(disable=A			)

          a     =				self.get_dummy_inputs()
          a     =				3 * [inputs["prompt"]]

          # forward
          a     =				pipe(**A			)
          a     =				output.images[0, -3:, -3:, -1]

          a     =				self.get_dummy_inputs()
          a     =				3 * [inputs.pop("prompt"			)]

          a     =				pipe.tokenizer(
              A  ,		padding="max_length"  ,		max_length=pipe.tokenizer.model_max_length  ,		truncation=A  ,		return_tensors="np"  ,		)
          a     =				text_inputs["input_ids"]

          a     =				pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]

          a     =				prompt_embeds

          # forward
          a     =				pipe(**A			)
          a     =				output.images[0, -3:, -3:, -1]

          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint  ,		provider="CPUExecutionProvider"			)
          pipe.set_progress_bar_config(disable=A			)

          a     =				self.get_dummy_inputs()
          a     =				3 * ["this is a negative prompt"]
          a     =				negative_prompt
          a     =				3 * [inputs["prompt"]]

          # forward
          a     =				pipe(**A			)
          a     =				output.images[0, -3:, -3:, -1]

          a     =				self.get_dummy_inputs()
          a     =				3 * [inputs.pop("prompt"			)]

          a     =				[]
          for p in [prompt, negative_prompt]:
               a     =				pipe.tokenizer(
                   A  ,		padding="max_length"  ,		max_length=pipe.tokenizer.model_max_length  ,		truncation=A  ,		return_tensors="np"  ,		)
               a     =				text_inputs["input_ids"]

               embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]			)

          a  ,				a     =				embeds

          # forward
          a     =				pipe(**A			)
          a     =				output.images[0, -3:, -3:, -1]

          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4







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







     @property
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



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







     @property
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.from_pretrained(
              "CompVis/stable-diffusion-v1-4"  ,		revision="onnx"  ,		safety_checker=A  ,		feature_extractor=A  ,		provider=self.gpu_provider  ,		sess_options=self.gpu_options  ,		)
          sd_pipe.set_progress_bar_config(disable=A			)

          a     =				"A painting of a squirrel eating a burger"
          np.random.seed(0			)
          a     =				sd_pipe([prompt]  ,		guidance_scale=6.0  ,		num_inference_steps=10  ,		output_type="np"			)
          a     =				output.images

          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          a     =				np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				DDIMScheduler.from_pretrained(
              "runwayml/stable-diffusion-v1-5"  ,		subfolder="scheduler"  ,		revision="onnx"			)
          a     =				OnnxStableDiffusionPipeline.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  ,		)
          sd_pipe.set_progress_bar_config(disable=A			)

          a     =				"open neural network exchange"
          a     =				np.random.RandomState(0			)
          a     =				sd_pipe([prompt]  ,		guidance_scale=7.5  ,		num_inference_steps=10  ,		generator=A  ,		output_type="np"			)
          a     =				output.images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          a     =				np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-v1-5"  ,		subfolder="scheduler"  ,		revision="onnx"			)
          a     =				OnnxStableDiffusionPipeline.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  ,		)
          sd_pipe.set_progress_bar_config(disable=A			)

          a     =				"open neural network exchange"
          a     =				np.random.RandomState(0			)
          a     =				sd_pipe([prompt]  ,		guidance_scale=7.5  ,		num_inference_steps=10  ,		generator=A  ,		output_type="np"			)
          a     =				output.images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          a     =				np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				0

          def test_callback_fn(A  ,		A  ,		A			) -> None:
               a     =				True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 0:
                    assert latents.shape == (1, 4, 64, 64)
                    a     =				latents[0, -3:, -3:, -1]
                    a     =				np.array(
                        [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]			)

                    assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1e-3
               elif step == 5:
                    assert latents.shape == (1, 4, 64, 64)
                    a     =				latents[0, -3:, -3:, -1]
                    a     =				np.array(
                        [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]			)

                    assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1e-3

          a     =				False

          a     =				OnnxStableDiffusionPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5"  ,		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     =				"Andromeda galaxy in a bottle"

          a     =				np.random.RandomState(0			)
          pipe(
              prompt=A  ,		num_inference_steps=5  ,		guidance_scale=7.5  ,		generator=A  ,		callback=A  ,		callback_steps=1  ,		)
          assert test_callback_fn.has_been_called
          assert number_of_steps == 6







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				OnnxStableDiffusionPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5"  ,		revision="onnx"  ,		safety_checker=A  ,		feature_extractor=A  ,		provider=self.gpu_provider  ,		sess_options=self.gpu_options  ,		)
          assert isinstance(A  ,		A			)
          assert pipe.safety_checker is None

          a     =				pipe("example prompt"  ,		num_inference_steps=2			).images[0]
          assert image is not None

          # check that there's no error when saving a pipeline with one of the models being None
          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(A			)
               a     =				OnnxStableDiffusionPipeline.from_pretrained(A			)

          # sanity check that the pipeline still works
          assert pipe.safety_checker is None
          a     =				pipe("example prompt"  ,		num_inference_steps=2			).images[0]
          assert image is not None







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

