




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_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE=0      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						np.random.RandomState(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    '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 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 128, 128, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				SCREAMING_SNAKE_CASE_								=						PNDMScheduler.from_config(pipe.scheduler.config      ,     skip_prk_steps=SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 128, 128, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				SCREAMING_SNAKE_CASE_								=						LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 128, 128, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				SCREAMING_SNAKE_CASE_								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 128, 128, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				SCREAMING_SNAKE_CASE_								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 128, 128, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				SCREAMING_SNAKE_CASE_								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 128, 128, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs()
				SCREAMING_SNAKE_CASE_								=						3 * [inputs['prompt']]

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

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs()
				SCREAMING_SNAKE_CASE_								=						3 * [inputs.pop('prompt'      )]

				SCREAMING_SNAKE_CASE_								=						pipe.tokenizer(
				    SCREAMING_SNAKE_CASE      ,     padding='max_length'      ,     max_length=pipe.tokenizer.model_max_length      ,     truncation=SCREAMING_SNAKE_CASE      ,     return_tensors='np'      ,     )
				SCREAMING_SNAKE_CASE_								=						text_inputs['input_ids']

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

				SCREAMING_SNAKE_CASE_								=						prompt_embeds

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

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





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,     provider='CPUExecutionProvider'      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

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

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs()
				SCREAMING_SNAKE_CASE_								=						3 * [inputs.pop('prompt'      )]

				SCREAMING_SNAKE_CASE_								=						[]
				for p in [prompt, negative_prompt]:
							SCREAMING_SNAKE_CASE_								=						pipe.tokenizer(
							    SCREAMING_SNAKE_CASE      ,     padding='max_length'      ,     max_length=pipe.tokenizer.model_max_length      ,     truncation=SCREAMING_SNAKE_CASE      ,     return_tensors='np'      ,     )
							SCREAMING_SNAKE_CASE_								=						text_inputs['input_ids']

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

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						embeds

				# forward
				SCREAMING_SNAKE_CASE_								=						pipe(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



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





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



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





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




				"""simple docstring"""



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

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

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

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						DDIMScheduler.from_pretrained(
				    'runwayml/stable-diffusion-v1-5'      ,     subfolder='scheduler'      ,     revision='onnx'      )
				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(
				    'runwayml/stable-diffusion-v1-5'      ,     revision='onnx'      ,     scheduler=SCREAMING_SNAKE_CASE      ,     safety_checker=SCREAMING_SNAKE_CASE      ,     feature_extractor=SCREAMING_SNAKE_CASE      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						LMSDiscreteScheduler.from_pretrained(
				    'runwayml/stable-diffusion-v1-5'      ,     subfolder='scheduler'      ,     revision='onnx'      )
				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(
				    'runwayml/stable-diffusion-v1-5'      ,     revision='onnx'      ,     scheduler=SCREAMING_SNAKE_CASE      ,     safety_checker=SCREAMING_SNAKE_CASE      ,     feature_extractor=SCREAMING_SNAKE_CASE      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

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

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE_								=						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 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0

				def test_callback_fn(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ) -> None:
							SCREAMING_SNAKE_CASE_								=						True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 0:
										assert latents.shape == (1, 4, 64, 64)
										SCREAMING_SNAKE_CASE_								=						latents[0, -3:, -3:, -1]
										SCREAMING_SNAKE_CASE_								=						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)
										SCREAMING_SNAKE_CASE_								=						latents[0, -3:, -3:, -1]
										SCREAMING_SNAKE_CASE_								=						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

				SCREAMING_SNAKE_CASE_								=						False

				SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(
				    'runwayml/stable-diffusion-v1-5'      ,     revision='onnx'      ,     safety_checker=SCREAMING_SNAKE_CASE      ,     feature_extractor=SCREAMING_SNAKE_CASE      ,     provider=self.gpu_provider      ,     sess_options=self.gpu_options      ,     )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						'Andromeda galaxy in a bottle'

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





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



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

				SCREAMING_SNAKE_CASE_								=						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(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE      )

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






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
