






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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






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



					def 	_lowercase					( self					:  Dict							,		__A					:  Optional[Any]=0	):
						snake_case__			:				Union[str, Any]       =  np.random.RandomState(__A	)
						snake_case__			:				List[str]       =  {
						    "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 	_lowercase					( self					:  Any	):
						snake_case__			:				Union[str, Any]       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Union[str, Any]       =  self.get_dummy_inputs()
						snake_case__			:				int       =  pipe(**__A	).images
						snake_case__			:				str       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__			:				Optional[int]       =  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 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				List[Any]       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						snake_case__			:				List[Any]       =  PNDMScheduler.from_config(pipe.scheduler.config							,		skip_prk_steps=__A	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				List[Any]       =  self.get_dummy_inputs()
						snake_case__			:				str       =  pipe(**__A	).images
						snake_case__			:				Dict       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__			:				List[Any]       =  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 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Union[str, Any]       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						snake_case__			:				str       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Dict       =  self.get_dummy_inputs()
						snake_case__			:				Any       =  pipe(**__A	).images
						snake_case__			:				List[Any]       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__			:				Tuple       =  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 	_lowercase					( self					:  List[str]	):
						snake_case__			:				int       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						snake_case__			:				Any       =  EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				int       =  self.get_dummy_inputs()
						snake_case__			:				Tuple       =  pipe(**__A	).images
						snake_case__			:				Optional[int]       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__			:				Any       =  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 	_lowercase					( self					:  Dict	):
						snake_case__			:				Any       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						snake_case__			:				List[str]       =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=__A	)

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

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__			:				List[str]       =  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 	_lowercase					( self					:  int	):
						snake_case__			:				int       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						snake_case__			:				str       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Tuple       =  self.get_dummy_inputs()
						snake_case__			:				List[str]       =  pipe(**__A	).images
						snake_case__			:				Tuple       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_2_8, 1_2_8, 3)
						snake_case__			:				Union[str, Any]       =  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 	_lowercase					( self					:  Any	):
						snake_case__			:				str       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Union[str, Any]       =  self.get_dummy_inputs()
						snake_case__			:				List[Any]       =  3 * [inputs["prompt"]]

						# forward
						snake_case__			:				Tuple       =  pipe(**__A	)
						snake_case__			:				str       =  output.images[0, -3:, -3:, -1]

						snake_case__			:				Optional[Any]       =  self.get_dummy_inputs()
						snake_case__			:				Any       =  3 * [inputs.pop("prompt"	)]

						snake_case__			:				List[str]       =  pipe.tokenizer(
						    __A							,		padding="max_length"							,		max_length=pipe.tokenizer.model_max_length							,		truncation=__A							,		return_tensors="np"							,		)
						snake_case__			:				str       =  text_inputs["input_ids"]

						snake_case__			:				List[Any]       =  pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]

						snake_case__			:				Union[str, Any]       =  prompt_embeds

						# forward
						snake_case__			:				Any       =  pipe(**__A	)
						snake_case__			:				List[str]       =  output.images[0, -3:, -3:, -1]

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






					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				Tuple       =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,		provider="CPUExecutionProvider"	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Optional[int]       =  self.get_dummy_inputs()
						snake_case__			:				List[str]       =  3 * ["this is a negative prompt"]
						snake_case__			:				List[Any]       =  negative_prompt
						snake_case__			:				Union[str, Any]       =  3 * [inputs["prompt"]]

						# forward
						snake_case__			:				Optional[int]       =  pipe(**__A	)
						snake_case__			:				List[str]       =  output.images[0, -3:, -3:, -1]

						snake_case__			:				str       =  self.get_dummy_inputs()
						snake_case__			:				str       =  3 * [inputs.pop("prompt"	)]

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

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

						snake_case__			:				str       =  embeds

						# forward
						snake_case__			:				Any       =  pipe(**__A	)
						snake_case__			:				str       =  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 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






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



					@property
					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				int       =  ort.SessionOptions()
						snake_case__			:				Union[str, Any]       =  False
						return options



					def 	_lowercase					( self					:  Tuple	):
						# using the PNDM scheduler by default
						snake_case__			:				Tuple       =  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	)

						snake_case__			:				List[Any]       =  "A painting of a squirrel eating a burger"
						np.random.seed(0	)
						snake_case__			:				List[str]       =  sd_pipe([prompt]							,		guidance_scale=6.0							,		num_inference_steps=1_0							,		output_type="np"	)
						snake_case__			:				str       =  output.images

						snake_case__			:				List[Any]       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				str       =  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 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Tuple       =  DDIMScheduler.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"							,		subfolder="scheduler"							,		revision="onnx"	)
						snake_case__			:				Union[str, Any]       =  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	)

						snake_case__			:				List[str]       =  "open neural network exchange"
						snake_case__			:				int       =  np.random.RandomState(0	)
						snake_case__			:				str       =  sd_pipe([prompt]							,		guidance_scale=7.5							,		num_inference_steps=1_0							,		generator=__A							,		output_type="np"	)
						snake_case__			:				Optional[int]       =  output.images
						snake_case__			:				Dict       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Optional[int]       =  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 	_lowercase					( self					:  str	):
						snake_case__			:				Dict       =  LMSDiscreteScheduler.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"							,		subfolder="scheduler"							,		revision="onnx"	)
						snake_case__			:				Any       =  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	)

						snake_case__			:				Optional[Any]       =  "open neural network exchange"
						snake_case__			:				int       =  np.random.RandomState(0	)
						snake_case__			:				List[str]       =  sd_pipe([prompt]							,		guidance_scale=7.5							,		num_inference_steps=1_0							,		generator=__A							,		output_type="np"	)
						snake_case__			:				int       =  output.images
						snake_case__			:				List[str]       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Dict       =  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 	_lowercase					( self					:  List[str]	):
						snake_case__			:				Union[str, Any]       =  0

						def test_callback_fn(__A					:  int							,		__A					:  int							,		__A					:  np.ndarray	) -> None:
							snake_case__			:				List[Any]       =  True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 0:
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__			:				List[Any]       =  latents[0, -3:, -3:, -1]
								snake_case__			:				int       =  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, 6_4, 6_4)
								snake_case__			:				Union[str, Any]       =  latents[0, -3:, -3:, -1]
								snake_case__			:				List[str]       =  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

						snake_case__			:				Dict       =  False

						snake_case__			:				Union[str, Any]       =  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	)

						snake_case__			:				str       =  "Andromeda galaxy in a bottle"

						snake_case__			:				Union[str, Any]       =  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 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Dict       =  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

						snake_case__			:				Union[str, Any]       =  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	)
							snake_case__			:				List[str]       =  OnnxStableDiffusionPipeline.from_pretrained(__A	)

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











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :      Optional[int]										=				logging.get_logger(__name__)

__lowerCamelCase :      str										=				{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"roberta-prelayernorm"



					def __init__( self					:  Tuple							,		__A					:  Any=5_0_2_6_5							,		__A					:  Optional[int]=7_6_8							,		__A					:  Dict=1_2							,		__A					:  Union[str, Any]=1_2							,		__A					:  List[Any]=3_0_7_2							,		__A					:  Optional[Any]="gelu"							,		__A					:  Optional[int]=0.1							,		__A					:  Tuple=0.1							,		__A					:  Optional[Any]=5_1_2							,		__A					:  List[str]=2							,		__A					:  Optional[int]=0.0_2							,		__A					:  Tuple=1e-1_2							,		__A					:  Any=1							,		__A					:  str=0							,		__A					:  int=2							,		__A					:  List[str]="absolute"							,		__A					:  Optional[Any]=True							,		__A					:  List[Any]=None							,		**__A					:  Optional[Any]							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Optional[Any]       =  hidden_size
						snake_case__			:				List[Any]       =  num_hidden_layers
						snake_case__			:				List[Any]       =  num_attention_heads
						snake_case__			:				Dict       =  hidden_act
						snake_case__			:				Union[str, Any]       =  intermediate_size
						snake_case__			:				List[Any]       =  hidden_dropout_prob
						snake_case__			:				Any       =  attention_probs_dropout_prob
						snake_case__			:				int       =  max_position_embeddings
						snake_case__			:				Tuple       =  type_vocab_size
						snake_case__			:				Optional[int]       =  initializer_range
						snake_case__			:				int       =  layer_norm_eps
						snake_case__			:				Dict       =  position_embedding_type
						snake_case__			:				int       =  use_cache
						snake_case__			:				Dict       =  classifier_dropout



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  Optional[int]	):
						if self.task == "multiple-choice":
							snake_case__			:				List[str]       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Tuple       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



