





"""simple docstring"""






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__UpperCAmelCase						=			logging.get_logger(__name__)

__UpperCAmelCase						=			{
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	List[Any]				     =				"marian"
				__lowerCamelCase				:	Optional[Any]				     =				["past_key_values"]
				__lowerCamelCase				:	int				     =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
				def __init__(		self    :			int	, a_    :			List[Any]=5_81_01	, a_    :			Optional[Any]=None	, a_    :			int=10_24	, a_    :			str=12	, a_    :			List[Any]=40_96	, a_    :			Tuple=16	, a_    :			Tuple=12	, a_    :			Tuple=40_96	, a_    :			str=16	, a_    :			Tuple=0.0	, a_    :			Any=0.0	, a_    :			str=True	, a_    :			Dict=True	, a_    :			List[Any]="gelu"	, a_    :			List[str]=10_24	, a_    :			Any=0.1	, a_    :			Optional[Any]=0.0	, a_    :			Any=0.0	, a_    :			Union[str, Any]=0.02	, a_    :			int=5_81_00	, a_    :			int=False	, a_    :			Dict=5_81_00	, a_    :			Optional[Any]=0	, a_    :			List[Any]=0	, a_    :			Optional[Any]=True	, **a_    :			Dict	, )	->	List[Any]:



							'''simple docstring'''



							a__  :	Optional[int]		 =			vocab_size
							a__  :	Optional[Any]		 =			decoder_vocab_size or vocab_size
							a__  :	List[Any]		 =			max_position_embeddings
							a__  :	Any		 =			d_model
							a__  :	Optional[int]		 =			encoder_ffn_dim
							a__  :	Optional[int]		 =			encoder_layers
							a__  :	int		 =			encoder_attention_heads
							a__  :	Any		 =			decoder_ffn_dim
							a__  :	str		 =			decoder_layers
							a__  :	List[Any]		 =			decoder_attention_heads
							a__  :	str		 =			dropout
							a__  :	Union[str, Any]		 =			attention_dropout
							a__  :	str		 =			activation_dropout
							a__  :	str		 =			activation_function
							a__  :	str		 =			init_std
							a__  :	Tuple		 =			encoder_layerdrop
							a__  :	Tuple		 =			decoder_layerdrop
							a__  :	Optional[Any]		 =			use_cache
							a__  :	Dict		 =			encoder_layers
							a__  :	Tuple		 =			scale_embedding  # scale factor will be sqrt(d_model) if True
							a__  :	str		 =			share_encoder_decoder_embeddings
							super().__init__(
							    pad_token_id=a_	, eos_token_id=a_	, is_encoder_decoder=a_	, decoder_start_token_id=a_	, forced_eos_token_id=a_	, **a_	, )



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Mapping[str, Mapping[int, str]]:



							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
										a__  :	Tuple		 =			OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ]    )

										if self.use_past:
													a__  :	Dict		 =			{0: "batch"}
													a__  :	Tuple		 =			{0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
													a__  :	Union[str, Any]		 =			{0: "batch", 1: "decoder_sequence"}
													a__  :	Union[str, Any]		 =			{0: "batch", 1: "decoder_sequence"}

										if self.use_past:
													self.fill_with_past_key_values_(a_	, direction="inputs"    )
							elif self.task == "causal-lm":
										# TODO: figure this case out.
										a__  :	Dict		 =			OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ]    )
										if self.use_past:
													a__ ,	a__  :	Optional[int]		 =			self.num_layers
													for i in range(a_    ):
																a__  :	List[str]		 =			{0: "batch", 2: "past_sequence + sequence"}
																a__  :	List[Any]		 =			{0: "batch", 2: "past_sequence + sequence"}
							else:
										a__  :	Optional[Any]		 =			OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
										        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
										    ]    )

							return common_inputs
				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def       UpperCAmelCase      (		self    :			str    )	->	Mapping[str, Mapping[int, str]]:



							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
										a__  :	List[Any]		 =			super().outputs
							else:
										a__  :	Union[str, Any]		 =			super(a_	, self    ).outputs
										if self.use_past:
													a__ ,	a__  :	int		 =			self.num_layers
													for i in range(a_    ):
																a__  :	str		 =			{0: "batch", 2: "past_sequence + sequence"}
																a__  :	Dict		 =			{0: "batch", 2: "past_sequence + sequence"}
							return common_outputs
				def       UpperCAmelCase      (		self    :			Any	, a_    :			PreTrainedTokenizer	, a_    :			int = -1	, a_    :			int = -1	, a_    :			bool = False	, a_    :			Optional[TensorType] = None	, )	->	Mapping[str, Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			self._generate_dummy_inputs_for_encoder_and_decoder(
							    a_	, a_	, a_	, a_	, a_    )

							# Generate decoder inputs
							a__  :	Optional[Any]		 =			seq_length if not self.use_past else 1
							a__  :	Union[str, Any]		 =			self._generate_dummy_inputs_for_encoder_and_decoder(
							    a_	, a_	, a_	, a_	, a_    )
							a__  :	Optional[int]		 =			{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
							a__  :	List[Any]		 =			dict(**a_	, **a_    )

							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
										else:
													import torch
										a__ ,	a__  :	Dict		 =			common_inputs["input_ids"].shape
										a__  :	int		 =			common_inputs["decoder_input_ids"].shape[1]
										a__ ,	a__  :	Optional[int]		 =			self.num_attention_heads
										a__  :	Any		 =			(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										a__  :	Union[str, Any]		 =			decoder_seq_length + 3
										a__  :	List[Any]		 =			(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										a__  :	str		 =			torch.cat(
										    [common_inputs["decoder_attention_mask"], torch.ones(a_	, a_    )]	, dim=1    )

										a__  :	Optional[int]		 =			[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										a__ ,	a__  :	Any		 =			self.num_layers
										a__  :	Dict		 =			min(a_	, a_    )
										a__  :	str		 =			max(a_	, a_    ) - min_num_layers
										a__  :	Any		 =			"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

										for _ in range(a_    ):
													common_inputs["past_key_values"].append(
													    (
													        torch.zeros(a_    ),
													        torch.zeros(a_    ),
													        torch.zeros(a_    ),
													        torch.zeros(a_    ),
													    )    )
										# TODO: test this.
										a__  :	Tuple		 =			encoder_shape if remaining_side_name == "encoder" else decoder_shape
										for _ in range(a_	, a_    ):
													common_inputs["past_key_values"].append((torch.zeros(a_    ), torch.zeros(a_    ))    )
							return common_inputs
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			PreTrainedTokenizer	, a_    :			int = -1	, a_    :			int = -1	, a_    :			bool = False	, a_    :			Optional[TensorType] = None	, )	->	Mapping[str, Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self._generate_dummy_inputs_for_encoder_and_decoder(
							    a_	, a_	, a_	, a_	, a_    )

							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
										else:
													import torch
										a__ ,	a__  :	Any		 =			common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										a__  :	Optional[int]		 =			seqlen + 2
										a__ ,	a__  :	Tuple		 =			self.num_layers
										a__ ,	a__  :	Union[str, Any]		 =			self.num_attention_heads
										a__  :	List[str]		 =			(
										    batch,
										    num_encoder_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)

										a__  :	Any		 =			common_inputs["attention_mask"].dtype
										a__  :	Tuple		 =			torch.cat(
										    [common_inputs["attention_mask"], torch.ones(a_	, a_	, dtype=a_    )]	, dim=1    )
										a__  :	Union[str, Any]		 =			[
										    (torch.zeros(a_    ), torch.zeros(a_    )) for _ in range(a_    )
										]
							return common_inputs
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			PreTrainedTokenizer	, a_    :			int = -1	, a_    :			int = -1	, a_    :			bool = False	, a_    :			Optional[TensorType] = None	, )	->	Mapping[str, Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			compute_effective_axis_dimension(
							    a_	, fixed_dimension=OnnxConfig.default_fixed_batch	, num_token_to_add=0    )

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							a__  :	List[str]		 =			tokenizer.num_special_tokens_to_add(a_    )
							a__  :	int		 =			compute_effective_axis_dimension(
							    a_	, fixed_dimension=OnnxConfig.default_fixed_sequence	, num_token_to_add=a_    )

							# Generate dummy inputs according to compute batch and sequence
							a__  :	Dict		 =			[" ".join([tokenizer.unk_token]    ) * seq_length] * batch_size
							a__  :	List[Any]		 =			dict(tokenizer(a_	, return_tensors=a_    )    )
							return common_inputs
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			PreTrainedTokenizer	, a_    :			int = -1	, a_    :			int = -1	, a_    :			bool = False	, a_    :			Optional[TensorType] = None	, )	->	Mapping[str, Any]:



							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
										a__  :	Any		 =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    a_	, batch_size=a_	, seq_length=a_	, is_pair=a_	, framework=a_    )

							else:
										a__  :	Optional[Any]		 =			self._generate_dummy_inputs_for_causal_lm(
										    a_	, batch_size=a_	, seq_length=a_	, is_pair=a_	, framework=a_    )

							return common_inputs
				def       UpperCAmelCase      (		self    :			Any	, a_    :			Any	, a_    :			Any	, a_    :			Optional[Any]	, a_    :			Dict    )	->	Union[str, Any]:



							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
										a__  :	Tuple		 =			super()._flatten_past_key_values_(a_	, a_	, a_	, a_    )
							else:
										a__  :	Any		 =			super(a_	, self    )._flatten_past_key_values_(
										    a_	, a_	, a_	, a_    )






				@property
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	float:



							'''simple docstring'''



							return 1E-4






"""simple docstring"""






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 					__UpperCAmelCase							(						_UpperCamelCase						,  unittest.TestCase  ):
				__lowerCamelCase				:	List[Any]				     =				"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
				def       UpperCAmelCase      (		self    :			str	, a_    :			Optional[Any]=0    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			np.random.RandomState(a_    )
							a__  :	List[Any]		 =			{
							    "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       UpperCAmelCase      (		self    :			Dict    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	Union[str, Any]		 =			pipe(**a_    ).images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	List[str]		 =			PNDMScheduler.from_config(pipe.scheduler.config	, skip_prk_steps=a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	List[Any]		 =			pipe(**a_    ).images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Tuple		 =			LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[Any]		 =			self.get_dummy_inputs()
							a__  :	Optional[Any]		 =			pipe(**a_    ).images
							a__  :	int		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Dict		 =			np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

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

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	str		 =			np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs()
							a__  :	List[str]		 =			pipe(**a_    ).images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Any:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	str		 =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

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

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			str    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs()
							a__  :	Any		 =			3 * [inputs["prompt"]]

							# forward
							a__  :	Union[str, Any]		 =			pipe(**a_    )
							a__  :	int		 =			output.images[0, -3:, -3:, -1]

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

							a__  :	Optional[Any]		 =			pipe.tokenizer(
							    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
							a__  :	List[str]		 =			text_inputs["input_ids"]

							a__  :	int		 =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

							a__  :	List[Any]		 =			prompt_embeds

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

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






				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Tuple		 =			self.get_dummy_inputs()
							a__  :	Dict		 =			3 * ["this is a negative prompt"]
							a__  :	Optional[Any]		 =			negative_prompt
							a__  :	Any		 =			3 * [inputs["prompt"]]

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

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

							a__  :	List[Any]		 =			[]
							for p in [prompt, negative_prompt]:
										a__  :	int		 =			pipe.tokenizer(
										    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
										a__  :	Any		 =			text_inputs["input_ids"]

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

							a__ ,	a__  :	Union[str, Any]		 =			embeds

							# forward
							a__  :	Dict		 =			pipe(**a_    )
							a__  :	Any		 =			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 					__UpperCAmelCase							(						unittest.TestCase  ):
				@property
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Optional[int]:



							'''simple docstring'''



							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Tuple:



							'''simple docstring'''



							a__  :	List[str]		 =			ort.SessionOptions()
							a__  :	List[str]		 =			False
							return options
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



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

							a__  :	Optional[int]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	str		 =			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Any		 =			DDIMScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	str		 =			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__  :	str		 =			"open neural network exchange"
							a__  :	Tuple		 =			np.random.RandomState(0    )
							a__  :	Dict		 =			sd_pipe([prompt]	, guidance_scale=7.5	, num_inference_steps=10	, generator=a_	, output_type="np"    )
							a__  :	Dict		 =			output.images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[Any]		 =			LMSDiscreteScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	List[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_    )

							a__  :	Any		 =			"open neural network exchange"
							a__  :	Optional[Any]		 =			np.random.RandomState(0    )
							a__  :	Optional[int]		 =			sd_pipe([prompt]	, guidance_scale=7.5	, num_inference_steps=10	, generator=a_	, output_type="np"    )
							a__  :	int		 =			output.images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			0

							def test_callback_fn(a_    :			int	, a_    :			int	, a_    :			np.ndarray    ) -> None:
										a__  :	Optional[int]		 =			True
										nonlocal number_of_steps
										number_of_steps += 1
										if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													a__  :	Any		 =			latents[0, -3:, -3:, -1]
													a__  :	Union[str, Any]		 =			np.array(
													    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]    )

													assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
										elif step == 5:
													assert latents.shape == (1, 4, 64, 64)
													a__  :	Union[str, Any]		 =			latents[0, -3:, -3:, -1]
													a__  :	Optional[int]		 =			np.array(
													    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]    )

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

							a__  :	Tuple		 =			False

							a__  :	Optional[int]		 =			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__  :	List[Any]		 =			"Andromeda galaxy in a bottle"

							a__  :	str		 =			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       UpperCAmelCase      (		self    :			Tuple    )	->	List[str]:



							'''simple docstring'''



							a__  :	Optional[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	, )
							assert isinstance(a_	, a_    )
							assert pipe.safety_checker is None

							a__  :	Tuple		 =			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__  :	Optional[int]		 =			OnnxStableDiffusionPipeline.from_pretrained(a_    )

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