





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



      def 						lowerCAmelCase__     (						self:    Optional[Any]		,	_lowerCAmelCase:    int=0							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =np.random.RandomState(_lowerCAmelCase							)
         UpperCAmelCase_           ={
             "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:    Optional[int]							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =OnnxStableDiffusionPipeline.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]

         assert image.shape == (1, 128, 128, 3)
         UpperCAmelCase_           =np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]							)

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



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

         UpperCAmelCase_           =OnnxStableDiffusionPipeline.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_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]							)

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



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

         UpperCAmelCase_           =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,	provider="CPUExecutionProvider"							)
         UpperCAmelCase_           =LMSDiscreteScheduler.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_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]							)

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



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

         UpperCAmelCase_           =OnnxStableDiffusionPipeline.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_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]							)

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



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

         UpperCAmelCase_           =OnnxStableDiffusionPipeline.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_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]							)

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



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

         UpperCAmelCase_           =OnnxStableDiffusionPipeline.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([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]							)

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



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

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

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

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

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

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

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

         UpperCAmelCase_           =prompt_embeds

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

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






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

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

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

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

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

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

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

         UpperCAmelCase_    ,							UpperCAmelCase_           =embeds

         # forward
         UpperCAmelCase_           =pipe(**_lowerCAmelCase							)
         UpperCAmelCase_           =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:    Optional[Any]							)    ->     Tuple:
         '''simple docstring'''

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



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

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



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

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

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

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

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]							)

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



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

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

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

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]							)

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



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

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

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

         assert image.shape == (1, 512, 512, 3)
         UpperCAmelCase_           =np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]							)

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



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =0

         def test_callback_fn(_lowerCAmelCase:    int		,	_lowerCAmelCase:    int		,	_lowerCAmelCase:    np.ndarray							) -> None:
            UpperCAmelCase_           =True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 0:
               assert latents.shape == (1, 4, 64, 64)
               UpperCAmelCase_           =latents[0, -3:, -3:, -1]
               UpperCAmelCase_           =np.array(
                   [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]							)

               assert np.abs(latents_slice.flatten() - expected_slice							).max() < 1e-3
            elif step == 5:
               assert latents.shape == (1, 4, 64, 64)
               UpperCAmelCase_           =latents[0, -3:, -3:, -1]
               UpperCAmelCase_           =np.array(
                   [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]							)

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

         UpperCAmelCase_           =False

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

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






      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     Tuple:
         '''simple docstring'''

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

         UpperCAmelCase_           =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(_lowerCAmelCase							)
            UpperCAmelCase_           =OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase							)

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     '''gpt_neox_japanese'''

					def __init__(						self       :  Union[str, Any] ,       __UpperCamelCase       :  str=3_2_0_0_0 ,       __UpperCamelCase       :  List[Any]=2_5_6_0 ,       __UpperCamelCase       :  Any=3_2 ,       __UpperCamelCase       :  List[str]=3_2 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  Dict="gelu" ,       __UpperCamelCase       :  List[Any]=1.00 ,       __UpperCamelCase       :  Any=1_0_0_0_0 ,       __UpperCamelCase       :  Optional[Any]=2_0_4_8 ,       __UpperCamelCase       :  Tuple=0.02 ,       __UpperCamelCase       :  List[str]=1E-5 ,       __UpperCamelCase       :  str=True ,       __UpperCamelCase       :  str=3_1_9_9_6 ,       __UpperCamelCase       :  int=3_1_9_9_9 ,       __UpperCamelCase       :  Optional[Any]=0.1 ,       __UpperCamelCase       :  Tuple=0.0 ,       **__UpperCamelCase       :  List[str] ,       ):
												super().__init__(bos_token_id=__UpperCamelCase ,       eos_token_id=__UpperCamelCase ,       **__UpperCamelCase )
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_multiple_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       rotary_pct
												lowerCamelCase_					=       rotary_emb_base
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       layer_norm_eps
												lowerCamelCase_					=       use_cache
												lowerCamelCase_					=       attention_dropout
												lowerCamelCase_					=       hidden_dropout





