
"""simple docstring"""


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


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    str      =    '''gptsan-japanese'''
						_lowerCamelCase:    Dict      =    [
						    '''past_key_values''',
						]
						_lowerCamelCase:    Dict      =    {
						    '''hidden_size''': '''d_model''',
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						}


						def __init__( self     :      Dict  ,A_     :      Any=3_6000  ,A_     :      Dict=1280  ,A_     :      Any=1024  ,A_     :      Optional[int]=8192  ,A_     :      int=4096  ,A_     :      Any=128  ,A_     :      Union[str, Any]=10  ,A_     :      str=0  ,A_     :      Union[str, Any]=16  ,A_     :      str=16  ,A_     :      str=128  ,A_     :      Any=0.0  ,A_     :      Tuple=1e-5  ,A_     :      int=False  ,A_     :      Optional[int]=0.0  ,A_     :      int="float32"  ,A_     :      List[Any]=False  ,A_     :      Union[str, Any]=False  ,A_     :      Optional[Any]=False  ,A_     :      str=0.0_02  ,A_     :      Optional[int]=False  ,A_     :      List[str]=True  ,A_     :      Optional[int]=3_5998  ,A_     :      Dict=3_5995  ,A_     :      str=3_5999  ,**A_     :      List[Any]  ,)     ->   Optional[int]:
							A						   =   vocab_size
							A						   =   max_position_embeddings
							A						   =   d_model
							A						   =   d_ff
							A						   =   d_ext
							A						   =   d_spout
							A						   =   num_switch_layers
							A						   =   num_ext_layers
							A						   =   num_switch_layers + num_ext_layers
							A						   =   num_heads
							A						   =   num_experts
							A						   =   expert_capacity
							A						   =   dropout_rate
							A						   =   layer_norm_epsilon
							A						   =   router_bias
							A						   =   router_jitter_noise
							A						   =   router_dtype
							A						   =   router_ignore_padding_tokens
							A						   =   output_hidden_states
							A						   =   output_attentions
							A						   =   initializer_factor
							A						   =   output_router_logits
							A						   =   use_cache

							super().__init__(
							    separator_token_id=A_  ,pad_token_id=A_  ,eos_token_id=A_  ,**A_  ,)






"""simple docstring"""


import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort







class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      List[str]=0      )     ->   Dict:
							A						   =   floats_tensor((1, 3, 128, 128)  ,rng=random.Random(A_      )      )
							A						   =   np.random.RandomState(A_      )
							A						   =   {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'strength': 0.75,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.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].flatten()

							assert image.shape == (1, 128, 128, 3)
							A						   =   np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]      )
							assert np.abs(image_slice - expected_slice      ).max() < 1e-1


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Union[str, Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.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_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,provider='CPUExecutionProvider'      )
							A						   =   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
							pipe.set_progress_bar_config(disable=A_      )

							# warmup pass to apply optimizations
							A						   =   pipe(**self.get_dummy_inputs()      )

							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_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.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_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.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_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]      )

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Optional[Any]:
							A						   =   OnnxStableDiffusionImgaImgPipeline.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([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]      )

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







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



						'''simple docstring'''


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   str:
							A						   =   ort.SessionOptions()
							A						   =   False
							return options


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Tuple:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							# using the PNDM scheduler by default
							A						   =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'  ,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						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=10  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Any:
							A						   =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'      )
							A						   =   init_image.resize((768, 512)      )
							A						   =   LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'  ,subfolder='scheduler'  ,revision='onnx'      )
							A						   =   OnnxStableDiffusionImgaImgPipeline.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  ,)
							pipe.set_progress_bar_config(disable=A_      )

							A						   =   'A fantasy landscape, trending on artstation'

							A						   =   np.random.RandomState(0      )
							A						   =   pipe(
							    prompt=A_  ,image=A_  ,strength=0.75  ,guidance_scale=7.5  ,num_inference_steps=20  ,generator=A_  ,output_type='np'  ,)
							A						   =   output.images
							A						   =   images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A						   =   np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]      )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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