





"""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  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""






       def 	snake_case		(       self       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				floats_tensor((1, 3, 1_2_8, 1_2_8)       ,					rng=random.Random(__UpperCAmelCase ) )
         lowerCAmelCase__     :List[str]											=				np.random.RandomState(__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				{
             '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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Union[str, Any]											=				self.get_dummy_inputs()
         lowerCAmelCase__     :Optional[int]											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Union[str, Any]											=				image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Union[str, Any]											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :str											=				PNDMScheduler.from_config(pipe.scheduler.config       ,					skip_prk_steps=__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :Optional[Any]											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Any											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :int											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :List[str]											=				LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         # warmup pass to apply optimizations
         lowerCAmelCase__     :List[Any]											=				pipe(**self.get_dummy_inputs() )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs()
         lowerCAmelCase__     :int											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Any											=				image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Union[str, Any]											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :Dict											=				EulerDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Optional[int]											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :str											=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :int											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,					provider='CPUExecutionProvider' )
         lowerCAmelCase__     :List[Any]											=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__     :Optional[Any]											=				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 	snake_case		(       self ):



         '''simple docstring'''

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






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				ort.SessionOptions()
         lowerCAmelCase__     :Optional[int]											=				False
         return options






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg' )
         lowerCAmelCase__     :Any											=				init_image.resize((7_6_8, 5_1_2) )
         # using the PNDM scheduler by default
         lowerCAmelCase__     :Optional[int]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             'CompVis/stable-diffusion-v1-4'       ,					revision='onnx'       ,					safety_checker=__UpperCAmelCase       ,					feature_extractor=__UpperCAmelCase       ,					provider=self.gpu_provider       ,					sess_options=self.gpu_options       ,					)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				'A fantasy landscape, trending on artstation'

         lowerCAmelCase__     :Optional[Any]											=				np.random.RandomState(0 )
         lowerCAmelCase__     :List[str]											=				pipe(
             prompt=__UpperCAmelCase       ,					image=__UpperCAmelCase       ,					strength=0.75       ,					guidance_scale=7.5       ,					num_inference_steps=1_0       ,					generator=__UpperCAmelCase       ,					output_type='np'       ,					)
         lowerCAmelCase__     :Any											=				output.images
         lowerCAmelCase__     :List[str]											=				images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         lowerCAmelCase__     :List[Any]											=				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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/img2img/sketch-mountains-input.jpg' )
         lowerCAmelCase__     :Optional[Any]											=				init_image.resize((7_6_8, 5_1_2) )
         lowerCAmelCase__     :List[Any]											=				LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-v1-5'       ,					subfolder='scheduler'       ,					revision='onnx' )
         lowerCAmelCase__     :Optional[Any]											=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'       ,					revision='onnx'       ,					scheduler=__UpperCAmelCase       ,					safety_checker=__UpperCAmelCase       ,					feature_extractor=__UpperCAmelCase       ,					provider=self.gpu_provider       ,					sess_options=self.gpu_options       ,					)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				'A fantasy landscape, trending on artstation'

         lowerCAmelCase__     :List[Any]											=				np.random.RandomState(0 )
         lowerCAmelCase__     :List[Any]											=				pipe(
             prompt=__UpperCAmelCase       ,					image=__UpperCAmelCase       ,					strength=0.75       ,					guidance_scale=7.5       ,					num_inference_steps=2_0       ,					generator=__UpperCAmelCase       ,					output_type='np'       ,					)
         lowerCAmelCase__     :Optional[Any]											=				output.images
         lowerCAmelCase__     :int											=				images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         lowerCAmelCase__     :List[Any]											=				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







"""simple docstring"""





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

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


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A											=						logging.get_logger(__name__)

__A											=						{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   """deberta-v2"""






       def __init__(       self       ,					__UpperCAmelCase=1_2_8_1_0_0       ,					__UpperCAmelCase=1_5_3_6       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=6_1_4_4       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=1E-7       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=-1       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase="gelu"       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				hidden_size
         lowerCAmelCase__     :Optional[int]											=				num_hidden_layers
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads
         lowerCAmelCase__     :int											=				intermediate_size
         lowerCAmelCase__     :Dict											=				hidden_act
         lowerCAmelCase__     :Tuple											=				hidden_dropout_prob
         lowerCAmelCase__     :Optional[int]											=				attention_probs_dropout_prob
         lowerCAmelCase__     :Optional[Any]											=				max_position_embeddings
         lowerCAmelCase__     :Optional[Any]											=				type_vocab_size
         lowerCAmelCase__     :List[Any]											=				initializer_range
         lowerCAmelCase__     :Dict											=				relative_attention
         lowerCAmelCase__     :Union[str, Any]											=				max_relative_positions
         lowerCAmelCase__     :List[str]											=				pad_token_id
         lowerCAmelCase__     :Optional[int]											=				position_biased_input

         # Backwards compatibility
         if type(__UpperCAmelCase ) == str:
           lowerCAmelCase__     :Dict											=				[x.strip() for x in pos_att_type.lower().split('|' )]

         lowerCAmelCase__     :str											=				pos_att_type
         lowerCAmelCase__     :Dict											=				vocab_size
         lowerCAmelCase__     :Tuple											=				layer_norm_eps

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('pooler_hidden_size'       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				pooler_dropout
         lowerCAmelCase__     :int											=				pooler_hidden_act






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self.task == "multiple-choice":
           lowerCAmelCase__     :str											=				{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           lowerCAmelCase__     :Tuple											=				{0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
           return OrderedDict(
               [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
         else:
           return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 1_2






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				super().generate_dummy_inputs(preprocessor=__UpperCAmelCase       ,					framework=__UpperCAmelCase )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
         return dummy_inputs
