


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


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

__UpperCamelCase    :						Any     =							{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Optional[int]    = "conditional_detr"
   a_				:							Union[str, Any]    = ["past_key_values"]
   a_				:							str    = {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }

   def __init__(  self  : Dict       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : int=None       ,  _lowerCamelCase  : Optional[int]=3       ,  _lowerCamelCase  : Tuple=3_0_0       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Union[str, Any]=2_0_4_8       ,  _lowerCamelCase  : List[str]=8       ,  _lowerCamelCase  : Any=6       ,  _lowerCamelCase  : Any=2_0_4_8       ,  _lowerCamelCase  : List[Any]=8       ,  _lowerCamelCase  : Optional[int]=0.0       ,  _lowerCamelCase  : Any=0.0       ,  _lowerCamelCase  : Tuple=True       ,  _lowerCamelCase  : Union[str, Any]="relu"       ,  _lowerCamelCase  : str=2_5_6       ,  _lowerCamelCase  : int=0.1       ,  _lowerCamelCase  : List[str]=0.0       ,  _lowerCamelCase  : Tuple=0.0       ,  _lowerCamelCase  : List[str]=0.02       ,  _lowerCamelCase  : Union[str, Any]=1.0       ,  _lowerCamelCase  : Optional[int]=False       ,  _lowerCamelCase  : List[str]="sine"       ,  _lowerCamelCase  : Optional[Any]="resnet50"       ,  _lowerCamelCase  : List[str]=True       ,  _lowerCamelCase  : Dict=False       ,  _lowerCamelCase  : Optional[int]=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : List[Any]=2       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Any=1       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Dict=5       ,  _lowerCamelCase  : Dict=2       ,  _lowerCamelCase  : Union[str, Any]=0.25       ,  **_lowerCamelCase  : Tuple       ,  ):






         '''simple docstring'''



         if backbone_config is not None and use_timm_backbone:
               raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""							)

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
                     __lowerCamelCase     :      Any       						=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
               elif isinstance(_lowerCamelCase       ,  _lowerCamelCase							):
                     __lowerCamelCase     :      str       						=						backbone_config.get("""model_type"""							)
                     __lowerCamelCase     :      Optional[Any]       						=						CONFIG_MAPPING[backbone_model_type]
                     __lowerCamelCase     :      Optional[Any]       						=						config_class.from_dict(_lowerCamelCase							)

         __lowerCamelCase     :      Optional[int]       						=						use_timm_backbone
         __lowerCamelCase     :      Union[str, Any]       						=						backbone_config
         __lowerCamelCase     :      List[Any]       						=						num_channels
         __lowerCamelCase     :      str       						=						num_queries
         __lowerCamelCase     :      Dict       						=						d_model
         __lowerCamelCase     :      List[Any]       						=						encoder_ffn_dim
         __lowerCamelCase     :      Optional[Any]       						=						encoder_layers
         __lowerCamelCase     :      Any       						=						encoder_attention_heads
         __lowerCamelCase     :      Dict       						=						decoder_ffn_dim
         __lowerCamelCase     :      List[str]       						=						decoder_layers
         __lowerCamelCase     :      Tuple       						=						decoder_attention_heads
         __lowerCamelCase     :      List[Any]       						=						dropout
         __lowerCamelCase     :      Any       						=						attention_dropout
         __lowerCamelCase     :      Optional[Any]       						=						activation_dropout
         __lowerCamelCase     :      List[str]       						=						activation_function
         __lowerCamelCase     :      Optional[int]       						=						init_std
         __lowerCamelCase     :      Union[str, Any]       						=						init_xavier_std
         __lowerCamelCase     :      Union[str, Any]       						=						encoder_layerdrop
         __lowerCamelCase     :      int       						=						decoder_layerdrop
         __lowerCamelCase     :      Dict       						=						encoder_layers
         __lowerCamelCase     :      Tuple       						=						auxiliary_loss
         __lowerCamelCase     :      Any       						=						position_embedding_type
         __lowerCamelCase     :      Tuple       						=						backbone
         __lowerCamelCase     :      int       						=						use_pretrained_backbone
         __lowerCamelCase     :      Tuple       						=						dilation
         # Hungarian matcher
         __lowerCamelCase     :      Dict       						=						class_cost
         __lowerCamelCase     :      Optional[Any]       						=						bbox_cost
         __lowerCamelCase     :      Any       						=						giou_cost
         # Loss coefficients
         __lowerCamelCase     :      Union[str, Any]       						=						mask_loss_coefficient
         __lowerCamelCase     :      List[Any]       						=						dice_loss_coefficient
         __lowerCamelCase     :      Any       						=						cls_loss_coefficient
         __lowerCamelCase     :      str       						=						bbox_loss_coefficient
         __lowerCamelCase     :      Dict       						=						giou_loss_coefficient
         __lowerCamelCase     :      List[str]       						=						focal_alpha
         super().__init__(is_encoder_decoder=_lowerCamelCase       ,  **_lowerCamelCase							)

   @property
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         return self.encoder_attention_heads

   @property
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         return self.d_model


   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						copy.deepcopy(self.__dict__							)
         if self.backbone_config is not None:
               __lowerCamelCase     :      Dict       						=						self.backbone_config.to_dict()
         __lowerCamelCase     :      int       						=						self.__class__.model_type
         return output



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Dict    = version.parse("1.11"						)

   @property
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                 ("""pixel_mask""", {0: """batch"""}),
             ]							)

   @property
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         return 1E-5


   @property
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         return 1_2







import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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 							_UpperCamelCase (						A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							Any    = "ssube/stable-diffusion-x4-upscaler-onnx"

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : List[str]=0							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						floats_tensor((1, 3, 1_2_8, 1_2_8)       ,  rng=random.Random(_lowerCamelCase							)							)
         __lowerCamelCase     :      int       						=						torch.manual_seed(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						{
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """output_type""": """numpy""",
         }
         return inputs

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      Tuple       						=						self.get_dummy_inputs()
         __lowerCamelCase     :      Dict       						=						pipe(**_lowerCamelCase							).images
         __lowerCamelCase     :      Dict       						=						image[0, -3:, -3:, -1].flatten()

         # started as 128, should now be 512
         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      str       						=						np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]							)
         assert np.abs(image_slice - expected_slice							).max() < 1E-1

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      str       						=						PNDMScheduler.from_config(pipe.scheduler.config       ,  skip_prk_steps=_lowerCamelCase							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      int       						=						np.array(
             [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]							)
         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      int       						=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      str       						=						np.array(
             [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]							)

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

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      Optional[Any]       						=						EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      List[str]       						=						np.array(
             [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]							)
         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1


   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,  provider="""CPUExecutionProvider"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      Optional[int]       						=						np.array(
             [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]							)

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



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

   '''simple docstring'''







   @property
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



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

   @property
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						ort.SessionOptions()
         __lowerCamelCase     :      str       						=						False
         return options

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         __lowerCamelCase     :      Optional[Any]       						=						init_image.resize((1_2_8, 1_2_8)							)
         # using the PNDM scheduler by default
         __lowerCamelCase     :      List[Any]       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						"""A fantasy landscape, trending on artstation"""

         __lowerCamelCase     :      str       						=						torch.manual_seed(0							)
         __lowerCamelCase     :      Any       						=						pipe(
             prompt=_lowerCamelCase       ,  image=_lowerCamelCase       ,  guidance_scale=7.5       ,  num_inference_steps=1_0       ,  generator=_lowerCamelCase       ,  output_type="""np"""       ,  )
         __lowerCamelCase     :      List[str]       						=						output.images
         __lowerCamelCase     :      Any       						=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      int       						=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]							)
         # 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  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						init_image.resize((1_2_8, 1_2_8)							)
         __lowerCamelCase     :      str       						=						LMSDiscreteScheduler.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  subfolder="""scheduler"""							)
         __lowerCamelCase     :      str       						=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
             """ssube/stable-diffusion-x4-upscaler-onnx"""       ,  scheduler=_lowerCamelCase       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
         pipe.set_progress_bar_config(disable=_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						"""A fantasy landscape, trending on artstation"""

         __lowerCamelCase     :      Tuple       						=						torch.manual_seed(0							)
         __lowerCamelCase     :      Dict       						=						pipe(
             prompt=_lowerCamelCase       ,  image=_lowerCamelCase       ,  guidance_scale=7.5       ,  num_inference_steps=2_0       ,  generator=_lowerCamelCase       ,  output_type="""np"""       ,  )
         __lowerCamelCase     :      int       						=						output.images
         __lowerCamelCase     :      List[Any]       						=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __lowerCamelCase     :      List[str]       						=						np.array(
             [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]							)
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



