



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			(       _lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    List[str]      						=					'''ssube/stable-diffusion-x4-upscaler-onnx'''

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=0      ) ->			Optional[int]:
  a__												= floats_tensor((1, 3, 1_2_8, 1_2_8)		,				rng=random.Random(SCREAMING_SNAKE_CASE      )      )
  a__												= torch.manual_seed(SCREAMING_SNAKE_CASE      )
  a__												= {
      '''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 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  a__												= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= self.get_dummy_inputs()
  a__												= pipe(**SCREAMING_SNAKE_CASE      ).images
  a__												= image[0, -3:, -3:, -1].flatten()

  # started as 128, should now be 512
  assert image.shape == (1, 5_1_2, 5_1_2, 3)
  a__												= np.array(
      [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]      )
  assert np.abs(image_slice - expected_slice      ).max() < 1e-1

 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
  a__												= PNDMScheduler.from_config(pipe.scheduler.config		,				skip_prk_steps=SCREAMING_SNAKE_CASE      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= self.get_dummy_inputs()
  a__												= pipe(**SCREAMING_SNAKE_CASE      ).images
  a__												= image[0, -3:, -3:, -1]

  assert image.shape == (1, 5_1_2, 5_1_2, 3)
  a__												= np.array(
      [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]      )
  assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
  a__												= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= self.get_dummy_inputs()
  a__												= pipe(**SCREAMING_SNAKE_CASE      ).images
  a__												= image[0, -3:, -3:, -1]

  assert image.shape == (1, 5_1_2, 5_1_2, 3)
  a__												= np.array(
      [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]      )

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

 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  a__												= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
  a__												= EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= self.get_dummy_inputs()
  a__												= pipe(**SCREAMING_SNAKE_CASE      ).images
  a__												= image[0, -3:, -3:, -1]

  assert image.shape == (1, 5_1_2, 5_1_2, 3)
  a__												= np.array(
      [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]      )
  assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1



 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  a__												= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint		,				provider='''CPUExecutionProvider'''      )
  a__												= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= self.get_dummy_inputs()
  a__												= pipe(**SCREAMING_SNAKE_CASE      ).images
  a__												= image[0, -3:, -3:, -1]

  assert image.shape == (1, 5_1_2, 5_1_2, 3)
  a__												= np.array(
      [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]      )

  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 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  return (
      "CUDAExecutionProvider",
      {
          "gpu_mem_limit": "15000000000",  # 15GB
          "arena_extend_strategy": "kSameAsRequested",
      },
  )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  a__												= ort.SessionOptions()
  a__												= False
  return options

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

  a__												= '''A fantasy landscape, trending on artstation'''

  a__												= torch.manual_seed(0      )
  a__												= pipe(
      prompt=SCREAMING_SNAKE_CASE		,				image=SCREAMING_SNAKE_CASE		,				guidance_scale=7.5		,				num_inference_steps=1_0		,				generator=SCREAMING_SNAKE_CASE		,				output_type='''np'''		,				)
  a__												= output.images
  a__												= 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)
  a__												= np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]      )
  # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



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

  a__												= '''A fantasy landscape, trending on artstation'''

  a__												= torch.manual_seed(0      )
  a__												= pipe(
      prompt=SCREAMING_SNAKE_CASE		,				image=SCREAMING_SNAKE_CASE		,				guidance_scale=7.5		,				num_inference_steps=2_0		,				generator=SCREAMING_SNAKE_CASE		,				output_type='''np'''		,				)
  a__												= output.images
  a__												= 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)
  a__												= np.array(
      [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]      )
  # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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










from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


a_      :      Any    =     logging.get_logger(__name__)


a_      :      str    =     TypeVar('DatasetType', Dataset, IterableDataset)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = "first_exhausted"							,  ):
 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError('''Unable to interleave an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."       )
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )
 else:
  return _interleave_iterable_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = 0							,  ):

 if not dsets:
  raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )
 else:
  return _concatenate_iterable_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )





