

"""simple docstring"""





import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


_a       =							threading.Lock()
_a       =							None

_a       =							{
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

_a       =							logging.WARNING

_a       =							True




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


   __lowercase								=						os.getenv("TRANSFORMERS_VERBOSITY",			UpperCamelCase_)
   if env_level_str:
      if env_level_str in log_levels:
         return log_levels[env_level_str]
      else:
         logging.getLogger().warning(
             F"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
             F"""has to be one of: { ", ".join(log_levels.keys()) }""")
   return _default_log_level




def   _A  (  )	->						str:
   '''simple docstring'''


   return __name__.split(".")[0]




def   _A  (  )	->						logging.Logger:
   '''simple docstring'''


   return logging.getLogger(_get_library_name())




def   _A  (  )	->						None:
   '''simple docstring'''


   global _default_handler

   with _lock:
      if _default_handler:
         # This library has already configured the library root logger.
         return
      __lowercase								=						logging.StreamHandler()  # Set sys.stderr as stream.
      __lowercase								=						sys.stderr.flush

      # Apply our default configuration to the library root logger.
      __lowercase								=						_get_library_root_logger()
      library_root_logger.addHandler(_default_handler)
      library_root_logger.setLevel(_get_default_logging_level())
      __lowercase								=						False




def   _A  (  )	->						None:
   '''simple docstring'''


   global _default_handler

   with _lock:
      if not _default_handler:
         return

      __lowercase								=						_get_library_root_logger()
      library_root_logger.removeHandler(_default_handler)
      library_root_logger.setLevel(logging.NOTSET)
      __lowercase								=						None




def   _A  (  )	->						int:
   '''simple docstring'''


   return log_levels




def   _A  (  UpperCamelCase_     :       Optional[str] = None)	->						logging.Logger:
   '''simple docstring'''


   if name is None:
      __lowercase								=						_get_library_name()

   _configure_library_root_logger()
   return logging.getLogger(UpperCamelCase_)




def   _A  (  )	->						int:
   '''simple docstring'''


   _configure_library_root_logger()
   return _get_library_root_logger().getEffectiveLevel()




def   _A  (  UpperCamelCase_     :       int)	->						None:
   '''simple docstring'''


   _configure_library_root_logger()
   _get_library_root_logger().setLevel(UpperCamelCase_)




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


   return set_verbosity(UpperCamelCase_)




def   _A  (  )	->						Optional[Any]:
   '''simple docstring'''


   return set_verbosity(UpperCamelCase_)




def   _A  (  )	->						Dict:
   '''simple docstring'''


   return set_verbosity(UpperCamelCase_)




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


   return set_verbosity(UpperCamelCase_)




def   _A  (  )	->						None:
   '''simple docstring'''


   _configure_library_root_logger()

   assert _default_handler is not None
   _get_library_root_logger().removeHandler(_default_handler)




def   _A  (  )	->						None:
   '''simple docstring'''


   _configure_library_root_logger()

   assert _default_handler is not None
   _get_library_root_logger().addHandler(_default_handler)




def   _A  (  UpperCamelCase_     :       logging.Handler)	->						None:
   '''simple docstring'''


   _configure_library_root_logger()

   assert handler is not None
   _get_library_root_logger().addHandler(UpperCamelCase_)




def   _A  (  UpperCamelCase_     :       logging.Handler)	->						None:
   '''simple docstring'''


   _configure_library_root_logger()

   assert handler is not None and handler not in _get_library_root_logger().handlers
   _get_library_root_logger().removeHandler(UpperCamelCase_)




def   _A  (  )	->						None:
   '''simple docstring'''


   _configure_library_root_logger()
   __lowercase								=						False




def   _A  (  )	->						None:
   '''simple docstring'''


   _configure_library_root_logger()
   __lowercase								=						True




def   _A  (  )	->						None:
   '''simple docstring'''


   __lowercase								=						_get_library_root_logger().handlers

   for handler in handlers:
      __lowercase								=						logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
      handler.setFormatter(UpperCamelCase_)




def   _A  (  )	->						None:
   '''simple docstring'''


   __lowercase								=						_get_library_root_logger().handlers

   for handler in handlers:
      handler.setFormatter(UpperCamelCase_)




def   _A  (  self     :       Optional[int],			*UpperCamelCase_     :       Optional[Any],			**UpperCamelCase_     :       List[str])	->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS",			UpperCamelCase_)
   if no_advisory_warnings:
      return
   self.warning(*UpperCamelCase_,			**UpperCamelCase_)


_a       =							warning_advice




@functools.lru_cache(UpperCamelCase_)
def   _A  (  self     :       Optional[Any],			*UpperCamelCase_     :       List[str],			**UpperCamelCase_     :       List[str])	->						Any:
   '''simple docstring'''


   self.warning(*UpperCamelCase_,			**UpperCamelCase_)


_a       =							warning_once




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Optional[Any],					*UpperCAmelCase__							:			int,					**UpperCAmelCase__							:			Optional[int]	):  # pylint: disable=unused-argument
    __lowercase								=						args[0] if args else None


 def __iter__(			self							:			Dict	):
    return iter(self._iterator	)


 def __getattr__(			self							:			int,					UpperCAmelCase__							:			int	):

    def empty_fn(*UpperCAmelCase__							:			Optional[Any],					**UpperCAmelCase__							:			Tuple	):  # pylint: disable=unused-argument
       return

    return empty_fn


 def __enter__(			self							:			List[Any]	):
    return self



 def __exit__(			self							:			List[str],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Union[str, Any]	):
    return







class 					_lowerCAmelCase  :







 """simple docstring"""







 def __call__(			self							:			Union[str, Any],					*UpperCAmelCase__							:			Dict,					**UpperCAmelCase__							:			Any	):
    if _tqdm_active:
       return tqdm_lib.tqdm(*UpperCAmelCase__,					**UpperCAmelCase__	)
    else:
       return EmptyTqdm(*UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int],					*UpperCAmelCase__							:			Optional[int],					**UpperCAmelCase__							:			Union[str, Any]	):
    __lowercase								=						None
    if _tqdm_active:
       return tqdm_lib.tqdm.set_lock(*UpperCAmelCase__,					**UpperCAmelCase__	)



 def 					_lowercase (			self							:			List[str]	):
    if _tqdm_active:
       return tqdm_lib.tqdm.get_lock()







_a       =							_tqdm_cls()




def   _A  (  )	->						bool:
   '''simple docstring'''


   global _tqdm_active
   return bool(_tqdm_active)




def   _A  (  )	->						Dict:
   '''simple docstring'''


   global _tqdm_active
   __lowercase								=						True
   hf_hub_utils.enable_progress_bars()




def   _A  (  )	->						Tuple:
   '''simple docstring'''


   global _tqdm_active
   __lowercase								=						False
   hf_hub_utils.disable_progress_bars()







"""simple docstring"""





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 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	"ssube/stable-diffusion-x4-upscaler-onnx"


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			List[str]=0	):
    __lowercase								=						floats_tensor((1, 3, 1_2_8, 1_2_8),					rng=random.Random(UpperCAmelCase__	)	)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "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 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    # started as 128, should now be 512
    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						PNDMScheduler.from_config(pipe.scheduler.config,					skip_prk_steps=UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			int	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			str	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						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 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @property
 def 					_lowercase (			self							:			Tuple	):
    return (
        "CUDAExecutionProvider",
        {
            "gpu_mem_limit": "15000000000",  # 15GB
            "arena_extend_strategy": "kSameAsRequested",
        },
    )


 @property
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						ort.SessionOptions()
    __lowercase								=						False
    return options


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

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=1_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						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)
    __lowercase								=						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 					_lowercase (			self							:			str	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    __lowercase								=						LMSDiscreteScheduler.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					subfolder="scheduler"	)
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					scheduler=UpperCAmelCase__,					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=2_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						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)
    __lowercase								=						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




