"""simple docstring"""



import logging
import os
import threading
import time


try:
       import warnings
except ImportError:
       __lowercase		:		Any														=							None

try:
       import msvcrt
except ImportError:
       __lowercase		:		str														=							None

try:
       import fcntl
except ImportError:
       __lowercase		:		Tuple														=							None


# Backward compatibility
# ------------------------------------------------
try:
       TimeoutError
except NameError:
       __lowercase		:		List[str]														=							OSError


# Data
# ------------------------------------------------
__lowercase		:		Tuple														=							[
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

__lowercase		:		Optional[int]														=							"3.0.12"


__lowercase		:		List[Any]														=							None





def    SCREAMING_SNAKE_CASE       (     ):
     global _logger
     __snake_case				=							_logger or logging.getLogger(__name__)
     return _logger



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     List[str]	,      A_							:     int    )    ->       Optional[int]:
         __snake_case				=							lock_file
         return None







    def __str__(		self							:     str    )    ->       Optional[int]:
         __snake_case				=							f"The file lock '{self.lock_file}' could not be acquired."
         return temp






class      _A	:




    """simple docstring"""




    def __init__(		self							:     Optional[int]	,      A_							:     Union[str, Any]    )    ->       Dict:
         __snake_case				=							lock
         return None
    def __enter__(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         return self.lock







    def __exit__(		self							:     Optional[Any]	,      A_							:     Dict	,      A_							:     Dict	,      A_							:     Tuple    )    ->       str:
         self.lock.release()
         return None






class      _A	:




    """simple docstring"""




    def __init__(		self							:     List[Any]	,      A_							:     Any	,      A_							:     Optional[int]=-1	,      A_							:     str=None    )    ->       List[Any]:
         __snake_case				=							max_filename_length if max_filename_length is not None else 255
         # Hash the filename if it's too long
         __snake_case				=							self.hash_filename_if_too_long(A_	,      A_    )
         # The path to the lock file.
         __snake_case				=							lock_file

         # The file descriptor for the *_lock_file* as it is returned by the
         # os.open() function.
         # This file lock is only NOT None, if the object currently holds the
         # lock.
         __snake_case				=							None

         # The default timeout value.
         __snake_case				=							timeout

         # We use this lock primarily for the lock counter.
         __snake_case				=							threading.Lock()

         # The lock counter is used for implementing the nested locking
         # mechanism. Whenever the lock is acquired, the counter is increased and
         # the lock is only released, when this value is 0 again.
         __snake_case				=							0
         return None
    @property
    def  lowercase					(		self							:     Any    )    ->       Tuple:
         return self._lock_file
    @property
    def  lowercase					(		self							:     Optional[Any]    )    ->       Any:
         return self._timeout
    @timeout.setter
    def  lowercase					(		self							:     List[str]	,      A_							:     int    )    ->       Union[str, Any]:
         __snake_case				=							float(A_    )
         return None
    def  lowercase					(		self							:     Optional[Any]    )    ->       Tuple:
         raise NotImplementedError()
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[Any]:
         raise NotImplementedError()
    @property
    def  lowercase					(		self							:     str    )    ->       List[str]:
         return self._lock_file_fd is not None
    def  lowercase					(		self							:     Tuple	,      A_							:     Union[str, Any]=None	,      A_							:     List[Any]=0.05    )    ->       Any:
         # Use the default timeout, if no timeout is provided.
         if timeout is None:
              __snake_case				=							self.timeout

         # Increment the number right at the beginning.
         # We can still undo it, if something fails.
         with self._thread_lock:
              self._lock_counter += 1

         __snake_case				=							id(self    )
         __snake_case				=							self._lock_file
         __snake_case				=							time.time()
         try:
              while True:
                   with self._thread_lock:
                        if not self.is_locked:
                             logger().debug(f"Attempting to acquire lock {lock_id} on {lock_filename}"    )
                             self._acquire()

                   if self.is_locked:
                        logger().debug(f"Lock {lock_id} acquired on {lock_filename}"    )
                        break
                   elif timeout >= 0 and time.time() - start_time > timeout:
                        logger().debug(f"Timeout on acquiring lock {lock_id} on {lock_filename}"    )
                        raise Timeout(self._lock_file    )
                   else:
                        logger().debug(
                            f"Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ..."    )
                        time.sleep(A_    )
         except:  # noqa
              # Something did go wrong, so decrement the counter.
              with self._thread_lock:
                   __snake_case				=							max(0	,      self._lock_counter - 1    )

              raise
         return _Acquire_ReturnProxy(lock=self    )
    def  lowercase					(		self							:     str	,      A_							:     List[str]=False    )    ->       int:
         with self._thread_lock:
              if self.is_locked:
                   self._lock_counter -= 1

                   if self._lock_counter == 0 or force:
                        __snake_case				=							id(self    )
                        __snake_case				=							self._lock_file

                        logger().debug(f"Attempting to release lock {lock_id} on {lock_filename}"    )
                        self._release()
                        __snake_case				=							0
                        logger().debug(f"Lock {lock_id} released on {lock_filename}"    )

         return None
    def __enter__(		self							:     int    )    ->       Optional[int]:
         self.acquire()
         return self
    def __exit__(		self							:     Optional[Any]	,      A_							:     Optional[int]	,      A_							:     List[Any]	,      A_							:     List[str]    )    ->       List[Any]:
         self.release()
         return None
    def __del__(		self							:     str    )    ->       Any:
         self.release(force=A_    )
         return None







    def  lowercase					(		self							:     Optional[Any]	,      A_							:     str	,      A_							:     int    )    ->       str:
         __snake_case				=							os.path.basename(A_    )
         if len(A_    ) > max_length and max_length > 0:
              __snake_case				=							os.path.dirname(A_    )
              __snake_case				=							str(hash(A_    )    )
              __snake_case				=							filename[: max_length - len(A_    ) - 8] + '''...''' + hashed_filename + '''.lock'''
              return os.path.join(A_	,      A_    )
         else:
              return path






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     Optional[int]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]=-1	,      A_							:     str=None    )    ->       Tuple:
         from .file_utils import relative_to_absolute_path

         super().__init__(A_	,      timeout=A_	,      max_filename_length=A_    )
         __snake_case				=							'''\\\\?\\''' + relative_to_absolute_path(self.lock_file    )
    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         __snake_case				=							os.O_RDWR | os.O_CREAT | os.O_TRUNC

         try:
              __snake_case				=							os.open(self._lock_file	,      A_    )
         except OSError:
              pass
         else:
              try:
                   msvcrt.locking(A_	,      msvcrt.LK_NBLCK	,      1    )
              except OSError:
                   os.close(A_    )
              else:
                   __snake_case				=							fd
         return None







    def  lowercase					(		self							:     Any    )    ->       Optional[int]:
         __snake_case				=							self._lock_file_fd
         __snake_case				=							None
         msvcrt.locking(A_	,      msvcrt.LK_UNLCK	,      1    )
         os.close(A_    )

         try:
              os.remove(self._lock_file    )
         # Probably another instance of the application
         # that acquired the file lock.
         except OSError:
              pass
         return None






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def __init__(		self							:     str	,      A_							:     Any	,      A_							:     Optional[Any]=-1	,      A_							:     Tuple=None    )    ->       str:
         __snake_case				=							os.statvfs(os.path.dirname(A_    )    ).f_namemax
         super().__init__(A_	,      timeout=A_	,      max_filename_length=A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         __snake_case				=							os.O_RDWR | os.O_CREAT | os.O_TRUNC
         __snake_case				=							os.open(self._lock_file	,      A_    )

         try:
              fcntl.flock(A_	,      fcntl.LOCK_EX | fcntl.LOCK_NB    )
         except OSError:
              os.close(A_    )
         else:
              __snake_case				=							fd
         return None







    def  lowercase					(		self							:     str    )    ->       Optional[Any]:
         # Do not remove the lockfile:
         #
         #   https://github.com/benediktschmitt/py-filelock/issues/31
         #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
         __snake_case				=							self._lock_file_fd
         __snake_case				=							None
         fcntl.flock(A_	,      fcntl.LOCK_UN    )
         os.close(A_    )
         return None






class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         __snake_case				=							os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
         try:
              __snake_case				=							os.open(self._lock_file	,      A_    )
         except OSError:
              pass
         else:
              __snake_case				=							fd
         return None







    def  lowercase					(		self							:     Dict    )    ->       Dict:
         os.close(self._lock_file_fd    )
         __snake_case				=							None

         try:
              os.remove(self._lock_file    )
         # The file is already deleted and that's what we want.
         except OSError:
              pass
         return None






__lowercase		:		str														=							None

if msvcrt:
       __lowercase		:		Union[str, Any]														=							WindowsFileLock
elif fcntl:
       __lowercase		:		Optional[int]														=							UnixFileLock
else:
       __lowercase		:		Dict														=							SoftFileLock

       if warnings is not None:
              warnings.warn("only soft file lock is available")







"""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      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
    def  lowercase					(		self							:     Optional[int]	,      A_							:     List[str]=0    )    ->       int:
         __snake_case				=							floats_tensor((1, 3, 128, 128)	,      rng=random.Random(A_    )    )
         __snake_case				=							np.random.RandomState(A_    )
         __snake_case				=							{
             '''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  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     Tuple    )    ->       Optional[int]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							PNDMScheduler.from_config(pipe.scheduler.config	,      skip_prk_steps=A_    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     Optional[int]    )    ->       str:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

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

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							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  lowercase					(		self							:     List[str]    )    ->       Any:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

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

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							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      _A	(						unittest.TestCase					):




    """simple docstring"""




    @property
    def  lowercase					(		self							:     str    )    ->       int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							ort.SessionOptions()
         __snake_case				=							False
         return options
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         # using the PNDM scheduler by default
         __snake_case				=							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_    )

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

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

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							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  lowercase					(		self							:     str    )    ->       Optional[int]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         __snake_case				=							LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''	,      subfolder='''scheduler'''	,      revision='''onnx'''    )
         __snake_case				=							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_    )

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

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

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							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