



"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
									UpperCAmelCase					=			1
									UpperCAmelCase					=			3
									UpperCAmelCase					=			(32, 32)

									UpperCAmelCase					=			floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0 ) ).to(lowercase_ )
									return image






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
									torch.manual_seed(0 )
									UpperCAmelCase					=			UNetaDConditionModel(
									    block_out_channels=(32, 32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=7       ,  out_channels=4       ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')       ,  up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')       ,  cross_attention_dim=32       ,  attention_head_dim=8       ,  use_linear_projection=lowercase_       ,  only_cross_attention=(True, True, False)       ,  num_class_embeds=1_00       ,  )
									return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Any:
									torch.manual_seed(0 )
									UpperCAmelCase					=			AutoencoderKL(
									    block_out_channels=[32, 32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']       ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']       ,  latent_channels=4       ,  )
									return model






						@property
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
									torch.manual_seed(0 )
									UpperCAmelCase					=			CLIPTextConfig(
									    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=10_00       ,  hidden_act='gelu'       ,  projection_dim=5_12       ,  )
									return CLIPTextModel(lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
									UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase					=			self.dummy_cond_unet_upscale
									UpperCAmelCase					=			DDPMScheduler()
									UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
									UpperCAmelCase					=			self.dummy_vae
									UpperCAmelCase					=			self.dummy_text_encoder
									UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

									UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
									UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

									# make sure here that pndm scheduler skips prk
									UpperCAmelCase					=			StableDiffusionUpscalePipeline(
									    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
									UpperCAmelCase					=			sd_pipe.to(lowercase_ )
									sd_pipe.set_progress_bar_config(disable=lowercase_ )

									UpperCAmelCase					=			'A painting of a squirrel eating a burger'
									UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
									UpperCAmelCase					=			sd_pipe(
									    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )

									UpperCAmelCase					=			output.images

									UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
									UpperCAmelCase					=			sd_pipe(
									    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  return_dict=lowercase_       ,  )[0]

									UpperCAmelCase					=			image[0, -3:, -3:, -1]
									UpperCAmelCase					=			image_from_tuple[0, -3:, -3:, -1]

									UpperCAmelCase					=			low_res_image.size[0] * 4
									assert image.shape == (1, expected_height_width, expected_height_width, 3)
									UpperCAmelCase					=			np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661] )

									assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
									UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase					=			self.dummy_cond_unet_upscale
									UpperCAmelCase					=			DDPMScheduler()
									UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
									UpperCAmelCase					=			self.dummy_vae
									UpperCAmelCase					=			self.dummy_text_encoder
									UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

									UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
									UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

									# make sure here that pndm scheduler skips prk
									UpperCAmelCase					=			StableDiffusionUpscalePipeline(
									    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
									UpperCAmelCase					=			sd_pipe.to(lowercase_ )
									sd_pipe.set_progress_bar_config(disable=lowercase_ )

									UpperCAmelCase					=			'A painting of a squirrel eating a burger'
									UpperCAmelCase					=			sd_pipe(
									    2 * [prompt]       ,  image=2 * [low_res_image]       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
									UpperCAmelCase					=			output.images
									assert image.shape[0] == 2

									UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
									UpperCAmelCase					=			sd_pipe(
									    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_images_per_prompt=2       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
									UpperCAmelCase					=			output.images
									assert image.shape[0] == 2






						@unittest.skipIf(torch_device != 'cuda'       ,  'This test requires a GPU' )
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
									UpperCAmelCase					=			self.dummy_cond_unet_upscale
									UpperCAmelCase					=			DDPMScheduler()
									UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
									UpperCAmelCase					=			self.dummy_vae
									UpperCAmelCase					=			self.dummy_text_encoder
									UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

									UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
									UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

									# put models in fp16, except vae as it overflows in fp16
									UpperCAmelCase					=			unet.half()
									UpperCAmelCase					=			text_encoder.half()

									# make sure here that pndm scheduler skips prk
									UpperCAmelCase					=			StableDiffusionUpscalePipeline(
									    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
									UpperCAmelCase					=			sd_pipe.to(lowercase_ )
									sd_pipe.set_progress_bar_config(disable=lowercase_ )

									UpperCAmelCase					=			'A painting of a squirrel eating a burger'
									UpperCAmelCase					=			torch.manual_seed(0 )
									UpperCAmelCase					=			sd_pipe(
									    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=2       ,  output_type='np'       ,  ).images

									UpperCAmelCase					=			low_res_image.size[0] * 4
									assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Dict )    ->      str:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
									UpperCAmelCase					=			load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-upscale/low_res_cat.png' )
									UpperCAmelCase					=			load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
									    '/upsampled_cat.npy' )

									UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
									UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(lowercase_ )
									pipe.to(lowercase_ )
									pipe.set_progress_bar_config(disable=lowercase_ )
									pipe.enable_attention_slicing()

									UpperCAmelCase					=			'a cat sitting on a park bench'

									UpperCAmelCase					=			torch.manual_seed(0 )
									UpperCAmelCase					=			pipe(
									    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
									UpperCAmelCase					=			output.images[0]

									assert image.shape == (5_12, 5_12, 3)
									assert np.abs(expected_image - image ).max() < 1E-3






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
									UpperCAmelCase					=			load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-upscale/low_res_cat.png' )
									UpperCAmelCase					=			load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
									    '/upsampled_cat_fp16.npy' )

									UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
									UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
									    lowercase_       ,  torch_dtype=torch.floataa       ,  )
									pipe.to(lowercase_ )
									pipe.set_progress_bar_config(disable=lowercase_ )
									pipe.enable_attention_slicing()

									UpperCAmelCase					=			'a cat sitting on a park bench'

									UpperCAmelCase					=			torch.manual_seed(0 )
									UpperCAmelCase					=			pipe(
									    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
									UpperCAmelCase					=			output.images[0]

									assert image.shape == (5_12, 5_12, 3)
									assert np.abs(expected_image - image ).max() < 5E-1






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									UpperCAmelCase					=			load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-upscale/low_res_cat.png' )

									UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
									UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
									    lowercase_       ,  torch_dtype=torch.floataa       ,  )
									pipe.to(lowercase_ )
									pipe.set_progress_bar_config(disable=lowercase_ )
									pipe.enable_attention_slicing(1 )
									pipe.enable_sequential_cpu_offload()

									UpperCAmelCase					=			'a cat sitting on a park bench'

									UpperCAmelCase					=			torch.manual_seed(0 )
									UpperCAmelCase					=			pipe(
									    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=5       ,  output_type='np'       ,  )

									UpperCAmelCase					=			torch.cuda.max_memory_allocated()
									# make sure that less than 2.9 GB is allocated
									assert mem_bytes < 2.9 * 10**9










"""simple docstring"""


import logging
import os
import threading
import time


try:
				import warnings
except ImportError:
				snake_case_    					=	None

try:
				import msvcrt
except ImportError:
				snake_case_    					=	None

try:
				import fcntl
except ImportError:
				snake_case_    					=	None


# Backward compatibility
# ------------------------------------------------
try:
				TimeoutError
except NameError:
				snake_case_    					=	OSError


# Data
# ------------------------------------------------
snake_case_    					=	[
    """Timeout""",
    """BaseFileLock""",
    """WindowsFileLock""",
    """UnixFileLock""",
    """SoftFileLock""",
    """FileLock""",
]

snake_case_    					=	"""3.0.12"""


snake_case_    					=	None

def 			_lowerCAmelCase				(       ):
						global _logger
						UpperCAmelCase					=			_logger or logging.getLogger(__name__						)
						return _logger







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:int       ,  lowercase_			:List[Any] )    ->      Optional[int]:
												UpperCAmelCase					=			lock_file
												return None






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







class 				A_      :



						"""simple docstring"""






						def __init__( self			:List[Any]       ,  lowercase_			:Dict )    ->      Dict:
												UpperCAmelCase					=			lock
												return None






						def __enter__( self			:Optional[int] )    ->      Optional[int]:
												return self.lock






						def __exit__( self			:Optional[int]       ,  lowercase_			:Tuple       ,  lowercase_			:Any       ,  lowercase_			:Union[str, Any] )    ->      List[Any]:
												self.lock.release()
												return None







class 				A_      :



						"""simple docstring"""






						def __init__( self			:List[str]       ,  lowercase_			:str       ,  lowercase_			:str=-1       ,  lowercase_			:List[Any]=None )    ->      Dict:
												UpperCAmelCase					=			max_filename_length if max_filename_length is not None else 2_55
												# Hash the filename if it's too long
												UpperCAmelCase					=			self.hash_filename_if_too_long(lowercase_       ,  lowercase_ )
												# The path to the lock file.
												UpperCAmelCase					=			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.
												UpperCAmelCase					=			None

												# The default timeout value.
												UpperCAmelCase					=			timeout

												# We use this lock primarily for the lock counter.
												UpperCAmelCase					=			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.
												UpperCAmelCase					=			0
												return None






						@property
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      int:
												return self._lock_file






						@property
						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Any:
												return self._timeout






						@timeout.setter
						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:Any )    ->      Tuple:
												UpperCAmelCase					=			float(lowercase_ )
												return None






						def      UpperCAmelCase__					( self			:Any )    ->      List[str]:
												raise NotImplementedError()






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      List[str]:
												raise NotImplementedError()






						@property
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Union[str, Any]:
												return self._lock_file_fd is not None






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:Dict=None       ,  lowercase_			:int=0.05 )    ->      Union[str, Any]:
												# Use the default timeout, if no timeout is provided.
												if timeout is None:
																		UpperCAmelCase					=			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

												UpperCAmelCase					=			id(self )
												UpperCAmelCase					=			self._lock_file
												UpperCAmelCase					=			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(lowercase_ )
												except:  # noqa
																		# Something did go wrong, so decrement the counter.
																		with self._thread_lock:
																								UpperCAmelCase					=			max(0       ,  self._lock_counter - 1 )

																		raise
												return _Acquire_ReturnProxy(lock=self )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:List[str]=False )    ->      int:
												with self._thread_lock:
																		if self.is_locked:
																								self._lock_counter -= 1

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

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

												return None






						def __enter__( self			:Any )    ->      Tuple:
												self.acquire()
												return self






						def __exit__( self			:Dict       ,  lowercase_			:Optional[int]       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Union[str, Any] )    ->      Any:
												self.release()
												return None






						def __del__( self			:Any )    ->      int:
												self.release(force=lowercase_ )
												return None






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:str       ,  lowercase_			:int )    ->      str:
												UpperCAmelCase					=			os.path.basename(lowercase_ )
												if len(lowercase_ ) > max_length and max_length > 0:
																		UpperCAmelCase					=			os.path.dirname(lowercase_ )
																		UpperCAmelCase					=			str(hash(lowercase_ ) )
																		UpperCAmelCase					=			filename[: max_length - len(lowercase_ ) - 8] + '...' + hashed_filename + '.lock'
																		return os.path.join(lowercase_       ,  lowercase_ )
												else:
																		return path







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






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

												super().__init__(lowercase_       ,  timeout=lowercase_       ,  max_filename_length=lowercase_ )
												UpperCAmelCase					=			'\\\\?\\' + relative_to_absolute_path(self.lock_file )






						def      UpperCAmelCase__					( self			:List[str] )    ->      str:
												UpperCAmelCase					=			os.O_RDWR | os.O_CREAT | os.O_TRUNC

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






						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			self._lock_file_fd
												UpperCAmelCase					=			None
												msvcrt.locking(lowercase_       ,  msvcrt.LK_UNLCK       ,  1 )
												os.close(lowercase_ )

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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:str       ,  lowercase_			:List[str]=-1       ,  lowercase_			:str=None )    ->      str:
												UpperCAmelCase					=			os.statvfs(os.path.dirname(lowercase_ ) ).f_namemax
												super().__init__(lowercase_       ,  timeout=lowercase_       ,  max_filename_length=lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Dict:
												UpperCAmelCase					=			os.O_RDWR | os.O_CREAT | os.O_TRUNC
												UpperCAmelCase					=			os.open(self._lock_file       ,  lowercase_ )

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






						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[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
												UpperCAmelCase					=			self._lock_file_fd
												UpperCAmelCase					=			None
												fcntl.flock(lowercase_       ,  fcntl.LOCK_UN )
												os.close(lowercase_ )
												return None







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[str]:
												UpperCAmelCase					=			os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
												try:
																		UpperCAmelCase					=			os.open(self._lock_file       ,  lowercase_ )
												except OSError:
																		pass
												else:
																		UpperCAmelCase					=			fd
												return None






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[Any]:
												os.close(self._lock_file_fd )
												UpperCAmelCase					=			None

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


snake_case_    					=	None

if msvcrt:
				snake_case_    					=	WindowsFileLock
elif fcntl:
				snake_case_    					=	UnixFileLock
else:
				snake_case_    					=	SoftFileLock

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





