import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase=0.999     ,      __UpperCamelCase="cosine"     ,      ):






      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__UpperCamelCase				):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__UpperCamelCase				):
                  return math.exp(t * -12.0				)

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

      SCREAMING_SNAKE_CASE_          =  []
      for i in range(a__				):
            SCREAMING_SNAKE_CASE_          =  i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_          =  (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(a__				) / alpha_bar_fn(a__				)     ,      a__				)				)
      return torch.tensor(a__     ,      dtype=torch.floataa				)





class   lowerCamelCase					(__lowercase  ,      __lowercase					):
  """simple docstring"""







  lowerCamelCase__     =						[e.name for e in KarrasDiffusionSchedulers]
  lowerCamelCase__     =						2







  @register_to_config
  def __init__(							self :       Any  ,     __magic_name__ :       Any = 1_000  ,     __magic_name__ :       Dict = 0.0_0085  ,     __magic_name__ :       Any = 0.012  ,     __magic_name__ :       Optional[int] = "linear"  ,     __magic_name__ :       List[str] = None  ,     __magic_name__ :       Optional[int] = "epsilon"  ,     __magic_name__ :       List[Any] = "linspace"  ,     __magic_name__ :       List[str] = 0  ,     )  ->	str:
        if trained_betas is not None:
              SCREAMING_SNAKE_CASE_          =  torch.tensor(__A  ,     dtype=torch.floataa   )
        elif beta_schedule == "linear":
              SCREAMING_SNAKE_CASE_          =  torch.linspace(__A  ,     __A  ,     __A  ,     dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              SCREAMING_SNAKE_CASE_          =  (
                  torch.linspace(beta_start**0.5  ,     beta_end**0.5  ,     __A  ,     dtype=torch.floataa   ) ** 2
              )
        elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              SCREAMING_SNAKE_CASE_          =  betas_for_alpha_bar(__A   )
        else:
              raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''   )

        SCREAMING_SNAKE_CASE_          =  1.0 - self.betas
        SCREAMING_SNAKE_CASE_          =  torch.cumprod(self.alphas  ,     dim=0   )

        #  set all values
        self.set_timesteps(__A  ,     __A  ,     __A   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       int=None   )  ->	Optional[Any]:
        if schedule_timesteps is None:
              SCREAMING_SNAKE_CASE_          =  self.timesteps

        SCREAMING_SNAKE_CASE_          =  (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter   ) == 0:
              SCREAMING_SNAKE_CASE_          =  1 if len(__A   ) > 1 else 0
        else:
              SCREAMING_SNAKE_CASE_          =  timestep.cpu().item() if torch.is_tensor(__A   ) else timestep
              SCREAMING_SNAKE_CASE_          =  self._index_counter[timestep_int]

        return indices[pos].item()







  @property
  def    __A     (							self :       List[Any]   )  ->	List[str]:
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str  ,     __magic_name__ :       Union[str, Any]  ,     )  ->	torch.FloatTensor:
        SCREAMING_SNAKE_CASE_          =  self.index_for_timestep(__A   )

        if self.state_in_first_order:
              SCREAMING_SNAKE_CASE_          =  self.sigmas[step_index]
        else:
              SCREAMING_SNAKE_CASE_          =  self.sigmas_interpol[step_index]

        SCREAMING_SNAKE_CASE_          =  sample / ((sigma**2 + 1) ** 0.5)
        return sample







  def    __A     (							self :       Tuple  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[int] = None  ,     __magic_name__ :       List[Any] = None  ,     )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  num_inference_steps

        SCREAMING_SNAKE_CASE_          =  num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
              SCREAMING_SNAKE_CASE_          =  np.linspace(0  ,     num_train_timesteps - 1  ,     __A  ,     dtype=__A   )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
              SCREAMING_SNAKE_CASE_          =  num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              SCREAMING_SNAKE_CASE_          =  (np.arange(0  ,     __A   ) * step_ratio).round()[::-1].copy().astype(__A   )
              timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
              SCREAMING_SNAKE_CASE_          =  num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              SCREAMING_SNAKE_CASE_          =  (np.arange(__A  ,     0  ,     -step_ratio   )).round().copy().astype(__A   )
              timesteps -= 1
        else:
              raise ValueError(
                  F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''   )

        SCREAMING_SNAKE_CASE_          =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
        SCREAMING_SNAKE_CASE_          =  torch.from_numpy(np.log(__A   )   ).to(__A   )

        SCREAMING_SNAKE_CASE_          =  np.interp(__A  ,     np.arange(0  ,     len(__A   )   )  ,     __A   )
        SCREAMING_SNAKE_CASE_          =  np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
        SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__A   ).to(device=__A   )

        # interpolate sigmas
        SCREAMING_SNAKE_CASE_          =  sigmas.log().lerp(sigmas.roll(1   ).log()  ,     0.5   ).exp()

        SCREAMING_SNAKE_CASE_          =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
        SCREAMING_SNAKE_CASE_          =  torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )

        if str(__A   ).startswith("mps"   ):
              # mps does not support float64
              SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__A   ).to(__A  ,     dtype=torch.floataa   )
        else:
              SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__A   ).to(__A   )

        # interpolate timesteps
        SCREAMING_SNAKE_CASE_          =  self.sigma_to_t(__A   ).to(__A  ,     dtype=timesteps.dtype   )
        SCREAMING_SNAKE_CASE_          =  torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,     dim=-1   ).flatten()

        SCREAMING_SNAKE_CASE_          =  torch.cat([timesteps[:1], interleaved_timesteps]   )

        SCREAMING_SNAKE_CASE_          =  None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        SCREAMING_SNAKE_CASE_          =  defaultdict(__A   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       List[Any]   )  ->	int:
        # get log sigma
        SCREAMING_SNAKE_CASE_          =  sigma.log()

        # get distribution
        SCREAMING_SNAKE_CASE_          =  log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        SCREAMING_SNAKE_CASE_          =  dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
        SCREAMING_SNAKE_CASE_          =  low_idx + 1

        SCREAMING_SNAKE_CASE_          =  self.log_sigmas[low_idx]
        SCREAMING_SNAKE_CASE_          =  self.log_sigmas[high_idx]

        # interpolate sigmas
        SCREAMING_SNAKE_CASE_          =  (low - log_sigma) / (low - high)
        SCREAMING_SNAKE_CASE_          =  w.clamp(0  ,     1   )

        # transform interpolation to time range
        SCREAMING_SNAKE_CASE_          =  (1 - w) * low_idx + w * high_idx
        SCREAMING_SNAKE_CASE_          =  t.view(sigma.shape   )
        return t







  @property
  def    __A     (							self :       Union[str, Any]   )  ->	Tuple:
        return self.sample is None







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Dict  ,     __magic_name__ :       str  ,     __magic_name__ :       int = True  ,     )  ->	Union[SchedulerOutput, Tuple]:
        SCREAMING_SNAKE_CASE_          =  self.index_for_timestep(__A   )

        # advance index counter by 1
        SCREAMING_SNAKE_CASE_          =  timestep.cpu().item() if torch.is_tensor(__A   ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
              SCREAMING_SNAKE_CASE_          =  self.sigmas[step_index]
              SCREAMING_SNAKE_CASE_          =  self.sigmas_interpol[step_index + 1]
              SCREAMING_SNAKE_CASE_          =  self.sigmas[step_index + 1]
        else:
              # 2nd order / KDPM2's method
              SCREAMING_SNAKE_CASE_          =  self.sigmas[step_index - 1]
              SCREAMING_SNAKE_CASE_          =  self.sigmas_interpol[step_index]
              SCREAMING_SNAKE_CASE_          =  self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        SCREAMING_SNAKE_CASE_          =  0
        SCREAMING_SNAKE_CASE_          =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
              SCREAMING_SNAKE_CASE_          =  sigma_hat if self.state_in_first_order else sigma_interpol
              SCREAMING_SNAKE_CASE_          =  sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
              SCREAMING_SNAKE_CASE_          =  sigma_hat if self.state_in_first_order else sigma_interpol
              SCREAMING_SNAKE_CASE_          =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
        elif self.config.prediction_type == "sample":
              raise NotImplementedError("prediction_type not implemented yet: sample"   )
        else:
              raise ValueError(
                  F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

        if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              SCREAMING_SNAKE_CASE_          =  (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              SCREAMING_SNAKE_CASE_          =  sigma_interpol - sigma_hat

              # store for 2nd order step
              SCREAMING_SNAKE_CASE_          =  sample
        else:
              # DPM-Solver-2
              # 2. Convert to an ODE derivative for 2nd order
              SCREAMING_SNAKE_CASE_          =  (sample - pred_original_sample) / sigma_interpol

              # 3. delta timestep
              SCREAMING_SNAKE_CASE_          =  sigma_next - sigma_hat

              SCREAMING_SNAKE_CASE_          =  self.sample
              SCREAMING_SNAKE_CASE_          =  None

        SCREAMING_SNAKE_CASE_          =  sample + derivative * dt

        if not return_dict:
              return (prev_sample,)

        return SchedulerOutput(prev_sample=__A   )







  def    __A     (							self :       int  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]  ,     )  ->	torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        SCREAMING_SNAKE_CASE_          =  self.sigmas.to(device=original_samples.device  ,     dtype=original_samples.dtype   )
        if original_samples.device.type == "mps" and torch.is_floating_point(__A   ):
              # mps does not support float64
              SCREAMING_SNAKE_CASE_          =  self.timesteps.to(original_samples.device  ,     dtype=torch.floataa   )
              SCREAMING_SNAKE_CASE_          =  timesteps.to(original_samples.device  ,     dtype=torch.floataa   )
        else:
              SCREAMING_SNAKE_CASE_          =  self.timesteps.to(original_samples.device   )
              SCREAMING_SNAKE_CASE_          =  timesteps.to(original_samples.device   )

        SCREAMING_SNAKE_CASE_          =  [self.index_for_timestep(__A  ,     __A   ) for t in timesteps]

        SCREAMING_SNAKE_CASE_          =  sigmas[step_indices].flatten()
        while len(sigma.shape   ) < len(original_samples.shape   ):
              SCREAMING_SNAKE_CASE_          =  sigma.unsqueeze(-1   )

        SCREAMING_SNAKE_CASE_          =  original_samples + noise * sigma
        return noisy_samples







  def __len__(							self :       int   )  ->	List[Any]:
        return self.config.num_train_timesteps



import logging
import os
import threading
import time


try:
      import warnings
except ImportError:
      A     :  Optional[int]					     =       None

try:
      import msvcrt
except ImportError:
      A     :  List[str]					     =       None

try:
      import fcntl
except ImportError:
      A     :  Union[str, Any]					     =       None


# Backward compatibility
# ------------------------------------------------
try:
      TimeoutError
except NameError:
      A     :  int					     =       OSError


# Data
# ------------------------------------------------
A     :  Any					     =       [
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

A     :  Tuple					     =       "3.0.12"


A     :  str					     =       None


def      a__ (		):
      global _logger
      SCREAMING_SNAKE_CASE_          =  _logger or logging.getLogger(__name__				)
      return _logger


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  lock_file
        return None







  def __str__(							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  F'''The file lock \'{self.lock_file}\' could not be acquired.'''
        return temp



class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  lock
        return None







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







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



class   lowerCamelCase					:
  """simple docstring"""







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

        # The default timeout value.
        SCREAMING_SNAKE_CASE_          =  timeout

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







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







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







  @timeout.setter
  def    __A     (							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  float(__magic_name__   )
        return None







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        raise NotImplementedError()







  def    __A     (							self :       Optional[Any]   )  ->	int:
        raise NotImplementedError()







  @property
  def    __A     (							self :       Dict   )  ->	List[str]:
        return self._lock_file_fd is not None







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Optional[int]=0.05   )  ->	str:
        # Use the default timeout, if no timeout is provided.
        if timeout is None:
              SCREAMING_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

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

              raise
        return _Acquire_ReturnProxy(lock=self   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Tuple=False   )  ->	Optional[int]:
        with self._thread_lock:
              if self.is_locked:
                    self._lock_counter -= 1

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

                          logger().debug(F'''Attempting to release lock {lock_id} on {lock_filename}'''   )
                          self._release()
                          SCREAMING_SNAKE_CASE_          =  0
                          logger().debug(F'''Lock {lock_id} released on {lock_filename}'''   )

        return None







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







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







  def __del__(							self :       Optional[int]   )  ->	Optional[Any]:
        self.release(force=__magic_name__   )
        return None







  def    __A     (							self :       int  ,     __magic_name__ :       str  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.path.basename(__magic_name__   )
        if len(__magic_name__   ) > max_length and max_length > 0:
              SCREAMING_SNAKE_CASE_          =  os.path.dirname(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  str(hash(__magic_name__   )   )
              SCREAMING_SNAKE_CASE_          =  filename[: max_length - len(__magic_name__   ) - 8] + "..." + hashed_filename + ".lock"
              return os.path.join(__magic_name__  ,     __magic_name__   )
        else:
              return path



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







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

        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  "\\\\?\\" + relative_to_absolute_path(self.lock_file   )







  def    __A     (							self :       Tuple   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC

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







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        msvcrt.locking(__magic_name__  ,     msvcrt.LK_UNLCK  ,     1   )
        os.close(__magic_name__   )

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



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Dict  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple=-1  ,     __magic_name__ :       Dict=None   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  os.statvfs(os.path.dirname(__magic_name__   )   ).f_namemax
        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )







  def    __A     (							self :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC
        SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )

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







  def    __A     (							self :       List[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
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        fcntl.flock(__magic_name__  ,     fcntl.LOCK_UN   )
        os.close(__magic_name__   )
        return None



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
              SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )
        except OSError:
              pass
        else:
              SCREAMING_SNAKE_CASE_          =  fd
        return None







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        os.close(self._lock_file_fd   )
        SCREAMING_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



A     :  List[str]					     =       None

if msvcrt:
      A     :  Tuple					     =       WindowsFileLock
elif fcntl:
      A     :  Union[str, Any]					     =       UnixFileLock
else:
      A     :  List[Any]					     =       SoftFileLock

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


