




"""simple docstring"""





import logging
import os
import threading
import time


try:
					import warnings
except ImportError:
					A   =       None

try:
					import msvcrt
except ImportError:
					A   =       None

try:
					import fcntl
except ImportError:
					A   =       None


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


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

A   =       """3.0.12"""


A   =       None

def 					_UpperCamelCase    (			)			->			Dict:


			"""simple docstring"""
			global _logger
			__UpperCAmelCase	: Union[str, Any]		=					_logger or logging.getLogger(__name__							)
			return _logger



class      lowerCamelCase__				(	__magic_name__   ):


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					lock_file
						return None


			def __str__(      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					F"The file lock '{self.lock_file}' could not be acquired."
						return temp






class      lowerCamelCase__				:


			def __init__(      self      :    str      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					lock
						return None


			def __enter__(      self      :    Dict):


						"""simple docstring"""




						return self.lock


			def __exit__(      self      :    Any      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						self.lock.release()
						return None






class      lowerCamelCase__				:


			def __init__(      self      :    Optional[Any]      ,  UpperCamelCase_      :    List[Any]      ,  UpperCamelCase_      :    List[str]=-1      ,  UpperCamelCase_      :    Dict=None):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					max_filename_length if max_filename_length is not None else 255
						# Hash the filename if it's too long
						__UpperCAmelCase	: Union[str, Any]		=					self.hash_filename_if_too_long(UpperCamelCase_      ,  UpperCamelCase_)
						# The path to the lock file.
						__UpperCAmelCase	: Optional[Any]		=					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	: Dict		=					None

						# The default timeout value.
						__UpperCAmelCase	: Dict		=					timeout

						# We use this lock primarily for the lock counter.
						__UpperCAmelCase	: Any		=					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	: Dict		=					0
						return None


			@property
			def       a_     (      self      :    Dict):


						"""simple docstring"""




						return self._lock_file


			@property
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						return self._timeout


			@timeout.setter
			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					float(UpperCamelCase_)
						return None


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						raise NotImplementedError()


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						raise NotImplementedError()


			@property
			def       a_     (      self      :    str):


						"""simple docstring"""




						return self._lock_file_fd is not None


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Any=None      ,  UpperCamelCase_      :    List[Any]=0.05):


						"""simple docstring"""




						if timeout is None:
									__UpperCAmelCase	: Dict		=					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	: List[Any]		=					id(self)
						__UpperCAmelCase	: str		=					self._lock_file
						__UpperCAmelCase	: Optional[int]		=					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(UpperCamelCase_)
						except:  # noqa
									# Something did go wrong, so decrement the counter.
									with self._thread_lock:
												__UpperCAmelCase	: Tuple		=					max(0      ,  self._lock_counter - 1)

									raise
						return _Acquire_ReturnProxy(lock=self)


			def       a_     (      self      :    int      ,  UpperCamelCase_      :    Any=False):


						"""simple docstring"""




						with self._thread_lock:
									if self.is_locked:
												self._lock_counter -= 1

												if self._lock_counter == 0 or force:
															__UpperCAmelCase	: Union[str, Any]		=					id(self)
															__UpperCAmelCase	: Any		=					self._lock_file

															logger().debug(F"Attempting to release lock {lock_id} on {lock_filename}")
															self._release()
															__UpperCAmelCase	: str		=					0
															logger().debug(F"Lock {lock_id} released on {lock_filename}")

						return None


			def __enter__(      self      :    int):


						"""simple docstring"""




						self.acquire()
						return self


			def __exit__(      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    Any      ,  UpperCamelCase_      :    Optional[Any]):


						"""simple docstring"""




						self.release()
						return None


			def __del__(      self      :    Tuple):


						"""simple docstring"""




						self.release(force=UpperCamelCase_)
						return None


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					os.path.basename(UpperCamelCase_)
						if len(UpperCamelCase_) > max_length and max_length > 0:
									__UpperCAmelCase	: Tuple		=					os.path.dirname(UpperCamelCase_)
									__UpperCAmelCase	: int		=					str(hash(UpperCamelCase_))
									__UpperCAmelCase	: Tuple		=					filename[: max_length - len(UpperCamelCase_) - 8] + "..." + hashed_filename + ".lock"
									return os.path.join(UpperCamelCase_      ,  UpperCamelCase_)
						else:
									return path






class      lowerCamelCase__				(	__magic_name__   ):


			def __init__(      self      :    List[str]      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    Union[str, Any]=-1      ,  UpperCamelCase_      :    List[Any]=None):


						"""simple docstring"""




						from .file_utils import relative_to_absolute_path

						super().__init__(UpperCamelCase_      ,  timeout=UpperCamelCase_      ,  max_filename_length=UpperCamelCase_)
						__UpperCAmelCase	: str		=					"\\\\?\\" + relative_to_absolute_path(self.lock_file)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					os.O_RDWR | os.O_CREAT | os.O_TRUNC

						try:
									__UpperCAmelCase	: List[str]		=					os.open(self._lock_file      ,  UpperCamelCase_)
						except OSError:
									pass
						else:
									try:
												msvcrt.locking(UpperCamelCase_      ,  msvcrt.LK_NBLCK      ,  1)
									except OSError:
												os.close(UpperCamelCase_)
									else:
												__UpperCAmelCase	: List[Any]		=					fd
						return None


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self._lock_file_fd
						__UpperCAmelCase	: Optional[int]		=					None
						msvcrt.locking(UpperCamelCase_      ,  msvcrt.LK_UNLCK      ,  1)
						os.close(UpperCamelCase_)

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






class      lowerCamelCase__				(	__magic_name__   ):


			def __init__(      self      :    Union[str, Any]      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Tuple=-1      ,  UpperCamelCase_      :    Tuple=None):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					os.statvfs(os.path.dirname(UpperCamelCase_)).f_namemax
						super().__init__(UpperCamelCase_      ,  timeout=UpperCamelCase_      ,  max_filename_length=UpperCamelCase_)


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					os.O_RDWR | os.O_CREAT | os.O_TRUNC
						__UpperCAmelCase	: Optional[Any]		=					os.open(self._lock_file      ,  UpperCamelCase_)

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


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					self._lock_file_fd
						__UpperCAmelCase	: str		=					None
						fcntl.flock(UpperCamelCase_      ,  fcntl.LOCK_UN)
						os.close(UpperCamelCase_)
						return None






class      lowerCamelCase__				(	__magic_name__   ):


			def       a_     (      self      :    Any):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
						try:
									__UpperCAmelCase	: Optional[Any]		=					os.open(self._lock_file      ,  UpperCamelCase_)
						except OSError:
									pass
						else:
									__UpperCAmelCase	: Optional[int]		=					fd
						return None


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						os.close(self._lock_file_fd)
						__UpperCAmelCase	: Optional[Any]		=					None

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






A   =       None

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

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








"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class      a__				:
			lowercase_    =	42

			# setable values
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	None


			@classmethod
			def       a_     (      cls      :    List[str]      ,  UpperCamelCase_      :    CommonSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray):


						"""simple docstring"""




						return cls(common=UpperCamelCase_      ,  init_noise_sigma=UpperCamelCase_      ,  timesteps=UpperCamelCase_)






@dataclass
class      a__				(	__magic_name__   ):
			lowercase_    =	42






class      a__				(	__magic_name__							,    __magic_name__   ):
			lowercase_    =	[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowercase_    =	42


			@property
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						return True


			@register_to_config
			def __init__(      self      :    str      ,  UpperCamelCase_      :    int = 1000      ,  UpperCamelCase_      :    float = 0.0001      ,  UpperCamelCase_      :    float = 0.02      ,  UpperCamelCase_      :    str = "linear"      ,  UpperCamelCase_      :    Optional[jnp.ndarray] = None      ,  UpperCamelCase_      :    str = "fixed_small"      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    str = "epsilon"      ,  UpperCamelCase_      :    jnp.dtype = jnp.floataa      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					dtype


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[CommonSchedulerState] = None):


						"""simple docstring"""




						if common is None:
									__UpperCAmelCase	: Tuple		=					CommonSchedulerState.create(self)

						# standard deviation of the initial noise distribution
						__UpperCAmelCase	: Tuple		=					jnp.array(1.0      ,  dtype=self.dtype)

						__UpperCAmelCase	: Any		=					jnp.arange(0      ,  self.config.num_train_timesteps).round()[::-1]

						return DDPMSchedulerState.create(
						    common=UpperCamelCase_      ,  init_noise_sigma=UpperCamelCase_      ,  timesteps=UpperCamelCase_      ,  )


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[int] = None):


						"""simple docstring"""




						return sample


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Tuple = ()):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					self.config.num_train_timesteps // num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# rounding to avoid issues when num_inference_step is power of 3
						__UpperCAmelCase	: List[str]		=					(jnp.arange(0      ,  UpperCamelCase_) * step_ratio).round()[::-1]

						return state.replace(
						    num_inference_steps=UpperCamelCase_      ,  timesteps=UpperCamelCase_      ,  )


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Union[str, Any]=None      ,  UpperCamelCase_      :    Optional[int]=None):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					state.common.alphas_cumprod[t]
						__UpperCAmelCase	: Optional[int]		=					jnp.where(t > 0      ,  state.common.alphas_cumprod[t - 1]      ,  jnp.array(1.0      ,  dtype=self.dtype))

						# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
						# and sample from it to get previous sample
						# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
						__UpperCAmelCase	: Tuple		=					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

						if variance_type is None:
									__UpperCAmelCase	: Optional[Any]		=					self.config.variance_type

						# hacks - were probably added for training stability
						if variance_type == "fixed_small":
									__UpperCAmelCase	: str		=					jnp.clip(UpperCamelCase_      ,  a_min=1e-20)
						# for rl-diffuser https://arxiv.org/abs/2205.09991
						elif variance_type == "fixed_small_log":
									__UpperCAmelCase	: Optional[int]		=					jnp.log(jnp.clip(UpperCamelCase_      ,  a_min=1e-20))
						elif variance_type == "fixed_large":
									__UpperCAmelCase	: Tuple		=					state.common.betas[t]
						elif variance_type == "fixed_large_log":
									# Glide max_log
									__UpperCAmelCase	: str		=					jnp.log(state.common.betas[t])
						elif variance_type == "learned":
									return predicted_variance
						elif variance_type == "learned_range":
									__UpperCAmelCase	: Any		=					variance
									__UpperCAmelCase	: Union[str, Any]		=					state.common.betas[t]
									__UpperCAmelCase	: List[str]		=					(predicted_variance + 1) / 2
									__UpperCAmelCase	: int		=					frac * max_log + (1 - frac) * min_log

						return variance


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[jax.random.KeyArray] = None      ,  UpperCamelCase_      :    bool = True      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					timestep

						if key is None:
									__UpperCAmelCase	: List[str]		=					jax.random.PRNGKey(0)

						if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
									__UpperCAmelCase	,     __UpperCAmelCase	: int		=					jnp.split(UpperCamelCase_      ,  sample.shape[1]      ,  axis=1)
						else:
									__UpperCAmelCase	: List[str]		=					None

						# 1. compute alphas, betas
						__UpperCAmelCase	: str		=					state.common.alphas_cumprod[t]
						__UpperCAmelCase	: str		=					jnp.where(t > 0      ,  state.common.alphas_cumprod[t - 1]      ,  jnp.array(1.0      ,  dtype=self.dtype))
						__UpperCAmelCase	: Tuple		=					1 - alpha_prod_t
						__UpperCAmelCase	: int		=					1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if self.config.prediction_type == "epsilon":
									__UpperCAmelCase	: Any		=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif self.config.prediction_type == "sample":
									__UpperCAmelCase	: Optional[int]		=					model_output
						elif self.config.prediction_type == "v_prediction":
									__UpperCAmelCase	: Optional[int]		=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
						else:
									raise ValueError(
									    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
									    " for the FlaxDDPMScheduler.")

						# 3. Clip "predicted x_0"
						if self.config.clip_sample:
									__UpperCAmelCase	: List[Any]		=					jnp.clip(UpperCamelCase_      ,  -1      ,  1)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						__UpperCAmelCase	: Union[str, Any]		=					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
						__UpperCAmelCase	: Union[str, Any]		=					state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						__UpperCAmelCase	: Optional[Any]		=					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						def random_variance():
									__UpperCAmelCase	: int		=					jax.random.split(UpperCamelCase_      ,  num=1)
									__UpperCAmelCase	: Any		=					jax.random.normal(UpperCamelCase_      ,  shape=model_output.shape      ,  dtype=self.dtype)
									return (self._get_variance(UpperCamelCase_      ,  UpperCamelCase_      ,  predicted_variance=UpperCamelCase_) ** 0.5) * noise

						__UpperCAmelCase	: Tuple		=					jnp.where(t > 0      ,  random_variance()      ,  jnp.zeros(model_output.shape      ,  dtype=self.dtype))

						__UpperCAmelCase	: Tuple		=					pred_prev_sample + variance

						if not return_dict:
									return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_      ,  state=UpperCamelCase_)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




						return add_noise_common(state.common      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




						return get_velocity_common(state.common      ,  UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_)


			def __len__(      self      :    int):


						"""simple docstring"""




						return self.config.num_train_timesteps


