





# 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__     :
      _UpperCAmelCase			:CommonSchedulerState

      # setable values
      _UpperCAmelCase			:jnp.ndarray
      _UpperCAmelCase			:jnp.ndarray
      _UpperCAmelCase			:Optional[int]							=     None





      @classmethod
      def  __UpperCamelCase(				cls ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




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



@dataclass
class       A__     ( __snake_case    ):
      _UpperCAmelCase			:DDPMSchedulerState



class       A__     ( __snake_case ,     __snake_case    ):
      _UpperCAmelCase			:Dict							=     [e.name for e in FlaxKarrasDiffusionSchedulers]

      _UpperCAmelCase			:jnp.dtype





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return True





      @register_to_config
      def __init__(				self ,							A_ = 1000 ,							A_ = 0.00_01 ,							A_ = 0.02 ,							A_ = "linear" ,							A_ = None ,							A_ = "fixed_small" ,							A_ = True ,							A_ = "epsilon" ,							A_ = jnp.floataa ,							):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     dtype





      def  __UpperCamelCase(				self ,							A_ = None     ):
       '''simple docstring'''




       if common is None:
        UpperCamelCase			:      List[Any] 			=     CommonSchedulerState.create(self     )

       # standard deviation of the initial noise distribution
       UpperCamelCase			:      List[Any] 			=     jnp.array(1.0 ,							dtype=self.dtype     )

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

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





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       return sample





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ = ()     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     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			:      Union[str, Any] 			=     (jnp.arange(0 ,							A_     ) * step_ratio).round()[::-1]

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





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_=None ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     state.common.alphas_cumprod[t]
       UpperCamelCase			:      List[str] 			=     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			:      str 			=     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
        UpperCamelCase			:      str 			=     self.config.variance_type

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

       return variance





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							A_ = None ,							A_ = True ,							):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     timestep

       if key is None:
        UpperCamelCase			:      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			:      List[str] 			=     jnp.split(A_ ,							sample.shape[1] ,							axis=1     )
       else:
        UpperCamelCase			:      int 			=     None

       # 1. compute alphas, betas
       UpperCamelCase			:      Tuple 			=     state.common.alphas_cumprod[t]
       UpperCamelCase			:      List[str] 			=     jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype     )     )
       UpperCamelCase			:      Union[str, Any] 			=     1 - alpha_prod_t
       UpperCamelCase			:      Tuple 			=     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			:      List[Any] 			=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
        UpperCamelCase			:      List[Any] 			=     model_output
       elif self.config.prediction_type == "v_prediction":
        UpperCamelCase			:      Tuple 			=     (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			:      Dict 			=     jnp.clip(A_ ,							-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			:      int 			=     (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			:      Any 			=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
        UpperCamelCase			:      Dict 			=     jax.random.split(A_ ,							num=1     )
        UpperCamelCase			:      List[Any] 			=     jax.random.normal(A_ ,							shape=model_output.shape ,							dtype=self.dtype     )
        return (self._get_variance(A_ ,							A_ ,							predicted_variance=A_     ) ** 0.5) * noise

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

       UpperCamelCase			:      str 			=     pred_prev_sample + variance

       if not return_dict:
        return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=A_ ,							state=A_     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_ ,							):
       '''simple docstring'''




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





      def __len__(				self     ):
       '''simple docstring'''




       return self.config.num_train_timesteps







import logging
import os
import threading
import time


try:
    import warnings
except ImportError:
    __lowerCamelCase :  str										=  None

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

try:
    import fcntl
except ImportError:
    __lowerCamelCase :  List[Any]										=  None


# Backward compatibility
# ------------------------------------------------
try:
    TimeoutError
except NameError:
    __lowerCamelCase :  Union[str, Any]										=  OSError


# Data
# ------------------------------------------------
__lowerCamelCase :  str										=  [
    """Timeout""",
    """BaseFileLock""",
    """WindowsFileLock""",
    """UnixFileLock""",
    """SoftFileLock""",
    """FileLock""",
]

__lowerCamelCase :  Union[str, Any]										=  """3.0.12"""


__lowerCamelCase :  Any										=  None



def 	A_       (  )		->				List[Any]:
 global _logger
 UpperCamelCase			:      Any 			=     _logger or logging.getLogger(__name__  )
 return _logger



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     lock_file
       return None





      def __str__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     F"""The file lock '{self.lock_file}' could not be acquired."""
       return temp



class       A__     :





      def __init__(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     lock
       return None





      def __enter__(				self     ):
       '''simple docstring'''




       return self.lock





      def __exit__(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       self.lock.release()
       return None



class       A__     :





      def __init__(				self ,							A_ ,							A_=-1 ,							A_=None     ):
       '''simple docstring'''




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

       # The default timeout value.
       UpperCamelCase			:      Optional[Any] 			=     timeout

       # We use this lock primarily for the lock counter.
       UpperCamelCase			:      Union[str, 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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self._lock_file





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self._timeout





      @timeout.setter
      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     float(A_     )
       return None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       raise NotImplementedError()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       raise NotImplementedError()





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self._lock_file_fd is not None





      def  __UpperCamelCase(				self ,							A_=None ,							A_=0.05     ):
       '''simple docstring'''




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

        raise
       return _Acquire_ReturnProxy(lock=self     )





      def  __UpperCamelCase(				self ,							A_=False     ):
       '''simple docstring'''




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

         if self._lock_counter == 0 or force:
          UpperCamelCase			:      List[Any] 			=     id(self     )
          UpperCamelCase			:      Dict 			=     self._lock_file

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

       return None





      def __enter__(				self     ):
       '''simple docstring'''




       self.acquire()
       return self





      def __exit__(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       self.release()
       return None





      def __del__(				self     ):
       '''simple docstring'''




       self.release(force=A_     )
       return None





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




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



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_=-1 ,							A_=None     ):
       '''simple docstring'''




       from .file_utils import relative_to_absolute_path

       super().__init__(A_ ,							timeout=A_ ,							max_filename_length=A_     )
       UpperCamelCase			:      List[Any] 			=     "\\\\?\\" + relative_to_absolute_path(self.lock_file     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     os.O_RDWR | os.O_CREAT | os.O_TRUNC

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self._lock_file_fd
       UpperCamelCase			:      str 			=     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__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_=-1 ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     os.statvfs(os.path.dirname(A_     )     ).f_namemax
       super().__init__(A_ ,							timeout=A_ ,							max_filename_length=A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     os.O_RDWR | os.O_CREAT | os.O_TRUNC
       UpperCamelCase			:      int 			=     os.open(self._lock_file ,							A_     )

       try:
        fcntl.flock(A_ ,							fcntl.LOCK_EX | fcntl.LOCK_NB     )
       except OSError:
        os.close(A_     )
       else:
        UpperCamelCase			:      List[str] 			=     fd
       return None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self._lock_file_fd
       UpperCamelCase			:      List[Any] 			=     None
       fcntl.flock(A_ ,							fcntl.LOCK_UN     )
       os.close(A_     )
       return None



class       A__     ( __snake_case    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
       try:
        UpperCamelCase			:      Optional[int] 			=     os.open(self._lock_file ,							A_     )
       except OSError:
        pass
       else:
        UpperCamelCase			:      Tuple 			=     fd
       return None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       os.close(self._lock_file_fd     )
       UpperCamelCase			:      str 			=     None

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


__lowerCamelCase :  Dict										=  None

if msvcrt:
    __lowerCamelCase :  Any										=  WindowsFileLock
elif fcntl:
    __lowerCamelCase :  Any										=  UnixFileLock
else:
    __lowerCamelCase :  int										=  SoftFileLock

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