





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


A_												=     "scheduler_config.json"

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    1
					A__				 =    2
					A__				 =    3
					A__				 =    4
					A__				 =    5

@dataclass
class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    42

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    SCHEDULER_CONFIG_NAME
					A__				 =    ["dtype"]
					A__				 =    []
					A__				 =    True





					@classmethod
					def 					__magic_name__ ( cls	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase=False	,  **_lowerCAmelCase	,  ):
										lowerCamelCase__    ,		lowerCamelCase__       =       cls.load_config(
										    pretrained_model_name_or_path=_lowerCAmelCase	,  subfolder=_lowerCAmelCase	,  return_unused_kwargs=_lowerCAmelCase	,  **_lowerCAmelCase	,  )
										lowerCamelCase__    ,		lowerCamelCase__       =       cls.from_config(_lowerCAmelCase	,  return_unused_kwargs=_lowerCAmelCase	,  **_lowerCAmelCase	)

										if hasattr(_lowerCAmelCase	,  "create_state"	) and getattr(_lowerCAmelCase	,  "has_state"	,  _lowerCAmelCase	):
															lowerCamelCase__       =       scheduler.create_state()

										if return_unused_kwargs:
															return scheduler, state, unused_kwargs

										return scheduler, state





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = False	,  **_lowerCAmelCase	):
										self.save_config(save_directory=_lowerCAmelCase	,  push_to_hub=_lowerCAmelCase	,  **_lowerCAmelCase	)





					@property
					def 					__magic_name__ ( self	):
										return self._get_compatibles()






					@classmethod
					def 					__magic_name__ ( cls	):
										lowerCamelCase__       =       list(set([cls.__name__] + cls._compatibles	)	)
										lowerCamelCase__       =       importlib.import_module(__name__.split("."	)[0]	)
										lowerCamelCase__       =       [
										    getattr(_lowerCAmelCase	,  _lowerCAmelCase	) for c in compatible_classes_str if hasattr(_lowerCAmelCase	,  _lowerCAmelCase	)
										]
										return compatible_classes





def    __UpperCamelCase      (    a,     a) ->jnp.ndarray:
					assert len(a) >= x.ndim
					return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(a) - x.ndim)),     a)





def    __UpperCamelCase      (    a,     a=0.9_9_9,     a=jnp.floataa) ->jnp.ndarray:

					def alpha_bar(a):
										return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

					lowerCamelCase__       =       []
					for i in range(a):
										lowerCamelCase__       =       i / num_diffusion_timesteps
										lowerCamelCase__       =       (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar(a) / alpha_bar(a),     a))
					return jnp.array(a,     dtype=a)

@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    42
					A__				 =    42
					A__				 =    42





					@classmethod
					def 					__magic_name__ ( cls	,  _lowerCAmelCase	):
										lowerCamelCase__       =       scheduler.config

										if config.trained_betas is not None:
															lowerCamelCase__       =       jnp.asarray(config.trained_betas	,  dtype=scheduler.dtype	)
										elif config.beta_schedule == "linear":
															lowerCamelCase__       =       jnp.linspace(config.beta_start	,  config.beta_end	,  config.num_train_timesteps	,  dtype=scheduler.dtype	)
										elif config.beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															lowerCamelCase__       =       (
															    jnp.linspace(
															        config.beta_start**0.5	,  config.beta_end**0.5	,  config.num_train_timesteps	,  dtype=scheduler.dtype	)
															    ** 2
															)
										elif config.beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															lowerCamelCase__       =       betas_for_alpha_bar(config.num_train_timesteps	,  dtype=scheduler.dtype	)
										else:
															raise NotImplementedError(
															    F"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"	)

										lowerCamelCase__       =       1.0 - betas

										lowerCamelCase__       =       jnp.cumprod(_lowerCAmelCase	,  axis=0	)

										return cls(
										    alphas=_lowerCAmelCase	,  betas=_lowerCAmelCase	,  alphas_cumprod=_lowerCAmelCase	,  )





def    __UpperCamelCase      (    a,     a,     a,     a) ->List[str]:
					lowerCamelCase__       =       state.alphas_cumprod

					lowerCamelCase__       =       alphas_cumprod[timesteps] ** 0.5
					lowerCamelCase__       =       sqrt_alpha_prod.flatten()
					lowerCamelCase__       =       broadcast_to_shape_from_left(a,     original_samples.shape)

					lowerCamelCase__       =       (1 - alphas_cumprod[timesteps]) ** 0.5
					lowerCamelCase__       =       sqrt_one_minus_alpha_prod.flatten()
					lowerCamelCase__       =       broadcast_to_shape_from_left(a,     original_samples.shape)

					return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       get_sqrt_alpha_prod(a,     a,     a,     a)
					lowerCamelCase__       =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples







def    __UpperCamelCase      (    a,     a,     a,     a) ->str:
					lowerCamelCase__    ,		lowerCamelCase__       =       get_sqrt_alpha_prod(a,     a,     a,     a)
					lowerCamelCase__       =       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
					return velocity








from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
A_												=     Lock()





def    __UpperCamelCase      (    a,     a,     a,     a,     a,     a,     a) ->Optional[int]:
					global process_lock

					# we perform n swaps since after n swaps we know we are sorted
					# we *could* stop early if we are sorted already, but it takes as long to
					# find out we are sorted as it does to sort the list with this algorithm
					for i in range(0,     10):
										if (i + position) % 2 == 0 and r_send is not None:
															# send your value to your right neighbor
															process_lock.acquire()
															r_send[1].send(a)
															process_lock.release()

															# receive your right neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       rr_cv[0].recv()
															process_lock.release()

															# take the lower value since you are on the left
															lowerCamelCase__       =       min(a,     a)
										elif (i + position) % 2 != 0 and l_send is not None:
															# send your value to your left neighbor
															process_lock.acquire()
															l_send[1].send(a)
															process_lock.release()

															# receive your left neighbor's value
															process_lock.acquire()
															lowerCamelCase__       =       lr_cv[0].recv()
															process_lock.release()

															# take the higher value since you are on the right
															lowerCamelCase__       =       max(a,     a)
    # after all swaps are performed, send the values back to main
					result_pipe[1].send(a)





def    __UpperCamelCase      (    a) ->Optional[Any]:
					lowerCamelCase__       =       []
					lowerCamelCase__       =       []
					# initialize the list of pipes where the values will be retrieved
					for _ in arr:
										result_pipe.append(Pipe())
					# creates the processes
					# the first and last process only have one neighbor so they are made outside
					# of the loop
					lowerCamelCase__       =       Pipe()
					lowerCamelCase__       =       Pipe()
					process_array_.append(
					    Process(
					        target=a,     args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),     ))
					lowerCamelCase__       =       temp_rs
					lowerCamelCase__       =       temp_rr

					for i in range(1,     len(a) - 1):
										lowerCamelCase__       =       Pipe()
										lowerCamelCase__       =       Pipe()
										process_array_.append(
										    Process(
										        target=a,     args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),     ))
										lowerCamelCase__       =       temp_rs
										lowerCamelCase__       =       temp_rr

					process_array_.append(
					    Process(
					        target=a,     args=(
					            len(a) - 1,
					            arr[len(a) - 1],
					            temp_ls,
					            None,
					            temp_lr,
					            None,
					            result_pipe[len(a) - 1],
					        ),     ))

					# start the processes
					for p in process_array_:
										p.start()

					# wait for the processes to end and write their values to the list
					for p in range(0,     len(a)):
										lowerCamelCase__       =       result_pipe[p][0].recv()
										process_array_[p].join()
					return arr





def    __UpperCamelCase      (    ) ->List[Any]:
					lowerCamelCase__       =       list(range(10,     0,     -1))
					print("Initial List")
					print(*a)
					lowerCamelCase__       =       odd_even_transposition(a)
					print("Sorted List\n")
					print(*a)


if __name__ == "__main__":
						main()

