





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE					=    '''scheduler_config.json'''






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				Any											=       1
	a    :				Dict											=       2
	a    :				Optional[Any]											=       3
	a    :				List[str]											=       4
	a    :				Any											=       5






@dataclass
class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				jnp.ndarray






class 							__lowercase       :







	'''simple docstring'''

	a    :				str											=       SCHEDULER_CONFIG_NAME
	a    :				Union[str, Any]											=       ["dtype"]
	a    :				str											=       []
	a    :				List[Any]											=       True


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							Tuple:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							cls.load_config(
				    pretrained_model_name_or_path=_lowerCamelCase     ,subfolder=_lowerCamelCase     ,return_unused_kwargs=_lowerCamelCase     ,**_lowerCamelCase     ,)
				__lowercase						,		__lowercase										=							cls.from_config(_lowerCamelCase     ,return_unused_kwargs=_lowerCamelCase     ,**_lowerCamelCase )

				if hasattr(_lowerCamelCase     ,'''create_state''' ) and getattr(_lowerCamelCase     ,'''has_state'''     ,_lowerCamelCase ):
							__lowercase										=							scheduler.create_state()

				if return_unused_kwargs:
							return scheduler, state, unused_kwargs

				return scheduler, state


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = False     ,**_lowerCamelCase )							->							str:







				'''simple docstring'''





				self.save_config(save_directory=_lowerCamelCase     ,push_to_hub=_lowerCamelCase     ,**_lowerCamelCase )


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				return self._get_compatibles()


	@classmethod
	def 						_UpperCAmelCase     (cls )							->							int:







				'''simple docstring'''





				__lowercase										=							list(set([cls.__name__] + cls._compatibles ) )
				__lowercase										=							importlib.import_module(__name__.split('''.''' )[0] )
				__lowercase										=							[
				    getattr(_lowerCamelCase     ,_lowerCamelCase ) for c in compatible_classes_str if hasattr(_lowerCamelCase     ,_lowerCamelCase )
				]
				return compatible_classes





def    _lowerCAmelCase						(				lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  Tuple[int]				):
			assert len(lowerCamelCase_				) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_				) - x.ndim)				)		,       lowerCamelCase_				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  Union[str, Any]=0.9_99		,       lowerCamelCase_			:  Union[str, Any]=jnp.floataa				):

			def alpha_bar(lowerCamelCase_			:  Any				):
						return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

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






@flax.struct.dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				jnp.ndarray
	a    :				jnp.ndarray
	a    :				jnp.ndarray


	@classmethod
	def 						_UpperCAmelCase     (cls     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							scheduler.config

				if config.trained_betas is not None:
							__lowercase										=							jnp.asarray(config.trained_betas     ,dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
							__lowercase										=							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.
							__lowercase										=							(
							    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
							__lowercase										=							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__}" )

				__lowercase										=							1.0 - betas

				__lowercase										=							jnp.cumprod(_lowerCamelCase     ,axis=0 )

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





def    _lowerCAmelCase						(				lowerCamelCase_			:  CommonSchedulerState		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray				):
			__lowercase										=							state.alphas_cumprod

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

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

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def    _lowerCAmelCase						(				lowerCamelCase_			:  CommonSchedulerState		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray				):
			__lowercase						,		__lowercase										=							get_sqrt_alpha_prod(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples




def    _lowerCAmelCase						(				lowerCamelCase_			:  CommonSchedulerState		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray		,       lowerCamelCase_			:  jnp.ndarray				):
			__lowercase						,		__lowercase										=							get_sqrt_alpha_prod(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity












'''simple docstring'''



from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			__lowercase										=							(low + high) // 2
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       mid + 1		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_cross_sum(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum




def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1

			__lowercase										=							0
			for i in range(lowerCamelCase_		,       low - 1		,       -1				):
						summ += arr[i]
						if summ > left_sum:
									__lowercase										=							summ
									__lowercase										=							i

			__lowercase										=							0
			for i in range(mid + 1		,       high + 1				):
						summ += arr[i]
						if summ > right_sum:
									__lowercase										=							summ
									__lowercase										=							i

			return max_left, max_right, (left_sum + right_sum)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							[randint(1		,       lowerCamelCase_				) for _ in range(lowerCamelCase_				)]
			__lowercase										=							time.time()
			max_subarray(lowerCamelCase_		,       0		,       input_size - 1				)
			__lowercase										=							time.time()
			return end - start




def    _lowerCAmelCase						(				):
			__lowercase										=							[1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
			__lowercase										=							[time_max_subarray(lowerCamelCase_				) for input_size in input_sizes]
			print('''No of Inputs\t\tTime Taken'''				)
			for input_size, runtime in zip(lowerCamelCase_		,       lowerCamelCase_				):
						print(lowerCamelCase_		,       '''\t\t'''		,       lowerCamelCase_				)
			plt.plot(lowerCamelCase_		,       lowerCamelCase_				)
			plt.xlabel('''Number of Inputs'''				)
			plt.ylabel('''Time taken in seconds'''				)
			plt.show()


if __name__ == "__main__":
	from doctest import testmod

	testmod()





